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This paper considers an admission control problem for a mobile cloud computing hotspot with a cloudlet. We first formulate the admission control problem as a Markov decision process (MDP). The objective is to maximize the average reward in terms of revenue for cloudlet service providers. However, the MDP could suffer from the complexity problem (i.e., curse of dimensionality). Therefore, we apply the simulation-based algorithm to obtain the optimal policy for the MDP. The algorithm can estimate the performance measure to update the policy gradient in an online fashion. The performance evaluation of the proposed algorithm uses parameters setting profiled from real mobile applications. The extensive simulation results clearly show the convergence and the efficiency of the proposed algorithm. © 2014 IEEE.

In this paper, we consider a decentralized wireless communication system with wireless energy transfer capability. We aim to minimize the total number of packets waiting at wireless nodes for the whole system. We first formulated the optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve an optimization problem with constraints, we applied the Lagrangian multiplier and the policy gradient method. In addition, to reduce the complexity of DEC-POMDP, we proposed a decentralized online learning algorithm with minimum communication among the wireless nodes. Under appropriate conditions, we showed that the proposed algorithm converges to a local optimal solution. The simulation results clearly showed the convergence as well as the efficiency of the proposed algorithm. © 2014 IEEE.