Future cellular networks will be dense and require key traffic management technologies for fine-grained network control. The problem gets more complicated in the presence of different network segments with bottleneck links limiting the desired quality of service (QoS) delivery to the last mile user. In this work, we first design a framework for software-defined cellular networks and then propose new mechanisms for management of QoS and non-QoS users traffic considering both access and backhaul networks, jointly. The overall SDN-LTE system and related approaches are developed and tested using network simulator in different network environments. Especially, when the users are non-uniformly distributed, the results shows that compared to other approaches, the proposed load distribution algorithm enables at least 6 and 23 increase in the average QoS user downlink throughput and the aggregate throughput of 40 users with lowest throughput (edge users), respectively. Also, the proposed system efficiently achieves desired QoS and handles the network congestion without incurring significant overhead.
@cite_0: There has been a lot of research focusing on optimizing the Long Term Evolution (LTE) networks performance. A large part of this research focused on how to optimize the LTE radio interface to efficiently distribute the scarce radio resources among the LTE users. Since LTE offers a diverse amount of services, the Quality of Service (QoS) differentiation is a major challenge. QoS is handled in LTE at different levels; the most important one is the radio scheduler. The QoS aware radio scheduler is designed to guarantee different service requirements. However, if the LTE radio interface is fully optimized towards QoS guarantees, what happen when the LTE transport network runs into congestion? Congestion can happen at any point in the network, and normally congested LTE backhaul tends to create large unfairness among the users, even among the ones with the same QoS class. The LTE transport congestion can occur at any router in the backhaul network. In this paper, we present two novel algorithms, which focus on the LTE transport congestion. The first algorithm is a congestion detection mechanism (CD) that identifies the transport network congestion as early as possible, and triggers the congestion control function. The second algorithm preserves and optimizes the QoS performance of each user over the transport to match the radio scheduler service differentiation, i.e., achieving fairness among the users in the same QoS demand and guaranteeing the enforced radio QoS weights for different service classes.
@cite_29: Self-organizing network (SON) technology aims at autonomously deploying, optimizing and repairing radio access networks (RANs). SON algorithms typically use key performance indicators (KPIs) from the RAN. It is shown that in certain cases, it is essential to take into account the impact of the backhaul state in the design of the SON algorithm. We revisit the base station (BS) load definition taking into account the backhaul state. We provide an analytical formula for the load along with a simple estimator for both elastic and guaranteed bit-rate (GBR) traffic. We incorporate the proposed load estimator in a self-optimized load balancing (LB) algorithm. Simulation results for a backhaul constrained heterogeneous network illustrate how the correct load definition can guarantee a proper operation of the SON algorithm.
As shown in Table , for backhaul networks @cite_29 @cite_0 propose solutions that do not consider SDN. @cite_29 two load detection approaches are proposed in backhaul constrained LTE networks, first runs locally, while the second runs globally. The designed approach enables cells to dynamically change their network coverage for better LB. Results show that the local scheme can balance an individual BS scheduler load, while the global scheme enables different types of cells to adjust their coverage based on the available resources resulting in better load distribution across different cells. This work only assume elastic traffic and it does not evaluate the benefits achieved when both local and global LB approaches work together. A backhaul network congestion detection and control strategy is described in @cite_0 , which periodically injects some packets into the network to avoid non-guaranteed bit rate (NGBR) packets to be randomly dropped upon backhaul congestion. The main drawback of this approach is the large overhead due to the probe packets sent between the gateway and the LTE eNodeB for congestion detection.