Full-Duplex Wireless-Powered Communication Network With Energy Causality

TLDR

The study addresses sum‑throughput maximization and total‑time minimization in a full‑duplex wireless‑powered communication network with energy‑causality constraints. A hybrid access point with two antennas broadcasts energy while simultaneously receiving uplink data via TDMA; users harvest energy continuously subject to causality, and the authors derive closed‑form STM solutions, a two‑step optimal TTM algorithm, and low‑complexity suboptimal schemes. Simulations indicate that distinct scheduling strategies are required for the STM and TTM problems.

Abstract

In this paper, we consider a wireless communication network with a full-duplex hybrid energy and information access point and a set of wireless users with energy harvesting capabilities. The hybrid access point (HAP) implements full-duplex through two antennas: one for broadcasting wireless energy to users in the downlink and the other for simultaneously receiving information from the users via time division multiple access (TDMA) in the uplink. Each user can continuously harvest wireless power from the HAP until it transmits, i.e., the energy causality constraint is modeled by assuming that energy harvested in the future cannot be used for the current transmission. This leads to the causal dependence of each user's harvesting time on the transmission time of earlier users, e.g., the second user scheduled to transmit can harvest more energy if the first user has longer transmission time. Under this setup, we investigate the sum-throughput maximization (STM) problem and the total-time minimization (TTM) problem for the proposed full-duplex wireless-powered communication network. For the STM problem, the optimal solution is obtained as a closed-form expression, which can be computed with linear complexity. For the TTM problem, by exploiting the properties of the coupled constraints, we propose a two-step algorithm to obtain an optimal solution. Then, low-complexity suboptimal solutions are proposed for each problem by exploiting the characteristics of the optimal solutions. Finally, simulation studies on the effect of user scheduling show that different scheduling strategies should be adopted for STM and TTM.