Optimization of a Power Splitting Protocol for Two-Way Multiple Energy Harvesting Relay System

TL;DR

This paper proposes an optimized power splitting protocol for a two-way energy harvesting relay system, jointly optimizing relay selection, power splitting ratios, and transmit power to maximize data rates, using geometric programming and swarm optimization.

Contribution

It introduces a joint-optimization framework for relay selection and power splitting in EH-based TWR systems, combining geometric programming and particle swarm optimization for improved performance.

Findings

The proposed scheme achieves near-optimal data rates compared to branch-and-bound methods.

GP-based optimization outperforms dual problem-based approaches.

Numerical results demonstrate the effectiveness of the joint optimization.

Abstract

Energy harvesting (EH) combined with cooperative communications constitutes a promising solution for future wireless technologies. They enable additional efficiency and increased lifetime to wireless networks. This paper investigates a multiple-relay selection scheme for an EH-based two-way relaying (TWR) system. All relays are considered as EH nodes that harvest energy from renewable energy and radio frequency (RF) sources. Some of them are selected to forward data to the destinations. The power splitting (PS) protocol, by which the EH node splits the input RF signal into two components for EH and information transmission, is adopted at the relay nodes. The objective is to jointly optimize i) the set of selected relays, ii) their PS ratios, and iii) their transmit power levels in order to maximize data rate-based utilities over multiple coherent time slots. A joint-optimization solution based on geometric programming (GP) and binary particle swarm optimization is proposed to solve non-convex problems for two utility functions reflecting the level of fairness in the TWR transmission. Numerical results illustrate the system's behavior versus various parameters and show that the performance of the proposed scheme is very close to that of the optimal branch-and-bound method and that GP outperforms the dual problem-based method.