A dynamic policy for selecting D2D mobile relays

Abstract

User-to-network relaying via Device-to-Device (D2D) communications is a promising technique for improving the performance of cellular networks. Since in practice the users serving as relays are mobile, a dynamic relay selection scheme is required to meet the requirements of communications and optimize the system performance In this paper, we propose a dynamic relay selection policy that maximizes the performance of cellular networks (e.g. throughput, reliability, coverage) under cost constraints (e.g. transmission power). We represent the relays’ dynamics as a Markov Decision Process (MDP) and, to limit the signaling overhead, we assume that only the locations of selected relays are observable. Therefore, the dynamic relay selection process is modeled as a Constrained Partially Observable Markov Decision Process (CPOMDP). Since finding the exact solution of such a framework is intractable, we develop a point-based value iteration solution and evaluate its performance. Furthermore, with the aim to reduce the complexity of the solution we prove the submodularity property of the reward and cost value functions and deduce a greedy solution that is scalable with the number of discovered relays. For the multi-user scenario, a distributed approach is introduced in order to reduce further the complexity and overhead of the proposed solution. We provide simulation results of a scenario where throughput is maximized under energy constraint and show the gain achieved by the proposed relay selection policy with respect to traditional cellular networks.