Dynamic computation offloading in time-varying environment forultra-dense networks: a stochastic game approach

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks (UDNs) and mobile edge computing (MEC)are considered as important solutions.In the MEC enabled UDNs, one of the most important issues is computation offloading. Although a number of workhave been done toward this issue, the problem of dynamic computation offloading in time-varying environment,especially the dynamic computation offloading problem for multi-user, has not been fully considered. Therefore, inorder to fill this gap, the dynamic computation offloading problem in time-varying environment for multi-user isconsidered in this paper. By considering the dynamic changes of channel state and users queue state, the dynamiccomputation offloading problem for multi-user is formulated as a stochastic game, which aims to optimize the delayand packet loss rate of users. To find the optimal solution of the formulated optimization problem, NashQ-learning(NQLN) algorithm is proposed which can be quickly converged to a Nash equilibrium solution. Finally, extensivesimulation results are presented to demonstrate the superiority of NQLN algorithm. It is shown that NQLN algorithmhas better optimization performance than the benchmark schemes.