Joint Resource Optimization for Multicell Networks with Wireless Energy Harvesting Relays

This paper first considers a multicell network deployment where the base station (BS) of each cell communicates with its cell-edge user with the assistance of an amplify-and-forward (AF) relay node. Equipped with a power splitter and a wireless energy harvester, the self-sustaining relay scavenges radio-frequency (RF) energy from the received signals to process and forward information. Our aim is to develop a resource allocation scheme that jointly optimizes 1) BS transmit power, 2) received power-splitting factors for energy harvesting and information processing at the relays, and 3) relay transmit power. In the face of strong intercell interference and limited radio resources, we formulate three highly nonconvex problems with the objectives of sum-rate maximization, max-min throughput fairness, and sum-power minimization. To solve such challenging problems, we propose applying the successive convex approximation approach and devising iterative algorithms based on geometric programming and difference-of-convex-function programming. The proposed algorithms transform the nonconvex problems into a sequence of convex problems, each of which is solved very efficiently by the interior-point method. We prove that our algorithms converge to the locally optimal solutions that satisfy the Karush-Kuhn-Tucker (KKT) conditions of the original nonconvex problems. We then extend our results to the case of decode-and-forward (DF) relaying with variable timeslot durations. We show that our resource allocation solutions in this case offer better throughput than that of the AF counterpart with equal timeslot durations, albeit at higher computational complexity. Numerical results confirm that the proposed joint optimization solutions substantially improve network performance, compared with cases where the radio resource parameters are individually optimized.