Wireless Information and Power Transfer: Energy Efficiency Optimization in OFDMA Systems

TL;DR

This paper develops resource allocation algorithms for OFDMA systems with simultaneous wireless information and power transfer, optimizing energy efficiency while considering different receiver power splitting capabilities.

Contribution

It introduces novel iterative algorithms for non-convex optimization in SWIPT OFDMA systems with both continuous and discrete power splitting models.

Findings

Algorithms approach optimal solutions quickly

Trade-offs between energy efficiency and system capacity

Effective power transfer with minimal delay constraints

Abstract

This paper considers orthogonal frequency division multiple access systems with simultaneous wireless information and power transfer. We study the resource allocation algorithm design for maximization of the energy efficiency of data transmission. In particular, we focus on power splitting hybrid receivers which are able to split the received signals into two power streams for concurrent information decoding and energy harvesting. Two scenarios are investigated considering different power splitting abilities of the receivers. In the first scenario, we assume receivers which can split the received power into a continuous set of power streams with arbitrary power splitting ratios. In the second scenario, we examine receivers which can split the received power only into a discrete set of power streams with fixed power splitting ratios. In both scenarios, we formulate the corresponding algorithm design as a non-convex optimization problem which takes into account the circuit power consumption, the minimum data rate requirements of delay constrained services, the minimum required system data rate, and the minimum amount of power that has to be delivered to the receivers. Subsequently, by exploiting fractional programming and dual decomposition, suboptimal iterative resource allocation algorithms are proposed to solve the non-convex problems. Simulation results illustrate that the proposed iterative resource allocation algorithms approach the optimal solution within a small number of iterations and unveil the trade-off between energy efficiency, system capacity, and wireless power transfer.