Balancing Available Energy Distribution in mMIMO SWIPT Sensor Networks With Low Resolution ADC/DAC

Abstract

This paper addresses the fair distribution of available energy (AE) in a massive multiple-input multiple-output (mMIMO) simultaneous wireless information and power transfer (SWIPT) sensor network, where sensor nodes are equipped with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). We define the system model and estimate the channel using linear minimum mean square error (LMMSE) estimation. To analyze system performance, we derive closed-form expressions for the signal-to-interference-plus-noise ratio (SINR), and harvested energy. These expressions allow us to evaluate the impact of low-resolution ADCs/DACs on both the achievable rate and energy harvesting. Based on these expressions, we formulate an optimization problem fair AE distribution. To solve this non-convex problem, we propose an alternating optimization (AO) algorithm that optimizes the downlink/uplink (DL/UL) powers and the power splitting (PS) ratio. Monte Carlo simulations confirm that our closed-form analysis accurately represents the system performance and that the optimization algorithm distributes AE more fairly compared to the equal power and power splitting case.

Keywords

• Available energy
• low-resolution analog-to-digital converters/digital-to-analog converters
• massive multiple-input multiple-output
• optimization
• sensor network