Multi-Objective Power Allocation for Energy Efficient Wireless Information and Power Transfer Systems

TL;DR

This paper develops a multi-objective resource allocation algorithm for SWIPT systems, balancing energy efficiency for information and power transfer, while ensuring security against eavesdroppers, with demonstrated performance improvements.

Contribution

It introduces a novel multi-objective optimization framework for energy-efficient SWIPT systems, including secure communication considerations with artificial noise injection.

Findings

Trade-off region between energy efficiencies and transmit power shown in simulations.

Proposed algorithm outperforms baseline schemes in energy efficiency and security.

Significant performance gains demonstrated through simulations.

Abstract

Simultaneous wireless information and power transfer (SWIPT) provides a promising solution for enabling perpetual wireless networks. As energy efficiency (EE) is an im- portant evaluation of system performance, this thesis studies energy-efficient resource allocation algorithm designs in SWIPT systems. We first investigate the trade-off between the EE for information transmission, the EE for power transfer, and the total transmit power in a basic SWIPT system with separated receivers. A multi-objective optimization problem is formulated under the constraint of maximum transmit power. We propose an algorithm which achieves flexible resource allocation for energy efficiencies maxi- mization and transmit power minimization. The trade-off region of the system design objectives is shown in simulation results. Further, we consider secure communication in a SWIPT system with power splitting receivers. Artificial noise is injected to the com- munication channel to combat the eavesdropping capability of potential eavesdroppers. A power-efficient resource allocation algorithm is developed when multiple legitimate information receivers and multi-antenna potential eavesdroppers co-exist in the system. Simulation results demonstrate a significant performance gain by the proposed optimal algorithm compared to suboptimal baseline schemes.