-AC is always much higher than that of the other two traditional AC policies. These results clearly demonstrate the performance gain of the proposed Num-AC and QoS-AC policies in terms of the number of admissible UEs. 9.2 Smart Handoff Policy Design for RAN Slicing Num-AC QoS-AC Number of BSs Figure 9.5 Comparisons of the number of admissible UEs vs. number of BSs. (The number of UEs is 200, and the number of NSs 20.) Smart Handoff Policy Design for RAN Slicing Owing to the introduction of network slicing into radio access networks forming a UE-BS-NS three-layer association, handoff becomes very complicated and cannot be resolved by conventional policies. In this section, we propose a multi-agent reinforcement LEarning based Smart handoff policy, named LESS, to reduce handoff cost while main- taining user QoS requirements in RAN slicing. LESS is based on a modified distributed Q-learning algorithm with small action space to make handoff decisions. Numerical results show that in typical scenarios, LESS can significantly reduce the handoff cost when compared with traditional handoff policies without learning. 9.2.1 RAN Slice Based Mobile Network Model We consider an NS-based mobile network architecture shown in Figure 9.6, which consists of multiple end-to-end NSs, BSs, as well as UEs. These NSs share the physical resources in both core networks and RAN. Each NS has different NF modules, such as connection management, mobility management, security, etc., to provide a specific service for UEs. The detailed descriptions of the network architecture can be found in [7]. Here we focus on RAN slicing from the mobility management perspective. We consider a multi-BS and multi-NS RAN model shown in Figure 9.6. Let B, N, and V be the set of BSs, NSs, and UEs, respectively. We assume that UEs in the system move at a random speed and in a random direction. Similar to that in [14], we use two parameters to describe QoS requirements: minimum threshold of transmission rate zmin and endurable 9 Access Control and Handoff Policy De