Optimal Beamforming and Time Allocation for Partially Wireless Powered Sensor Networks with Downlink SWIPT

TL;DR

This paper develops optimal beamforming and time allocation strategies for partially wireless powered sensor networks using SWIPT, balancing self-sustainability and reliability in IoT applications.

Contribution

It introduces semi-closed-form globally optimal solutions for joint downlink and uplink beamforming and time allocation in SDMA and TDMA scenarios, with low complexity.

Findings

Proposed solutions achieve global optimality.

Simulation results confirm effectiveness and low complexity.

Balances energy harvesting and reliable communication.

Abstract

Wireless powered sensor networks (WPSNs) have emerged as a key development towards the future self-sustainable Internet of Things (IoT) networks. To achieve a good balance between self-sustainability and reliability, partially WPSNs with a mixed power solution are desirable for practical applications. Specifically, most of the sensor nodes are wireless powered but the key sensor node adopts traditional wire/battery power for reliability. As a result, this paper mainly investigates optimal design for the partially WPSNs in which simultaneous wireless information and power transfer (SWIPT) is adopted in the downlink. Two scenarios with space division multiple access (SDMA) and time division multiple access (TDMA) in the uplink are considered. For both the SDMA-enabled and TDMA-enabled partially WPSNs, joint design of downlink beamforming, uplink beamforming and time allocation is investigated to maximize the uplink sum rate while guaranteeing the quality-of-service (i.e., satisfying the downlink rate constraint) at the key sensor node. After analyzing the feasibility of uplink sum rate maximization problems and the influence of the downlink rate constraint, semi-closed-form optimal solutions for both SDMA-enabled and TDMA-enabled WPSNs are proposed with guaranteed global optimality. Complexity analysis is also provided to justify the advantage of the proposed solutions in low complexity. The effectiveness and optimality of the proposed optimal solutions are finally demonstrated by simulations.