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3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 5.2.1 Description | In a modern factory, a team on a workstation comprises two human operators, two mobile robots and a fixed robot. Everyone has his own pre-defined task. Robots assist the human operators by accomplishing painful tasks in a fluid and precise manner; they also monitor that the workstation environment remains safe for the human operators. The mobile robots must not interfere with humans and between them.
The robot control is not executed on a distant server in the cloud because reliability and confidentiality were not ensured at a sufficient level. Furthermore, as stated in [14], the overall end-to-end latency is not always guaranteed which can cause production loss. Communications between robots can rely on private wireless networks in the factory that enable expected QoS (reliability, throughput, and latency) as well as confidentiality.
The new robots are autonomous robots that can react to human voices or learn in real-time what operators do. They can perceive their environment and transmit information to other robots. They can communicate, learn from each other, assist each other and do self-monitoring.
The autonomous robot’s skills rely on several AI/ML models running on the robot itself which has the inconvenience that the mobile robot’s battery drain quicker. To overcome this issue, when the battery level reaches a certain value, a part of the AI/ML model can be transferred to a service hosting environment and / or to another robot by splitting the AI/ML model as defined in [20]. The split model approach of [20] is applicable to a UE-to-UE (or robot-to-robot) architecture. Thus, the AI/ML model M is split and shared between (e.g.) 2 robots, say an assisted robot and an assistant robot. Intermediate data generated by the assisted robot are transferred to the assisting robot which finalizes the inference and transmits the results back to the assisted robot. This intermediate data transfer must be extremely efficient in terms of latency and throughput. When many models are at stake, the split model method is an additional challenge for the 5GS in terms of throughput, latency and synchronization.
Because they are more autonomous, mobile and smart, the industry 4.0 robots embed a large variety of sensors that generate huge amount of data to process. Table 5.2.1-1 reflects that variety.
Each type of sensing data requires a different AI/ML model. Each of these models produce predictions in a certain delay and with a certain accuracy.
Thus, as an offloading strategy, we can imagine that a model is split between 2 robots because it has been established that for a particular AI/ML model, the latency with the sidelink communication was better (smaller) than with the regular 5G communication path, as stated in [12] and [13].
At the same time, other AI/ML models are split between the robot and the service hosting environment because from an energy standpoint this configuration is the best.
Table 5.2.1-2 is an example of this offloading strategy where four AI/ML models are split between a robot and the service hosting environment and four other AI/ML models are split between two robots.
Sidelink and 5G communication paths are complementary from an AI/ML model split policy standpoint.
Table 5.2.1-1 shows some typical and diverse AI/ML models that can be used on robots. For each model, all the split point candidates have been considered and only the split points that generate the minimum and the maximum amount of intermediate data have been noted.
Table 5.2.1-1: Intermediate AI/ML data size per AI/ML model
Model Name
Model type
Intermediate data size (MB)
8 bits data format
32 bits data format
Min
Max
Min
Max
AlexNet [21]
Image recognition
0.02
0.06
0.08
0.27
ResNet50 [22]
Image recognition
0.002
1.6
0.008
6.4
SoundNet [11]
Sound recognition
0.0017
0.22
0.0068
0.88
PointNet [15]
Point Cloud
0.262
1.04
0.0068
4.19
VGGFace [19]
Face recognition
0.000016
0.8
0.000064
3.2
Inception resnet
Face recognition
0.0017
0.37
0.0068
1.51
In Table 5.2.1-2 the AI/ML models are distributed between the service hosting environment and the proximity robot. The way the models are distributed is out of scope of this use case and depends on various criteria as said previously. Therefore, the next table is an example that illustrates the distribution. The intermediate data size is presented with the range [Min – Max], where Min and Max are respectively the sum of the Min values and the sum of the Max values of the selected models as defined in Table 5.2.1-1 (figures in bold).
Table 5.2.1-2: Example of models distribution and data rate for intermediate AI/ML data
Model Name
Offloading target
Intermediate data size (MB)
Transfer time (ms)
Data rate (Gb/s)
AlexNet [21]
Proximity robot or Service Hosting Environment
[0.000016 – 1.6]
(8 bits data format)
10
[0.128 - 1.28]
ResNet50 [22]
VGGFace [19]
SoundNet [11]
[0.000064 – 6.4]
(32 bits data format)
10
[0.512 – 5.12]
PointNet [15]
Inception resnet
As previously said latency is a critical requirement. Figure 5.2.1-3 summarizes the latency cost in three scenarios:
(A) The inference of model M is done locally. Latency is denoted LLI.
(B) The inference process is fully offloaded on a second device (Robot/UE). Latency is denoted LFO.
(C) The inference process is partially offloaded on a second device (Robot/UE). Latency is denoted LPO.
Figure 5.2.1‑3 Latency summary
The current Use Case promotes the scenario (C) where a model M is split in two sub-models Ma and Mb. If both robots (UEs) have a similar computing power, the assumption is that the latency due to the inference of model M is almost equal to the latency of model Ma plus the latency of model Mb.
Hence, once the split model is deployed on the two robots (UEs), the aim is to minimize the E2E latency and to be as close as possible to the non-split case. This is done with a transfer delay of both the intermediate data and the inference results as small as possible. We can note that if the computing power on the assistant robot is more important, then scenario (C) would be the preferred scenario.
In scenario (B), the inference process is fully offloaded on the assistant robot (UE). The major inconvenience is the strong and negative impact on latency of the raw data transfer towards the assistant robot.
5.2.2 Pre-conditions
Two human operators are working.
Two mobile AI-driven robots (Arobot and Brobot) and one static AI-driven robot (Crobot) assist them.
The three robots (Arobot, Brobot and Crobot) belong to the same service area, embed the same two powerful AI/ML models M1 and M2, sensors (e.g. LIDAR, microphone) and cameras (e.g., 8K videos stream).
Arobot and Brobot are powered with a battery and Crobot with fixed ground power.
The three robots (Arobot, Brobot and Crobot) are connected, e.g., to the AF, 5GC, or to each other using D2D technologies (Prose, BT, WiFi, etc.).
The workstation is equipped with camera and sensors.
The service area is 30 m x 30 m and the robot speed is at maximum 10 km/h.
The service area is covered by a small cell and a service hosting environment is connected and can support AI/ML processes.
5.2.3 Service Flows
Figure 5.2.3‑1 Factory service flow
a) Brobot battery level is rather low but it can still work for a while if a part of its machine learning process is offloaded.
b) Brobot broadcasts a request message to get assistance. Crobot and the service hosting environment responds positively.
c) Brobot negotiates with Crobot and the service hosting environment what parts of M1 and M2 respectively for the inference process they are in charge of, knowing that the quality of the prediction must not be under a certain level and that the end-to-end latency must not be above a certain value. M1 model is split between Brobot and the service hosting environment. M2 model is split between Brobot and Crobot.
d) Brobot, Crobot and the service hosting environment agree on split points for both M1 and M2 models and Brobot starts sending the intermediate data to Crobot and the service hosting environment.
e) Crobot infers and transmits using unicast mode with a very short delay the predictions back to Brobot. The service hosting environment infers and transmits using unicast mode the predictions back to Brobot in a very short delay.
f) In the meanwhile, Arobot is carrying a load to the operator Aoperator.
g) Aoperator bends down to pick up a screw that has fallen on the floor. At the same time Boperator is passing between Aoperator and Arobot. Arobot can’t see Aoperator anymore.
h) Brobot is busy with another task, but it can observe the scene. It reports the scene as intermediate data to Crobot and the service hosting environment.
i) Crobot and the service hosting environment amend the ML model based on the new training data.
j) Crobot and the service hosting environment infer and then transmit in unicast the prediction back to Brobot. The safety application on the service hosting environment collects the inference results.
5.2.4 Post-conditions
Intermediate data can be exchanged between two robots (UEs) and / or service hosting environment, and the robot with a low battery level can continue working for a while.
All the robots in the group receive the alert message and react:
a) They all stop working; or
b) Arobot changes its trajectory.
Aoperator and Boperator can work safely.
The huge amount of data that is required for inferring is kept local in the factory.
5.2.5 Existing features partly or fully covering the use case functionality
The Use Case can rely on the Proximity Service (ProSe) services as defined in 3GPP TS 23.303 [17].
Cyber-Physical Control Applications, see 3GPP TS 22.104 [18], already proposes to rely on a ProSe communication path. The proposed requirements are limited in terms of data transfer as shown in Table 5.2-1, where the message size does not exceed a few hundred of Bytes (250 kB at maximum).
3GPP TS 22.261 [16] clause 6.40 provides requirements for AI/ML model transfer in 5GS. The requirements in this clause does not consider requirements for direct device connection.
In 3GPP TS 22.261 [16] Table 7.6.1-1, the max. end-to-end latency is 10 ms, the maximum data rate is [1] Gbits/s and reliability is 99.99% for Gaming or Interactive Data Exchanging.
Table 7.6.1-1 KPI Table for additional high data rate and low latency service
Use Cases
Characteristic parameter (KPI)
Influence quantity
Max allowed end-to-end latency
Service bit rate: user-experienced data rate
Reliability
# of UEs
UE Speed
Service Area
(note 2)
Gaming or Interactive Data Exchanging
(note 3)
10ms (note 4)
0,1 to [1] Gbit/s supporting visual content (e.g. VR based or high definition video) with 4K, 8K resolution and up to120 frames per second content.
99,99 % (note 4)
≤ [10]
Stationary or Pedestrian
20 m x 10 m; in one vehicle (up to 120 km/h) and in one train (up to 500 km/h)
NOTE 1: Unless otherwise specified, all communication via wireless link is between UEs and network node (UE to network node and/or network node to UE) rather than direct wireless links (UE to UE).
NOTE 2: Length x width (x height).
NOTE 3: Communication includes direct wireless links (UE to UE).
NOTE 4: Latency and reliability KPIs can vary based on specific use case/architecture, e.g. for cloud/edge/split rendering, and can be represented by a range of values.
NOTE 5: The decoding capability in the VR headset and the encoding/decoding complexity/time of the stream will set the required bit rate and latency over the direct wireless link between the tethered VR headset and its connected UE, bit rate from 100 Mbit/s to [10] Gbit/s and latency from 5 ms to 10 ms.
NOTE 6: The performance requirement is valid for the direct wireless link between the tethered VR headset and its connected UE.
These requirements partially cover the current Use Case needs.
5.2.6 Potential New Requirements needed to support the use case |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 5.2.6.1 Potential Functionality Requirements | [P.R.5.2.6-001] Subject to user consent and operator policy, the 5G system shall support the transfer of AI/ML model intermediate data from UE to UE via the direct device connection.
[P.R.5.2.6-002] Subject to user consent and operator policy, the 5G system shall be able to provide means to predict and expose network condition changes (i.e. bitrate, latency, reliability) and receive user preferences on usage of the direct device connection or the direct network connection in order to meet the user experienced data rate and latency.
[P.R.5.2.6-003] Subject to user consent and operator policy, the 5G system shall be able to dynamically select the intermediate device that is capable to perform the needed functionalities, e.g., AIML splitting.
[P.R.5.2.6-004] Subject to user consent and operator policy, the 5G system shall be able to maintain the QoS (latency, reliability, data rate as defined in the Table 5.2.6.2-1 below) of the communication path of the direct device connection.
[P.R.5.2.6-005] Subject to user consent and operator policy, the 5G system shall be able to have the means to modify the QoS of the communication path of the direct device connection.
NOTE: The split point selection is dynamic. In consequence, the amount of intermediate data will vary. To maintain the QoS, the bandwidth is adjusted. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 5.2.6.2 Potential KPI Requirements | Based on Table 5.2.1-2, the potential KPI requirement is as below:
Table 5.2.6.2-1 KPI for intermediate AI/ML data transmission for model split based robot control
Model Name
Payload size (Intermediate data size)
Max allowed end-to-end latency
Experienced data rate
Service area dimension
Communication service availability
Reliability
AlexNet [21]
0.000016 – 1.6 MByte
(8 bits data format)
10 ms
0.128 - 1.28 Gbps
900 m2
(30 m x 30 m)
99.999 %
99.999 %
ResNet50 [22]
VGGFace [19]
SoundNet [11]
PointNet [15]
0.000064 – 6.4 Mbyte
(32 bits data format)
10 ms
0.512 - 5.12 Gbps
Inception resnet |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6 AI/ML model/data distribution and sharing by leveraging direct device connection | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.1 AI Model Transfer Management through Direct Device Connection | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.1.1 Description | Based on the earlier study in phase one 3GPP TR 22.874 [2], operators can provide services to help manage and distribute the AI/ML models especially in the edge server so that the UE can acquire a proper model immediately. However, when a lot of UEs requesting for the same model at the same time or the UE is blocked by barriers with poor connection with the base station, the model transfer process will become longer than expected.
To overcome this difficulty, as shown in Fig.1, a volunteer UE which is well connected to the base station can help relaying AI/ML models or receive and store AI/ML models first. Then, the other UEs can download AI/ML models from the volunteer UE through direct device connection. In this way, all UE can have a stable and reliable model transfer process while the radio resource of the base station can be saved. Besides, the volunteer UE can transfer the stored models to other volunteer UEs under operator’s control.
The selection of volunteer UE can be realized by local network policies and strategies. And it also can be exposed as a capability to the 3rd party company when the company wants to choose one or a few certain UEs to be volunteer UEs in an activity. For example, a travel company may assign the tour guides’ Augmented Reality (AR) headsets as volunteer UEs in a carnival through the operator’s network exposure. The travel company may sign a higher quality plan for tourist guides’ devices to provide better user experience for following tourists. Meanwhile, operator can benefit from the alternative open service based on AI/ML model management capabilities and may avoid low Quality of Service due to crowding direct connections to base stations during the carnival.
Fig.1 AI/ML Model management through direct device connection |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.1.2 Pre-conditions | The operator’s MEC near the Jurassic Park stores a variety of AI/ML models according to the the park company’s requirements. And it is capable to transfer the stored model to the device such as AR headset.
The operator rolls out a new high-quality plan which can allow the user customizes own Service Level Agreement (SLA) for specific network address access and data (e.g. AI/ML Models) download. As a trade-off, the user’s device will help transfer the same data through direct device connection to nearby devices sharing common aspiration.
The AR headset can transfer the stored AI/ML model to the other AR headsets. However, the AR headset cannot store all models for different scenarios due to limited storage. Indeed, a model needs to be downloaded when or a few seconds before the UE first appears in the certain area.
Alice and Bob are tour guide hired by Jurassic Park and their real-time positions can be acquired when they are in the park based on signed agreements.
All of AR headsets should in the coverage of the base stations serving for the Jurassic Park. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.1.3 Service Flows | 1. Jurassic Park provides panorama AR tour guide services in a commercial area and a tropical rainforest area. AR headsets need to download Model A and B (both are VGG-16, 552MByte) respectively.
2. To provide high quality user experience, Jurassic Park company indicates to the operator that AR headsets require to download model A in area A and model B in area B.
3. The Jurassic Park company signs a high-quality plan for tour guide Alice and Bob’s AR Headsets for providing better service to the tour group using direct device connection.
4. When Bob and his tour group enter area A, their headsets request for the Model A. The operator network finds they requested the same model and Bob is a signed volunteer UE, then triggers to establish a QoS acceleration for Bob’s model downloading timely within 1 second. Meanwhile, Jurassic Park requests the operator network to inform Bob to help transfer the model to all other UE near Bob. Also, the operator network informs all other UE near Bob that Bob can provide the model as well. The UE which is a little far from Bob (e.g. out of Bob’s coverage) will still download the model through the base station directly.
5. Alice and her group are 10 meters far from Bob and also move towards to area A. Jurassic Park predicts their desire model based on their movement and finds Bob has already downloaded it based on the model transferring records. Jurassic Park requests operator network to inform Alice that she can request model from Bob. Meanwhile, the operator network indicates all other UE near Alice to download the model from Alice.
6. For Alice and Bob, they can see the status of all direct device connections to themselves through network exposures providing by operators (e.g. monitored bandwidth and latency of each direct device connection)
7. When Alice and Bob notice that their groups have a poor QoS of model transfer through direct device connection, they can send a request to the park company for promoting the performance of their direct device connections and the park company will send a similar message to the operator through network exposure to active a temporary acceleration of these direct device connections (e.g. expand the bandwidth of each direct device connection). |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.1.4 Post-conditions | 1. The tourists can enjoy the continuous AR services with smooth model switchover when their location and responding models change.
2. Tour group’s AR headsets provides user experience of the panorama AR tour guide services that can help retrain and improve AI/ML models in operator’s MEC by Jurassic Park company (e.g. Federated/Distributed Learning).
3. the operator network performs analytics, based on network statistics and quality of experience reported by Jurassic Park company, to improve and optimized the model transfer process (e.g. setting constraints for maximum direct device connection for one volunteer UE and choose a temporary volunteer UE for sharing model transfer task). |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.1.5 Existing features partly or fully covering the use case functionality | In 3GPP TS 22.261 [8] clause 6.27.2 "Requirements"
The 5G system shall be able to make the position-related data available to an application or to an application server existing within the PLMN, external to the PLMN, or in the User Equipment.
In 3GPP TS 22.261 [8] clause 6.9.2.4 "Relay UE Selection"
The 3GPP system shall support selection and reselection of relay UEs based on a combination of different criteria e.g.
- the characteristics of the traffic that is intended to be relayed (e.g. expected message frequency and required QoS),
- the subscriptions of relay UEs and remote UE,
- the capabilities/capacity/coverage when using the relay UE,
- the QoS that is achievable by selecting the relay UE,
- the power consumption required by relay UE and remote UE,
- the pre-paired relay UE,
- the 3GPP or non-3GPP access the relay UE uses to connect to the network,
- the 3GPP network the relay UE connects to (either directly or indirectly),
- the overall optimization of the power consumption/performance of the 3GPP system, or
- battery capabilities and battery lifetime of the relay UE and the remote UE.
NOTE: Reselection may be triggered by any dynamic change in the selection criteria, e.g. by the battery of a relay UE getting depleted, a new relay capable UE getting in range, a remote UEs requesting additional resources or higher QoS, etc.
In 3GPP TS 22.261 [8] v18.6.0 clause 6.40.2
Based on operator policy, the 5G system shall be able to provide an indication about a planned change of bitrate, latency, or reliability for a QoS flow to an authorized 3rd party so that the 3rd party AI/ML application is able to adjust the application layer behaviour if time allows. The indication shall provide the anticipated time and location of the change, as well as the target QoS parameters.
Subject to user consent, operator policy and regulatory constraints, the 5G system shall be able to support a mechanism to expose monitoring and status information of an AI-ML session to a 3rd party AI/ML application.
NOTE: Such mechanism is needed for AI/ML application to determine an in-time transfer of AI/ML model.
Subject to user consent, operator policy and regulatory requirements, the 5G system shall be able to expose information (e.g. candidate UEs) to an authorized 3rd party to assist the 3rd party to determine member(s) of a group of UEs (e.g. UEs of a FL group). |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.1.6 Potential New Requirements needed to support the use case | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.1.6.1 Potential Functionality Requirements | [P.R.6.1-001] Subject to user consent, operator policies and regional or national regulatory requirements, the 5G system shall be able to support means to monitor a direct device connection and expose corresponding monitoring information (e.g. experienced data rate, latency) to an authorized 3rd party.
NOTE: The monitoring information in [P.R.6.1-001] doesn’t include any user position-related data.
[P.R.6.1-002] Subject to user consent and operator policies, the 5G system shall be able to provide means for an authorized third-party to authorize a group of UEs to exchanging data with each other via direct device connection.
[P.R.6.1-003] The 5G system shall support a mechanism for an authorized third-party to negotiate a suitable QoS of direct device connections for a group of UEs to exchange data with each other.
[P.R.6.1-004] Subject to user consent, operator policies and regulatory requirements, the 5G system shall support means to monitor, characteristics of traffic relayed by a UE participating in the communication and expose to 3rd party. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.1.6.2 Potential KPI Requirements | [P.R.6.1-005] The 5G system shall support to use direct device communication to transmit the AI/ML model of image recognition and 3D object recognition with the following KPIs.
Table 6.1.6.2-1: KPIs for image recognition and 3D object recognition using direct device connection
Model Type
Max allowed DL end-to-end latency
Experienced data rate
in PC5
Model size
Communication service availability
AlexNet
1s
1.92 Gbit/s
240 MByte
99.9 %
ResNet-152
1s
1.92 Gbit/s
240 Mbyte
99.9 %
ResNet-50
1s
0.8 Gbit/s
100 Mbyte
99.9 %
GoogleNet
1s
0.218 Gbit/s
27.2 Mbyte
99.9 %
Inception-V3
1s
0.736 Gbit/s
92 Mbyte
99.9 %
PV-RCNN
1s
0.4 Gbit/s
50 Mbyte
99.9 %
PointPillar
1s
0.14 Gbit/s
18 Mbyte
99.9 %
SECOND
1s
0.16 Gbit/s
20 Mbyte
99.9 %
For the size of image recognition model, it refers to table 6.1.1-1 in TR22.874 [2], for the size of 3D object recognition model, see [24].
Reliability is assumed to be [99.9 – 99.999]% |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.2 5GS assisted transfer learning for trajectory prediction | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.2.1 Description | AIML model transfer learning is beneficial for lowing cost and raising effective when training a model using a target UE based on a pre-training model. The principle of transfer learning is to use the knowledge from the source domain to train a model in the target domain to achieve more expedient and higher accuracy efficiency [25].
Figure 6.2-1 AI/ML model transfer learning from source UE to target UE [26]
Since the AI model is a kind of knowledge, when the centralized application server acquires enough number of AIML model used by UEs, it may perform a backward inference/inversion attacks [27] to derive the feature of UE’s local data set, which means a privacy risk exists. In order to resolve the privacy concern for transfer learning, the model transfer via direct device connection is a better to be used so that the network node (e.g. application server) cannot acquire the AIML model used by UE and no way to do backward inference. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.2.2 Pre-conditions | Alice is a customer of intelligent-driving service provided by company-A. She lives in Chaoyang district in Beijing and driving to her office building in CBD every working day. By using the intelligent driving service, Alice’s car can predict the trajectory of neighbouring vehicles (as figure 6.2.2 shows), so as to pre-alert Alice of some potential collision and Alice can decide whether to steer, accelerate, or any other driving operation.
Figure 6.2-2: Qualitative results using model of trajectory prediction: the orange trajectory represents the observed 2s. Red represents ground truth for the next 3 seconds and green represents the multiple forecasted trajectories for those 3s [24].
An AIML model can be for the object recognition and prediction, the model is offered by company-A and customers of company-A have signed “smart driving project” (an agreement for AIML model sharing and improvement). |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.2.3 Service Flows | 1. Bob bought a car equipped with intelligent driving functionality and he would like to use auto-driving for his daily driving, so he applies to company-A to offer the intelligent-driving service.
2. Company-A needs to install certain AIML model to Bob’s car while use Bob’s local data to train the model. The company-A identified Alice’s model to be shared to Bob’s car.
In order to minimize privacy issue, the “smart driving project” signed by customer only allows the model to be transferred among users directly instead of letting application server to acquire and forward it.
3. Company-A requests 5G system to transmit the AIML model for intelligent driving from Alice’s car to Bob’s car via direct device connection at a proper time (e.g. when the direct device connection can be established)
4. When acquiring the AI model from Alice’s car, Bob’s car performs “fine-tuning” operation of transfer learning based on the local data to tune the model to be better used for its own intelligent-driving service. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.2.4 Post-conditions | Thanks to 5GS assisted AIML model transfer via direct device connection, Bob’s car efficiently gets an ideal AIML model for intelligent-driving by means of transfer learning. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.2.5 Existing features partly or fully covering the use case functionality | In 3GPP TS 22.261 [8] v18.6.1 clause 6.9
The 5G system shall support different traffic flows of a remote UE to be relayed via different indirect network connection paths.
The connection between a remote UE and a relay UE shall be able to use 3GPP RAT or non-3GPP RAT and use licensed or unlicensed band.
The connection between a remote UE and a relay UE shall be able to use fixed broadband technology.
The 5G system shall be able to provide indication to a remote UE (alternatively, an authorized user) on the quality of currently available indirect network connection paths.
The 5G system shall be able to maintain service continuity of indirect network connection for a remote UE when the communication path to the network changes (i.e. change of one or more of the relay UEs, change of the gNB).
The 5G system shall be able to support a UE using simultaneous indirect and direct network connection mode.
The 5G system shall enable the network operator to authorize a UE to use indirect network connection. The authorization shall be able to be restricted to using only relay UEs belonging to the same network operator. The authorization shall be able to be restricted to only relay UEs belonging to the same application layer group.
In 3GPP TS 22.261 [8] v18.6.1 6.40
Subject to user consent, operator policy and regulatory requirements, the 5G system shall be able to expose information (e.g. candidate UEs) to an authorized 3rd party to assist the 3rd party to determine member(s) of a group of UEs (e.g. UEs of a FL group). |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.2.6 Potential New Requirements needed to support the use case | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.2.6.1 Potential Functionality Requirements | [P.R.6.2-001] Based on user consent and 3rd party request, operator policy, the 5G system shall support a means to authorize specific UEs to transmit data (e.g. AI-ML model tansfer for a specific application) via direct device connection in a certain location and time.
[P.R.6.2-002] Subject to user consent and operator policy, the 5G system shall be able to expose information to an authorized 3rd party to assist the 3rd party to determine candidate UEs for data transmission via direct device connection (e.g. for AIML model transfer). |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 6.2.6.2 Potential KPI Requirements | [P.R.6.2-003] The 5G system shall be able to support transmitting an AI/ML model via direct device connection fulfilling the KPIs for transmission of typical AIML model for trajectory prediction and object recognition [24][28] in Table 6.2-1.
Table 6.2-1
Payload size
Latency for model transmission (NOTE 1)
Transmission Data rate
LaneGCN
15 MByte
3 seconds
5 MByte/s
ResNet-50
25 MByte
3 seconds
8.33 MByte/s
ResNet-152
60 MByte
3 seconds
20 Mbyte/s
PointPillar
18 MByte
3 seconds
6 MByte/s
SECOND
20 MByte
3 seconds
6.67 MByte/s
Part-A2-Free
226 MByte
3 seconds
75.33 MByte/s
Part-A2-Anchor
244 MByte
3 seconds
81.33 MByte/s
PV-RCNN
50 MByte
3 seconds
16.67 MByte/s
Voxel R-CNN (Car)
28 MByte
3 seconds
9.33 MByte/s
CaDDN (Mono)
774 MByte
3 seconds
248 MByte/s
NOTE 1: The transfer learning does not have a very high requirement for transmission latency since it is not a real-time inference service, hence it assumes the model transmission via direct device connection should be finished in 3 seconds. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7 Distributed/Federated Learning by leveraging direct device connection | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.1 Direct device connection assisted Federated Learning | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.1.1 Description | In many circumstances, an application server holding a Federated Learning (FL) task has a transmission delay requirement and limited FL coverage. An FL coverage means an area in which UEs the Application server can organize for federated learning.
An Application server has a transmission delay requirement for each FL member (UE). Some of UEs are actually holding valuable dataset but cannot fulfil transmission delay requirement, which leads to a decreasing of FL performance. However, if a UE’s direct network connection cannot fulfil the transmission delay requirement (i.e. an QoS on Uu), leveraging the devices with direct connections helps to involve more UEs holding valuable dataset for the FL task with the following case study:
A UE-A with bad transmission condition sends a UE’s training result to UE-B via direct device connection. In such case, a UE-B aggregates the training result locally and provides to UEs an update of training model for next round.
Some research e.g. in [6][7] have illustrated the increasing performance in subcase-B (we call it “decentralized averaging methods”). In order to include more devices to participate in FL and to reduce the devices’ reliance on the PS, the authors in [7] uses decentralized averaging methods to update the local ML model of each device. In particularly, using the decentralized averaging methods, each device only needs to transmit its local ML parameters to its neighboring devices. And the neighboring devices can use the acquired ML parameters to estimate the global ML model. Therefore, using the decentralized averaging methods can reduce the communication overhead of FL parameter transmission.
Figure 7.1-1 FL with decentralized averaging method outperforms the original FL
To show the performance of decentralized averaging method, the [6] implemented a preliminary simulation for a network that consists of one BS that is acted as an application server and six devices, as shown in Figure-1. In Figure-1, the green and purple lines respectively represent the local ML parameter transmission of original FL and the FL with decentralized averaging methods. Due to the transmission latency requirement, only 4 devices can participate in original FL. For the FL with the decentralized averaging update method, 6 devices can participate in the FL training process since the devices which are out of coverage can connect to their neighbouring devices (i.e. Device a and Device b) for model updating.
From Figure-1, we can see that the FL with decentralized averaging method outperforms the original FL in terms of identification accuracy. Specifically, the original FL (without using direct device connection) has an upper limit of identification accuracy to about 0.85, while using direct device connection for decentralized averaging method helps to increase the identification accuracy to about 0.88 which is actually a big optimization since the line already goes smoothly after 200 round of FL training.
Besides, the FL leveraging direct device connection can also reduce the energy consumption for some devices since it only needs to transmit its ML model parameters to device instead of the BS. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.1.2 Pre-conditions | Figure 7.1-2 two UEs performs decentralized FL using Direct Device connection
As depicted in Figure-2, there is an Application server for federated learning which needs to communicate with the UEs in a FL coverage for FL task.
To achieve an ideal performance (i.e. fast convergence and high model accuracy), there is a transmission latency requirement to each FL member UE’s data transmission.
Alice and Bob are FL members but their cell phones sometimes have bad signal condition which cannot transmit data to FL service directly. Meanwhile, Bob is willing to support the “decentralized averaging method” (as described in clause 7.1.1) service for its neighbouring cell phones.
Alice, Bob are neighbouring to each other within a FL coverage. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.1.3 Service Flows | 1. Alice is a FL member and already acquires the global AI/ML model from the Application server for FL task, later on when Alice moves to a tunnel with bad signal condition, Alice cell phone’s with direct device connection with her neighboring cell phone cannot transmit model data to its Application server anymore.
2. In the tunnel, Alice discovers Bob, who is neighboring to Alice, a FL member and willing to activate the “decentralized averaging method” service. Thus, Alice requests Bob to establish a direct device connection so that Alice can transmit the AI/ML model training result to Bob.
3. Bob updates the AI/ML model based on Alice’s training result and Bob’s local training result. And Bob sends the updated AI/ML model to Alice for further training.
When Bob moves to a good coverage and is able to transmit the AIML training model (e.g. after several rounds of AIML model parameters exchange between Alice and Bob have been done), Bob transmits the training result to Application server to assist the Application server to perform a global model updating. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.1.4 Post-conditions | By leveraging direct device connection, Alice and Bob can keep the model training of a FL task even when they are under a bad network coverage. And the training result between Alice and Bob can be further uploaded to Application server for global model updating.
Thanks to leveraging direct device connection, it helps FL to be performed even when no communication availability to FL server. Such use case helps to optimize the FL performance. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.1.5 Existing features partly or fully covering the use case functionality | In 3GPP TS 22.261 [8] v18.6.1 clause 6.40.2
Based on operator policy, the 5G system shall be able to provide means to allow an authorized third-party to monitor the resource utilisation of the network service that is associated with the third-party.
NOTE 1: Resource utilization in the preceding requirement refers to measurements relevant to the UE’s performance such as the data throughput provided to the UE.
Based on operator policy, the 5G system shall be able to provide an indication about a planned change of bitrate, latency, or reliability for a QoS flow to an authorized 3rd party so that the 3rd party AI/ML application is able to adjust the application layer behaviour if time allows. The indication shall provide the anticipated time and location of the change, as well as the target QoS parameters.
Based on operator policy, 5G system shall be able to provide means to predict and expose predicted network condition changes (i.e. bitrate, latency, reliability) per UE, to an authorized third party.
Subject to user consent, operator policy and regulatory constraints, the 5G system shall be able to support a mechanism to expose monitoring and status information of an AI-ML session to a 3rd party AI/ML application.
NOTE 2: Such mechanism is needed for AI/ML application to determine an in-time transfer of AI/ML model.
Subject to user consent, operator policy and regulatory requirements, the 5G system shall be able to expose information (e.g. candidate UEs) to an authorized 3rd party to assist the 3rd party to determine member(s) of a group of UEs (e.g. UEs of a FL group). |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.1.6 Potential New Requirements needed to support the use case | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.1.6.1 Functional requirement | [P.R.7.1-001] Based on user consent and operator policies, the 5G system shall be able to configure a group of UEs who participate in the same service group (e.g. for the same AI-ML FL task) to establish communication with each other via direct device connection e.g. when direct network connection cannot fulfil the required QoS.
[P.R.7.1-002] Based on user consent, operator policies and the request from an authorized 3rd party, the 5G system shall be able to dynamically add or remove UEs to/from the same service (e.g. a AI-ML federated learning task) when communicating via direct device connection. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.1.6.2 KPI requirement for direct device communication | The 5G system shall be able to support the following KPI for direct device connection as defined in Table 7.1.6-1
NOTE: The table refers to a typical AI/ML model for image recognition i.e. a 7-bit CNN model VGG16_BN using 2242243 images as training data) [2].
Table 7.1.6-1: Latency and user experienced UL/DL data rates for uncompressed Federated Learning
Model size
(8 bit VGG-16 BN) (see NOTE 2)
Mini-batch size
(images)
Maximum latency for trained gradient uploading and global model distribution (see NOTE 1)
User experienced UL/DL data rate for trained gradient uploading and global model distribution (see NOTE 2)
132 Mbyte
64
3.24s
325Mbit/s
32
1.9s
55Mbit/s
16
1.3s
810Mbit/s
8
1.1s
960Mbit/s
4
1.04s
1.0Gbit/s
NOTE 1: Latency in this table is assumed 20 times the device GPU computation time for the given mini-batch size.
NOTE 2: Values provided in the table are calculative needs for an 8-bit VGG16 BN model with 132MByte size [2] |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.2 Asynchronous FL via direct device connection | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.2.1 Description | Federated Learning (FL) is an important machine learning service. Due to the Synchronous FL [8], Sync-FL, requires a strict communication quality for each UE in order to get all the intermediate results to FL server in time, the Synchronous FL is sometimes vulnerable to the unpredicted wireless condition and the divergence of UEs’ capabilities. Therefore, the Asynchronous FL [9], Async-FL, has been widely used in many circumstances. The main idea of Async-FL is to let UE report its result whenever it is ready and the FL server will refresh the model without waiting for all intermediates results are collected. The Sync-FL and Async-FL have pros and cons as the table 7.2-1 shows.
Table 7.2-1 Comparison of Sync-FL and Async-FL
Sync-FL
Async-FL
Total computation workload
Lower.
Higher. The UE will get a new model for training when it uploads the result without waiting for other UE’s result. So the computation work load in each UE can be increased.
Communication requirement
Higher. All UEs shall report its result before next FL round starts
Lower. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.2.2 Pre-conditions | The direct device connection can be used to realize the Async-FL. As The figure 7.2-1 shows, for some UEs who are in a bad coverage it can use the indirect network connection to communicate with Parameter Server (PS). The communication requirement via indirect network connection can be relaxed i.e. no need to transmitted all UEs training result with a restricted timing.
Figure 7.2-1 Group based Async-FL
For each member UE, it can send its training result to the PS via either direct network connection or indirect network connection, and the PS will send a new model to the member UE without waiting for other Aggregate UEs’ result (i.e. Async-FL). |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.2.3 Service Flows | 1) The Parameter Server (PS) distributes the global model to FL member UEs via direct network connection or indirect network connection; For the UEs in bad coverage, it can use the indirect network connection to perform a Async-FL with PS.
2) When receiving the training result from member UE, the relay UE sends it to the Parameter server immediately to get a new model for the member UE. Due to the relay UE has a limited QoS for its own network connection (PDU session), the relay UE needs to determine the QoS for indirect network connection for each of member UEs based on an aggregated QoS (QoS upper limit) for the group of members served by the relay UE.
3) The Async-FL will be performed until the model accuracy reached a certain threshold. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.2.4 Post-conditions | Thanks to the indirect network connection, the FL server can still use the valuable data stored in UEs who are out of coverage with the method of Async-FL. The model training is finally finished with expected model performance.
The charging for an Remote UE using an Indirect 3GPP Communication will be done.
7.2.5 Existing features partly or fully covering the use case functionality
In TS 22.261 (v19.2.0) clause 3.1
aggregated QoS: QoS requirement(s) that apply to the traffic of a group of UEs.
In TS 22.261 (v19.2.0) clause 6.9
The 5G system shall support different traffic flows of a remote UE to be relayed via different indirect network connection paths.
The connection between a remote UE and a relay UE shall be able to use 3GPP RAT or non-3GPP RAT and use licensed or unlicensed band.
The connection between a remote UE and a relay UE shall be able to use fixed broadband technology.
The 5G system shall be able to provide indication to a remote UE (alternatively, an authorized user) on the quality of currently available indirect network connection paths.
The 5G system shall be able to maintain service continuity of indirect network connection for a remote UE when the communication path to the network changes (i.e. change of one or more of the relay UEs, change of the gNB).
The 5G system shall be able to support a UE using simultaneous indirect and direct network connection mode.
The 5G system shall enable the network operator to authorize a UE to use indirect network connection. The authorization shall be able to be restricted to using only relay UEs belonging to the same network operator. The authorization shall be able to be restricted to only relay UEs belonging to the same application layer group.
In TS 22.261 (v19.2.0) clause 6.40,
Subject to user consent, operator policy and regulatory requirements, the 5G system shall be able to expose information (e.g. candidate UEs) to an authorized 3rd party to assist the 3rd party to determine member(s) of a group of UEs (e.g. UEs of a FL group).
In TS 22.115 (V18.0.0) Clause 4.8 on "Charging Requirements for Indirect 3GPP Communication"
This section describes the requirements to enable operator collection of charging data for an Evolved ProSe Remote UE and Relay UE using an Indirect 3GPP Communication. The requirements also apply in the roaming case.
The 3GPP core network shall be able to collect charging data for an Evolved ProSe Remote UE which accesses the 3GPP core network through an Indirect 3GPP Communication. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.2.6 Potential New Requirements needed to support the use case | [P.R. 7.2-001] 5GS shall be able to support an aggregated QoS for a group UEs served by a relay UE.
[P.R. 7.2-002] 5GS shall be able to provision an aggregated QoS to a relay UE for a group-based service.
[P.E. 7.2-003] Based on 3rd party request and user consent, the 5G system shall be able to expose information (e.g. candidate UEs) to an authorized 3rd party to assist the 3rd party for UE member(s) selection in a group of UEs (e.g. UEs of a FL group), for UEs using direct or indirect network connection. E.g. the 3rd party request may include the expected QoS as a criteria for UE member selection. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.3 5GS assisted distributed joint inference for 3D object detection | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.3.1 Description | Distributed joint inference is to leverage multiple nodes (e.g. UEs) to provide inference results so that an aggregation of those inference results can lead to a better performance.
When a 3rd party vehicle wants to obtain relevant information of a certain vehicle 1 (e.g. position, width, length, height, profile, orientation), the data collected by the 3rd party vehicle itself is limited. For example, as shown in figure 7.3.1-1, the 3rd party vehicle, which is directly behind vehicle 1, can only obtain relevant data on the tail of vehicle 1 through sensors, and can identify the width and height of the vehicle 1 through the inference of the local 3D object detection model, but there is no way to know the length of the vehicle 1, or even a more precise vehicle profile, orientation, etc. In addition, although the location of UE1 can be known through equipment such as the radar of the 3rd party vehicle, limited by the singleness of the data, the positioning accuracy based on the information obtained by a single vehicle is limited.
Figure-7.3.1-1: Joint inference among multiple vehicles for 3D object detection
All of the above problems need to be solved through multi-vehicle joint inference. The performance to use the joint inference is shown in the Figure-7.3.1-2. Its clear shows that despite the green vehicle generating false orientations and location by its local model, the global map (i.e., the red box) can correct the orientation and location error for the green vehicle based on the aggregated results of three vehicles (i.e. blue box, green box and yellow box) [23].
Figure-7.3.1-2: distributed joint learning leads to a better inference performance |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.3.2 Pre-conditions | As shown in figure-7.3.2-1, when a vehicle accident occurs somewhere and the road is congested. Alice's auto-driving vehicle wants to know the complete situation of the accident (i.e. the exact location and shape of the accident vehicle including the length, width and height of the vehicle), so as to use the inference result for auto-driving decision in real time. Alice’s vehicle needs to find and establish connections with vehicles located in different position to the accident vehicle, and collect inference results to perform the accurate 3D object detection of the accident vehicle.
Though the accident vehicle cannot move due to the collision to a barricade afront, the electronic device can still work as normal.
Figure-7.3.2-1: Joint inference among multiple vehicles for accident vehicle detection |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.3.3 Service Flows | 1. Alice’s vehicle wants to know the complete situation of the accident vehicle. So, her car sends the request to 5G system to select the vehicles located in different positions /direction and a certain distance to the accident vehicle
2. Based on the candidate UE list located in different position to the accident vehicle, the Alice’s vehicle starts to establish direction device connection to each of the selected vehicles and transmit the 3D object detection model to them via direct device connection.
3. Alice’s vehicle receives the inference result the 3D object detection model made by the selected vehicles and further aggregate the results to acquire a highly accurate 3D object reconstruction of the accident vehicles.
4. Alice’s vehicle may also share the aggregated result to other vehicles or the application server so that they can use it to assist their own intelligent driving as well. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.3.4 Post-conditions | Thanks to candidate UE list provided by the 5G system and inference results provided by other vehicles, Alice’s vehicle can get the situation of the accident scene accurately and make a path planning to avoid road congestion effectively. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.3.5 Existing features partly or fully covering the use case functionality | In TS 22.261 clause 6.40.2, there is a requirement for FL scenario, i.e. the 5GS to assist 3rd party to determine FL members. But it is between the 5GS NF and the 3rd party. For the distributed joint inference use case, the communication is between the 3rd party UE and the UE1 or other UEs. The existing 5G system cannot support to help find the suitable UEs request by the 3rd party UE via direct device connection.
Subject to user consent, operator policy and regulatory requirements, the 5G system shall be able to expose information (e.g. candidate UEs) to an authorized 3rd party to assist the 3rd party to determine member(s) of a group of UEs (e.g. UEs of a FL group). |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.3.6 Potential New Requirements needed to support the use case | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.3.6.1 Potential Functionality Requirements | [P.R.7.3-001] Subject to user consent, operator policy, and 3rd party’s request, 5GS shall be able to provide and configure the QoS applied to a group of UEs communicating via direct device connection (e.g. part of a joint AIML inference task).
NOTE: the above requirement assumes unicast type of communication.
[P.R.7.3-002] Subject to user consent, operator policy and 3rd party’s request, the 5G system shall be able to provide information of certain UEs (e.g. located in a specific location) to an authorized 3rd party (e.g. to assist a joint AIML task using direct device communication). |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 7.3.6.2 Potential KPI Requirements | According to [24], some typical 3D objection model size and transmission KPI are listed in the table below
Model Type
Max allowed DL end-to-end latency
Experienced data rate
(PC5)
Model size
Communication service availability
PointPillar
1s
0.14 Gbit/s
18 MByte
99.99 %
SECOND
1s
0.16 Gbit/s
20 MByte
99.99 %
PV-RCNN
1s
0.4 Gbit/s
50 MByte
99.99 %
Voxel R-CNN (Car)
1s
0.22 Gbit/s
28 MByte
99.99 % |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 8 Consolidated potential requirements and KPIs | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 8.1 Consolidated potential requirements | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 8.1.1 Authorization | Table 8.1.1 –Authorization Consolidated Requirements
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 8.1.1-1
Based on user consent, operator policy and trusted 3rd party request, the 5G system shall support a means to authorize specific UEs to transmit data (e.g. AI-ML model data for a specific application,) via direct divice connection in a certain location and time.
P.R.5.2.6-001
P.R.6.2-001
CPR 8.1.1-2
Based on user consent, operator policy, and trusted 3rd party’s request, the 5G system shall be able to provide means for an operator to authorize specific UEs who participate in the same service (e.g. for the same AI-ML FL task) to exchange data with each other via direct device connection, e.g. when direct network connection cannot fulfil the required QoS.
P.R.6.1-002
P.R.7.1-001
CPR 8.1.2-3
Based on user consent, operator policy and trusted 3rd party request, the 5G system shall be able to dynamically add or remove specific UEs to/from the same service (e.g. a AI-ML federated learning task) when communicating via direct device connection.
P.R.7.1-002 |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 8.1.2 QoS control | Table 8.1.2 – QoS control Consolidated Requirements
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 8.1.2-1
Based on user consent and operator policy, the 5G system shall be able to provide means for the network to configure and modify remote UEs’ communication QoS , when a relay UE is involved, e.g., to satisfy end to end latency for proximity-based work task offloading.
NOTE 1: for proximity-based work task offloading, the data packet size transmitted over the sidelink and Uu parts of the UE indirect network connection can be different.
P.R.5.1.6-001
P.R.5.2.6-005
CPR 8.1.2-2
Subject to user consent and operator policy, the 5G system shall be able to support configuration of the QoS (e.g., latency, reliability, data rate) of a communication path using direct device connection, e.g., for AI-ML data transfer.
P.R.5.2.6-004
CPR 8.1.2-3
Based on user consent, operator policy and trusted 3rd party request, the 5G system shall be able to support means to monitor the QoS characteristics (e.g. data rate, latency) of traffic transmitted via direct device connection or relayed by a UE, and 5G network expose the monitored information to the 3rd party.
NOTE: The monitoring information doesn’t include user position-related data.
P.R.6.1-001
CPR 8.1.2-4
Subject to user consent, operator policy and trusted 3rd party request, the 5G system shall be able to provide means the network to predict and expose QoS information changes for UEs’ traffic using direct or indirect network connection (e.g., bitrate, latency, reliability).
P.R.5.2.6-002
CPR 8.1.2-5
The 5G system shall be able to support a mechanism for a trusted third-party to negotiate with the 5G system for a suitable QoS for direct device connections of multiple UEs exchanging data with each other (e.g. a group of UEs using the same AI-ML service)
P.R.6.1-003
CPR 8.1.2-6
Based on user consent, operator policy and trusted 3rd party’s request, the 5G system shall be able to support and provision an aggregated QoS for multiple remote UEs served by a relay UE.
P.R. 7.2-001
P.R. 7.2-002
CPR 8.1.2-7
Based on user consent, operator policy and trusted 3rd party’s request, the 5G system shall be able to support configuring specific QoS limitations applied to multiple UEs communicating via direct device connection (e.g. part of a joint AI-ML inference task).
NOTE: the above requirement assumes unicast type of communication.
P.R.7.3-001 |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 8.1.3 Information Exposure | Table 8.1.3 – Member selection Consolidated Requirements
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 8.1.3-1
Subject to user consent, regulation, trusted 3rd party’s request and operator policy, the 5G network shall be able to expose information to assist the 3rd party to determine candidate UEs for data transmission via direct device connection (e.g. for AIML model transfer for a specific application).
NOTE: the information does not include user’s specific positioning and can include QoS information
P.R.6.2-002
CPR 8.1.3-2
Subject to user consent, operator policy, regulation and trusted 3rd party’s request, the 5G network shall be able to expose information of certain UEs using the same service to the 3rd party (e.g. to assist a joint AIML task of UEs in a specific area using direct device communication)
NOTE: the information does not include user’s exact positioning information.
P.R.7.3-002 |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 8.1.4 Charging | Table 8.1.4 – Charging Consolidated Requirements
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 8.1.4-1
The 5G system shall be able to support charging mechanisms for multiple UEs exchanging data for the same service using the direct device connection (e.g. for AI-ML applications).
PR.5.1.6-003 |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 8.2 Consolidated potential KPIs | |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 8.2.1 Split AI/ML operation between AI/ML endpoints | Table 8.1-1 KPI Table of Split AI/ML operation between AI/ML endpoints for AI inference by leveraging direct device connection
Max allowed end-to-end latency (NOTE 1)
UL Payload size (Intermediate data size)
(NOTE 1)
UL Experienced data rate
(NOTE 1)
Service area dimension
Communication service availability
(NOTE 1)
Reliability
(NOTE 1)
Remarks
2–100 ms
≤1.5 Mbyte for each frame
≤720 Mbps
Proximity-based work task offloading for Remote driving, AR displaying/gaming, remote-controlled robotics, video recognition and One-shot object recognition
10 ms
≤ 1.6 MByte
(8 bits data format)
≤1.28 Gbps
900 m2
(30 m x 30 m)
99.999 %
99.99 %
Local AI/ML model split on factory robots
10 ms
≤ 6.4 Mbyte
(32 bits data format)
≤1.5 Gbps
Local AI/ML model split on factory robots
NOTE 1: The KPIs in the table apply to UL data transmission in case of indirect network connection.. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 8.2.2 AI/ML model/data distribution and sharing by leveraging direct device connection | Table 8.1-1 KPI Table of AI/ML model/data distribution and sharing by leveraging direct device connection
Max allowed end-to-end latency
(NOTE 1)
Experienced data rate
(NOTE 1)
Payload size
(NOTE 1)
Communication service availability
(NOTE 1)
Remark
1s
≤1.92 Gbit/s
≤240 MByte
99.9 %
AI Model Transfer Management through Direct Device Connection
3s
≤81.33 Mbyte/s
≤244 MByte
-
transfer learning for trajectory prediction
NOTE 1: The KPIs in the table apply to data transmission using direct device connection.
NOTE 2: The AI/ML model data distribution is for a specific application service |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 8.2.3 Distributed/Federated Learning by leveraging direct device connection | Table 8.1-1 KPI Table of Distributed/Federated Learning by leveraging direct device connection
Payload size
(NOTE 1)
Maximum latency
Experienced data rate
Reliability
Remark
132 MByte
2-3 s
≤528 Mbit/s
Direct device connection assisted Federated Learning (Uncompressed model)
Asynchronous Federated Learning via direct device connection
≤50 MByte
1 s
≤220 Mbit/s
99.99%
NOTE 1: The KPIs in the table apply to both UL and DL data transmission in case of indirect network connection. |
3b008e4f4eb4734158412e812ecd3c39 | 22.876 | 9 Conclusion and recommendations | Regarding the Feasibility Study on traffic characteristics and performance requirements for AI/ML Model Transfer via direct device connection, the TR analyses use cases of AIML-Ph2 as follows: • Use cases on split AI/ML operation between AI/ML endpoints for AI inference by leveraging direct device connection: ◦ Proximity based work task offloading for AI/ML inference ◦ Local AI/ML model split on factory robots. • Use cases on AI/ML model/data distribution and sharing by leveraging direct device connection: ◦ AI Model Transfer Management through Direct Device Connection; ◦ 5GS assisted transfer learning for trajectory prediction. • Use cases on Distributed/Federated Learning by leveraging direct device connection: ◦ Direct device connection assisted Federated Learning; ◦ Asynchronous FL via direct device connection; ◦ 5GS assisted distributed joint inference for 3D object detection; It is recommended to proceed with normative work including the potential new requirements identified by this TR. The consolidated potential requirements in Clause 8 are the baseline for the subsequent normative work. Annex A: Change history Change history Date Meeting TDoc CR Rev Cat Subject/Comment New version 2022.05 SA1#98e S1-221010 - - - Initial Skeleton 0.0.0 2022.09 SA1#99e - - - Inclusion of pCRs agreed at SA1#99: S1-222397; S1-222158; S1-222398; S1-222399; S1-222400; S1-222401 0.1.0 2022.11 SA1#100 Inclusion of pCRs agreed at SA1#100: S1-223629; S1-223630; S1-223713; S1-223732 0.2.0 2023.02 SA1#101 Inclusion of pCRs agreed at SA1#101: S1-230783; S1-230393; S1-230394; S1-230087; S1-230396; S1-230784; S1-230744 0.3.0 2023-03 SA#99 SP-230224 MCC clean-up for presentation to SA#99 1.0.0 2023-05 SA1#102 Inclusion of pCR agreed at SA1#102： S1-231104, S1-231509, S1-231797, S1-231741, S1-231512 1.1.0 2023-06 SA#100 SP-230513 MCC clean-up for approval by SA#10 2.0.0 2023-06 SA#100 SP-230513 Raised to v.19.0.0 by MCC following approval by SA#10 19.0.0 2023-09 SA#101 SP-231023 0002 1 F Updating of KPI consolidated requirements 19.1.0 2023-09 SA#101 SP-231023 0001 3 F Updating of functional consolidated requirements 19.1.0 |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 1 Scope | The present document describes use cases related to the following three Roaming Value-Added Services (RVAS) that are enabled by the PLMN for 5GS roaming:
• Welcome SMS
• Steering of Roaming (SoR) during the registration procedure
• Subscription-based routing to a particular core network (e.g., in a different country)
Potential requirements are derived for these three services and consolidated in a dedicated chapter. The report ends with recommendation regarding the continuation of the work.
NOTE: This document is not expected to introduce any changes to the security mechanisms between operators, and responsible groups will verify that 5GS security mechanisms are not negatively impacted by these requirements. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 2 References | The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or non‑specific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
[1] 3GPP TR 21.905: "Vocabulary for 3GPP Specifications".
[2] 3GPP TS 33.501: "Security architecture and procedures for 5G System"
[3] 3GPP TR 22.003: "Circuit Teleservices supported by a Public Land Mobile Network (PLMN)".
[4] 3GPP TS 22.011: "Service Accessibility". |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 3 Definitions of terms, symbols and abbreviations | |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 3.1 Terms | For the purposes of the present document, the terms given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1]. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 3.2 Abbreviations | For the purposes of the present document, the abbreviations given in 3GPP TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in 3GPP TR 21.905 [1].
RVAS Roaming Value-Added Services |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 4 Overview | Roaming Value-Added Services (RVAS) form part of the roaming services ecosystem and have traditionally been provided by either the PLMN or outsourced to a fully trusted entity. The RVAS provider acting on behalf of the PLMN could be any trusted 3rd party. The focus of this work is on RVAS enabled by the PLMN for 5GS roaming.
With the introduction of e2e encryption for roaming in 5GS [2], it is in some cases not possible for the trusted entities to provide RVAS in a proprietary way and they therefore need to be standardized in order to work in a multi-vendor environment.
This report describes the following three RVAS that are enabled by the PLMN for 5GS roaming:
• Welcome SMS
• Steering of Roaming (SoR) during the registration procedure
• Subscription-based routing to a particular core network (e.g. in a different country) |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5 Use cases | |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.1 Use case on welcome SMS | |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.1.1 Description | A welcome SMS is a SMS sent to a roaming subscriber’s UE when the UE is registered in a new network for the first time. The SMS typically follows a predefined template and is sent on behalf of the home operator and may contain relevant information related to the visited country e.g., the cost to call home, how to reach the operator’s customer service etc.
The use case describes how the home operator identifies that a user is registered in a new network and trigger sending a welcome SMS to the UE. The formatting and sending of the welcome SMS are done by an application server in the same way as many other SMS applications and is not described further in the use case. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.1.2 Pre-conditions | A user X has a subscription with operator MNO1.
User X is going to a country for trip and brings the phone.
One of the operators in the country is MNO2. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.1.3 Service Flows | User X arrives to the countries capital airport and turns off airplane mode on the UE at arrival.
The UE register to MNO2’s network.
MNO2 forwards the registration to user X’s HPLMN (i.e., MNO1).
MNO1 identifies that User X is registered in a new network and initiates a welcome SMS using a northbound API including the information about MNO2’s network and the needed subscriber information.
Either the HPLMN or a trusted 3rd party will trigger a welcome SMS to user X’s UE. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.1.4 Post-conditions | Shortly an SMS is delivered to the UE with a welcome SMS containing useful information related to the new country. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.1.5 Existing features partly or fully covering the use case functionality | The functionality to send MT SMS to the UE is “old as a rock” and is defined in a normative annex in 3GPP TS 22.003 [3]. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.1.6 Potential New Requirements needed to support the use case | [PR 5.1.6-001] The 5G system shall be able to support mechanisms for the HPLMN to provide a notification, including equipment and subscription identifiers, to a trusted application server when a UE successfully registers in a VPLMN. In response to the notification, the trusted application server can indicate specific actions to the HPLMN (e.g., send an SMS to the UE).
NOTE: The trusted application server can be hosted by the home operator or a trusted 3rd party and is out of 3GPP scope. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.2 Use case on Steering of Roaming (SoR) during the registration procedure | |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.2.1 Description | HPLMNs can steer their subscribers to preferred partner networks in case of roaming by means of issuing commands and updating the Operator Controlled PLMN Selector list on the USIM, either by using SMS or via signalling, as defined in TS 22.011 [2].
Additionally, for more short-term balancing of distribution across VPLMNs, operators use mechanisms to reject registration attempts from some share of UEs to certain VPLMNs to make them select a different VPLMN.
Both mechanisms – SoR as defined in 3GPP and the here described SoR during the registration procedure – can be applied in parallel by a HPLMN.
This use case describes how the home operator identifies that a roaming user attempts to register in a new network and triggers the sending of reject messages to the UE, resulting in the UE attempting to register to another VPLMN. The details of how often a reject is sent to a particular UE to achieve the desired result and to prevent the UE from being without a network, are left to the application server and not described here. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.2.2 Pre-conditions | Users X and Y have a subscription with operator HPLMN1.
Both users X and Y are travelling to another country, where two networks are available – VPLMN1 and VPLMN2. Both networks have a roaming agreement with HPLMN1.
VPLMN1 has a higher priority for both users. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.2.3 Service Flows | Users X and Y arrive at the country and switch on their phones. According to existing procedures both UEs select VPLMN1 as their first choice for registration and try to register on that network.
VPLMN1 forwards the registration request messages of the UEs of users X and Y to the HPLMN1.
HPLMN1 recognises the registration attempts and invokes the steering service via a northbound API. The steering service, hosted by the HPLMN or some trusted 3rd party, decides if some steering action is needed for any of the UEs.
In this use case it decides to allow the UE of user X to register on VPLMN1 whereas user Y’s UE should not use VPLMN1.
The steering service triggers the steering action using the northbound API for user Y’s UE, which results in a reject message being sent to this UE, including an appropriate reason for the rejection. The registration process for user X’s UE is not affected. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.2.4 Post-conditions | While the UE of user X successfully registered to VPLMN1 the UE of user Y selects VPLMN2 as the only other available network and registers there.
If more than one remaining VPLMN is available, the UE picks one of them according to network selection procedures. The process of rejecting could be repeated as needed. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.2.5 Existing features partly or fully covering the use case functionality | Registration to networks and rejecting registration attempts with different information corresponding to the reason for rejection, causing the UE to search for other networks. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.2.6 Potential New Requirements needed to support the use case | [PR 5.2.6-001] The 5G system shall be able to support mechanisms enabling the HPLMN to:
- provide a notification, including subscription and equipment identifiers, to a trusted application server when a UE tries to register in a VPLMN
- receive a notification reply from the trusted application server indicating specific actions to the HPLMN e.g., reject UE registration (with a specific cause), trigger a SoR command.
NOTE: The trusted application server can be hosted by the home operator or a trusted 3rd party and is out of 3GPP scope. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.3 Use case on Subscription-based routing to a particular core network | |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.3.1 Description | Some operators use more than one PLMN ID, e.g., multi-national operators. Due to certain business and operational demands, it might be necessary to route signalling traffic of a certain customer segment, typically from a certain IMSI range of USIMs, of a PLMN to another PLMN and to further handle the subscriber there. This means the signalling is not handled by the "real" HPLMN (according to MNC and MCC) but by some alternative PLMN.
This e.g. enables the case where several national subsidiaries of a multi-national operator offer various services for different customer segments but for operational efficiency the actual service for a certain group is provided by only one dedicated network.
This mechanism is not visible for the UE and it therefore do not need any additional features to support this RVAS. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.3.2 Pre-conditions | Subscriptions a, b, c and d are with operator MNO1.
Subscriptions b and c are part of a certain customer segment X and this information is part of the subscription.
MNO1 has an agreement with MNO2 that MNO2 shall handle the signalling of subscriptions of all UEs belonging to the customer segment X. For this purpose, there is a connection between the networks of MNO1 and MNO2. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.3.3 Service Flows | |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.3.3.1 Non roaming case | The UEs of subscribers a, b, c and d attach to the PLMN of MNO1.
The network recognizes subscriptions b and c to be part of customer segment X and forwards the signalling to the PLMN of MNO2 via the pre-established connection.
Subscriptions a and d are not affected.
Later, subscription c is removed from customer segment X by customer care. This results in removal of the corresponding information in the subscription. From now on signalling related to subscription c will be handled by the network of MNO1 again.
Further on, subscription a is added to the customer segment X by customer care and subscription data are updated accordingly. So, signalling related to subscription a will be handled by the network of MNO2.
The UEs of subscribers c and a are not aware of these updates. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.3.3.2 Roaming case | Subscribers a, b, c and d attach to a VPLMN. The corresponding signalling is routed to their HPLMN (network of MNO1).
The further procedure is the same as in the non-roaming case: The HPLMN recognizes subscriptions b and c to be part of customer group X and forwards the signalling to the PLMN of MNO2 via the pre-established connection. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.3.4 Post-conditions | Subscriptions of customer group X are handled by the network of MNO2, all other subscriptions by the regular HPLMN MNO1. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.3.5 Existing features partly or fully covering the use case functionality | Subscriptions can contain a routing indicator which might be re-used for assigning a subscription to a certain customer group which requires routing to a different network. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 5.3.6 Potential New Requirements needed to support the use case | [PR 5.3.6-001] The 5G system shall be able to support a mechanism for forwarding signalling traffic pertaining to UEs of specific subscribers from their HPLMN to a target PLMN, e.g., to enable further handling of those UEs by the target PLMN. The forwarding mechanism shall minimize signalling traffic in the HPLMN, e.g., by using efficient means to forward traffic from selected UEs.
NOTE 1: The above requirement assumes that the HPLMN has an agreement with the target PLMN and routing policies are in place.
NOTE 2: In case of UEs connected via a VPLMN, it is assumed that signalling traffic is forwarded to the target PLMN by the HPLMN. |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 6 Consolidated potential requirements | Table 6-1 –Consolidated Potential Requirements
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 6-001
The 5G system shall be able to support mechanisms for the HPLMN to provide a notification, including equipment and subscription identifiers, to a trusted application server when a UE successfully registers in a VPLMN. In response to the notification, the trusted application server can indicate specific actions to the HPLMN (e.g., send an SMS to the UE).
NOTE: The trusted application server can be hosted by the home operator or a trusted 3rd party and is out of 3GPP scope.
PR 5.1.6-001
CPR 6-002
The 5G system shall be able to support mechanisms enabling the HPLMN to:
- provide a notification, including subscription and equipment identifiers, to a trusted application server when a UE tries to register in a VPLMN
- receive a notification reply from the trusted application server indicating specific actions to the HPLMN e.g., reject UE registration (with a specific cause), trigger a SoR command.
NOTE: The trusted application server can be hosted by the home operator or a trusted 3rd party and is out of 3GPP scope.
PR 5.2.6-001
CPR 6-003
The 5G system shall be able to support a mechanism for forwarding signalling traffic pertaining to UEs of specific subscribers from their HPLMN to a target PLMN, e.g., to enable further handling of those UEs by the target PLMN. The forwarding mechanism shall minimize signalling traffic in the HPLMN, e.g., by using efficient means to forward traffic from selected UEs.
NOTE 1: The above requirement assumes that the HPLMN has an agreement with the target PLMN, and routing policies are in place.
NOTE 2: In case of UEs connected via a VPLMN, it is assumed that signalling traffic is forwarded to the target PLMN by the HPLMN.
PR 5.3.6-001 |
46022d2d65fd68fe9fd8662086a95712 | 22.877 | 7 Conclusion and recommendations | This technical report provides use cases and potential new requirements for the three RVAS: • Welcome SMS • Steering of Roaming (SoR) during the registration procedure • Subscription-based routing to a particular core network (e.g., in a different country) The resulting service requirements have been consolidated and can be found in chapter 6. It is recommended to consider the consolidated requirements identified in this TR as the baseline for subsequent normative work. Annex A (informative): Change history Change history Date Meeting Tdoc CR Rev Cat Subject/Comment New version 2022-08 SA1#99-e S1-202010 TR skeleton 0.0.0 2022-09 SA1#99-e S1-222407, S1-222408, S1-222409, S1-222410, S1-222411 Scope, Overview Welcome SMS Use case SoR during registration IMSI based routing 0.1.0 2022-11 SA1#100 S1-223375 S1-223376 S1-223388 S1-223378 Editorial clean-up Update of use case 3 Consolidation Conclusion 0.2.0 2022-12 SA#98e SP-221265 MCC clean-up for presentation for one-step approval to TSG SA 1.0.0 2022-12 SA#98e SP-221265 Raised to v.19.0.0 following SA#98e one-step approval 19.0.0 |
17e8174f94d72a34a3d8a81dbfebc7a5 | 22.882 | 1 Scope | The present document provides stage 1 use cases and potential 5G requirements on the following aspects regarding enhancements to Energy Efficiency of 5G network and application service enabler aspects:
- Defining and supporting energy efficiency criteria as part of communication service to user and application services;
- Supporting information exposure of systematic energy consumption or level of energy efficiency to vertical customers;
- Gap analysis between the identified potential requirements and existing 5GS requirements or functionalities;
- Potential requirements on security, charging and privacy aspects. |
17e8174f94d72a34a3d8a81dbfebc7a5 | 22.882 | 2 References | The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or non‑specific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
[1] 3GPP TR 21.905: "Vocabulary for 3GPP Specifications".
[2] ETSI ES 201 554: "Environmental Engineering (EE); Measurement method for Energy efficiency of Mobile Core network and Radio Access Control equipment".
[3] ETSI ES 203 228: "Environmental Engineering (EE); Assessment of mobile network energy efficiency".
[4] GSMA Intelligence: "Going green: benchmarking the energy efficiency of mobile", June 2021.
[5] 3GPP TR 21.866: "Study on Energy Efficiency Aspects of 3GPP Standards".
[6] 3GPP TS 28.310: "Management and orchestration; Energy efficiency of 5G".
[7] 3GPP TR 28.813: "Management and orchestration; Study on new aspects of Energy Efficiency (EE) for 5G".
[8] 3GPP TR 38.864: "Study on network energy savings for NR ".
[9] ETSI ES 202 336‑1: "Environmental Engineering (EE); Monitoring and control interface for infrastructure equipment (power, cooling and building environment systems used in telecommunication networks); Part 1: Generic Interface".
[10] ETSI ES 202 336‑12: "Environmental Engineering (EE); Monitoring and control interface for infrastructure equipment (power, cooling and building environment systems used in telecommunication networks); Part 12: ICT equipment power, energy and environmental parameters monitoring information model".
[11] 3GPP TS 28.552: "Management and orchestration; 5G performance measurements".
[12] 3GPP TS 28.554: "Management and orchestration; 5G end to end Key Performance Indicators (KPI)".
[13] 3GPP TS 28.622: "Telecommunication management; Generic Network Resource Model (NRM) Integration Reference Point (IRP); Information Service (IS)".
[14] Void
[15] 3GPP TS 22.261: "Service requirements for the 5G system".
[16] 3GPP TS 22.115: "Service aspects; Charging and billing"
[17] 3GPP TS 23.503: "Policy and charging control framework for the 5G System (5GS); Stage 2"
[18] 3GPP TS 32.299: " Telecommunication management; Charging management; Diameter charging applications".
[19] NGMN: "NGMN Energy Efficiency White Paper, Phase 2", Dec 2022.
[20] GSMA: "5G energy efficiencies: green is the new black, Nov 2020".
[21] Renewable Energy Certificates (RECs): https://www.epa.gov/green-power-markets/renewable-energy-certificates-recs
[22] ETSI EN 303 472: "Environmental Engineering (EE); Energy Efficiency measurement methodology and metrics for RAN equipment".
[23] ETSI GS OEU 020 (v1.1.1): "Operational energy Efficiency for Users (OEU); Carbon equivalent Intensity measurement; Operational infrastructures; Global KPIs; Global KPIs for ICT Sites".
[24] Methodological standard for the environmental assessment for Internet Service Provision (ISP), February 2023, https://librairie.ademe.fr/cadic/7695/pcr_internet_services_provision__english_version.pdf. Accessed April 29th, 2023
[25] 3GPP TR 28.829: "Study on network and service operations for energy utilities"
[26] 3GPP TR 28.913: "Study on new aspects of EE for 5G networks phase 2" |
17e8174f94d72a34a3d8a81dbfebc7a5 | 22.882 | 3 Definitions of terms, symbols and abbreviations | |
17e8174f94d72a34a3d8a81dbfebc7a5 | 22.882 | 3.1 Terms | For the purposes of the present document, the terms given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
energy state: state of a cell, a network element and/or a network function with respect to energy, e.g. (not) energy saving states, which are defined in TS 28.310 [6].
energy charging rate: a means of determining the energy consumption consequence (use of energy credit) associated with charging events.
energy credit: a quantity of credit associated with the subscriber that can be used for credit control by the 5G system.
maximum energy consumption: a policy establishing an upper bound on the quantity of energy consumption by the 5G system in a specific period of time or space, e.g. energy consumption inside a given service area.
maximum energy credit limit: a policy establishing an upper bound on the aggregate quantity of energy consumption by the 5G system to provide services to a specific subscriber, e.g. in kilowatt hours.
NOTE 1: The term maximum energy credit limit is distinct from 'maximum energy consumption' because the credit limit is a total amount of energy consumed, where maximum energy consumption is a limit to the consumption in a given interval of time.
carbon emissions: kilograms of equivalent carbon dioxide emitted (kg of CO2 equivalent)
carbon intensity: quantity of CO2 equivalent emission per unit of final energy consumption for an operational period of use [23]
communication service pooling: refers to an operator serving subscribers from other operators traditionally providing communication service over the same geographical area, but which temporarily stop providing their service over their own network infrastructure for energy saving, e.g. via cell switch-off.
NOTE 2: Communication service pooling can be achieved, e.g. via NG-RAN sharing techniques or national roaming agreements wherever applicable, and apply to coverage and/or capacity layers.
renewable energy: energy from renewable sources as energy from renewable non-fossil sources, namely wind, solar, aerothermal, geothermal, hydrothermal and ocean energy, hydropower, biomass, landfill gas, sewage treatment plant gas and biogases
NOTE 3: This definition was taken from [22]. |
17e8174f94d72a34a3d8a81dbfebc7a5 | 22.882 | 3.2 Abbreviations | For the purposes of the present document, the abbreviations given in 3GPP TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in 3GPP TR 21.905 [1].
AS Application Server
DV Data Volume
EC Energy Consumption
EE Energy Efficiency |
17e8174f94d72a34a3d8a81dbfebc7a5 | 22.882 | 4 Overview | Climate change and global energy shortage are issues that requires international cooperation and coordinated solutions at all levels, many regions and countries have published related policies and requirements to control carbon release and promote energy efficiency. These policies have made energy efficiency a strategic priority for many telecom operators around the world. Energy efficiency has been considered in many standard groups and specifications.
The existing studies concentrate more on how to satisfy user experience and try to achieve energy efficiency at the same time and achieve energy efficiency within the network, so the requirements, use cases and solutions are basically within the network itself. Verticals and customers have no approach for energy efficiency related information from the network.
Introducing energy efficiency as a service will allow users to have the choice to select proper energy efficiency criteria as well as other network performance parameters when they need them, which may include:
- Define and support energy efficiency criteria as part of communication service to user and application services.
- Provide information exposure on systematic energy consumption or level of energy efficiency to vertical customers.
Such as in satellite and terrestrial convenience scenario, for some regions where both satellite and terrestrial coverage exist, energy saving could be taken as a dimension while providing the communication service, users or operators could have the choice to find out a best way in satisfying both user experience and energy efficiency. From another perspective, the network could also react to different energy consumption modes of applications or adjust network resources.
Both aspects above need more interactions between applications and networks on energy consumption status. It is worth considering how to deliver services with energy efficiency as service criteria, associated with verticals’ preferences, and how to support the policy of handling energy as part of a subscription. |
17e8174f94d72a34a3d8a81dbfebc7a5 | 22.882 | 5 Use cases | |
17e8174f94d72a34a3d8a81dbfebc7a5 | 22.882 | 5.1 Use case on energy consumption as a performance criteria for best effort communication | |
17e8174f94d72a34a3d8a81dbfebc7a5 | 22.882 | 5.1.1 Description | Currently energy consumption and efficiency can be monitored and considered through O&M and network operation, but not as a service performance criterion, as for example bit rate, latency or availability. Guidance from SA to all working groups states:
"The EE-specific efforts so far undertaken e.g., in SA5 have aimed mostly at improving the energy efficiency by impacting the operations of the system. As we now are starting to specify the 5G-Advanced features, TSG SA kindly requests the recipient WGs and TSGs to consider EE even more as a guiding principle when developing new solutions and evolving the 3GPP systems specification, in addition to the other established principles of 3GPP system design.
TSG SA clarifies that in addition to EE, other system level criteria shall continue to be met (i.e. the energy efficiency aspects of a solution defined in 3GPP is not to be interpreted to take priority or to be alternative to security, privacy, complexity etc. and to meeting the requirements and performance targets of the specific feature(s) the solution addresses)."
There is an important type of traffic where energy efficiency policy, for example a maximum amount of energy to be utilized could be applied without conflict with this guidance. Best effort traffic is a type of traffic that is provided as a service to customers everything else being equal. Of course, security, privacy and complexity principles will not be sacrificed, but there is no conflict between a service policy that constrains performance (e.g. latency, throughput, even availability) on the basis of energy consumption and a best effort service, since there are no guarantees in the case of best effort traffic. We can say that best effort traffic is not associated with QoS policy service performance level criteria.
Today the 5G system works to support services efficiently, though does not take into account energy consumption at the service level. The use case explores a particular opportunity to identify this information and use it to make more efficient use of all network resources without sacrificing service quality. In particular, information gathered through O&M, and in the future possibly from the network (see 5.1.5 which identifies a gap and opportunity), can be leveraged to make it possible to employ energy consumption information as part of service delivery.
In the following use case, the possibility of using energy consumption as a new service criterion for this less constrained type of mobile telecommunication service is explored.
A large-scale logistics company L has deployed a large number of communicating components. These are integrated into vehicles, palettes, facilities, etc. Essentially, IoT terminals enable remote tracking and monitoring functions. The information gathered is relevant, but not constrained with respect to latency. In fact, eventual delivery (e.g. after hours or even a full day) of communication is entirely acceptable for L. The MNO M offers a 'green service' which limits the rate of energy consumed for communication over a particular time interval (e.g. per day) and this service is appropriate for L, whose overall corporate goals are also served by 'green service,' as they strive to operate with energy efficiency. |
17e8174f94d72a34a3d8a81dbfebc7a5 | 22.882 | 5.1.2 Pre-conditions | L deploys many UEs with associated 'green service' subscriptions from M. These subscriptions policies include the following criteria:
- Best Effort Service (service that is not associated with QoS policy service performance level criteria)
- Energy Constraints applied to service delivery |
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