Long-term scheduling and power control for wirelessly powered cell-free IoT

TL;DR

This paper proposes a long-term scheduling and power control scheme for wirelessly powered cell-free IoT networks, optimizing sensor data rates and energy levels through Lyapunov optimization and asymptotic analysis.

Contribution

It introduces a novel long-term optimization framework for power control and scheduling in wirelessly powered IoT, with closed-form energy and rate expressions.

Findings

Closed-form expressions accurately predict energy and rate.

Proposed scheme significantly improves minimum average data rate.

Simulation confirms effectiveness over greedy schemes.

Abstract

We investigate the long-term scheduling and power control scheme for a wirelessly powered cell-free Internet of Things (IoT) network which consists of distributed access points (APs) and large number of sensors. In each time slot, a subset of sensors are scheduled for uplink data transmission or downlink power transfer. Through asymptotic analysis, we obtain closedform expressions for the harvested energy and the achievable rates that are independent of random pilots. Then, using these expressions, we formulate a long-term scheduling and power control problem to maximize the minimum time average achievable rate among all sensors, while maintaining the battery state of each sensor higher than a predefined minimum level. Using Lyapunov optimization, the transmission mode, the active sensor set, and the power control coefficients for each time slot are jointly determined. Finally, simulation results validate the accuracy of our derived closed-form expressions and reveal that the minimum time average achievable rate is boosted significantly by the proposed scheme compare with the simple greedy transmission scheme.