ber of handoffs, and (c) outage probability of UEs. 9 Access Control and Handoff Policy Design for RAN Slicing Next, we compare the handoff performance for the four handoff mechanisms with dif- ferent number of UEs, as shown in Figure 9.8. Figure 9.8a shows that the learning based mechanisms LESS and LESS-DL significantly outperform the other two in terms of handoff cost. As the learning objective is handoff cost, the performance in terms of the number of handoffs and UE outage probability of LESS is very close to that of NS-Prior mechanism, which considers NS service type when making handoff decisions. This can be concluded from Figure 9.8b,c. Summary In this chapter, we discuss the access control and handoff policy design for RAN slicing. First, we propose a unified framework for access control in RAN slicing. Owing to resource limitation, not all users' QoS requirements can be satisfied simultaneously. Hence, we design two user access control policies to select admissible users for optimizing the QoS and the number of admissible users respectively. Numerical results show that in typical scenarios, our proposed access control policies can significantly outperform traditional policies in terms of the number of admissible users. Next, we investigate the handoff issue for mobile users in RAN slicing. We propose a multi-agent reinforcement LEarning based Smart handoff policy, named LESS, to reduce handoff cost while maintaining 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. Bibliography 1 Zhang, H., Liu, N., Chu, X. et al. (2017). Network slicing based 5G and future mobile networks: mobility, resource management, and challenges. IEEE Communications Magazine 55 (8): 138-145. 2 Zhang, L., Ijaz, A., Mao, J. et al. (2017). Multi-service signal multiplexing a