tored action policy (4) (S) of UE i is stated as no(s) E A, arbitrarily, (9.24) (sfS or a(t), otherwise, where a;(t) is the action of UE i at time t. From Ref. [17] we can get the corollary that for an arbitrary state S, we have s()amiQ(A) (9.25) Thus, when we get the converged q-table, choosing the current stored action (1) (S) for each individual UE can guarantee the minimum handoff cost. In general, based on e-greedy policy LESS-DL can be described as: before q-value is converged, we choose the current stored policy (1) (S) as the target NS-BS pair for each UE respectively with probability p = (1 - - e), and choose other pairs randomly with probability p = E. Then, we update the q-values in a distributed manner accord- ing to (9.21) by using the obtained handoff cost. Finally, we update the current stored action policy based on (9.24) for the next handoff decision. Once we get the converged q-tables, we always choose the current stored action as the target NS-BS pair for each individual UE. 9 Access Control and Handoff Policy Design for RAN Slicing 9.2.4 Experiment, Results, and Discussions In this section, we compare the performance of LESS with three other handoff mechanisms: Max-SINR, NS-Prior, and LESS-DL. In detail, Max-SINR first selects the BS with the maxi- mum SINR for UEs [12], and then finds the NS deployed in this BS with satisfied QoS provi- sioning. If such a BS-NS pair is found, select them as the target; otherwise, deploy a new NS that satisfies the UE's QoS. NS-Prior mechanism first selects the NS that satisfies the QoS requirement of UEs, and then finds the BS covered by this NS with sufficient bandwidth. Lastly, by comparing with LESS-DL, we can verify the effectiveness of LESS-DS data sharing policy. The handoff trigger condition is the same for all the four mechanisms in (9.18). We consider a network that consists of an MBS located at the center of a circular area with a radius of 1000 m and varying number of PBSs, FBSs, and UEs. The number of NSs deployed is 40. Each NS covers 8 BSs rando