Spectral and Energy Efficiencies in Full-Duplex Wireless Information and Power Transfer

Van‐Dinh Nguyen, Trung Q. Duong, Hoang Duong Tuan, Oh‐Soon Shin, H. Vincent Poor

IEEE Transactions on Communications · 2017 · 138 citations · 39 references

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TL;DR

The study examines a full‑duplex multi‑antenna base station serving downlink users and uplink users that harvest energy, noting that the non‑concave utility functions and non‑convex constraints make the optimization computationally challenging. The authors aim to maximize sum rate and energy efficiency while meeting uplink throughput constraints by jointly optimizing beamforming and time allocation. They employ a two‑phase full‑duplex protocol with simultaneous information and energy beamforming, and use path‑following algorithms that iteratively optimize beamforming and time allocation with guaranteed convergence. Simulations show the algorithms converge rapidly and outperform conventional solutions.

Abstract

A communication system is considered consisting of a full-duplex multiple-antenna base station (BS) and multiple single-antenna downlink users (DLUs) and single-antenna uplink users (ULUs), where the latter need to harvest energy for transmitting information to the BS. The communication is thus divided into two phases. In the first phase, the BS uses all available antennas for conveying information to DLUs and wireless energy to ULUs via information and energy beamforming, respectively. In the second phase, ULUs send their independent information to the BS using their harvested energy while the BS transmits the information to the DLUs. In both the phases, the communication is operated at the same time and over the same frequency band. The aim is to maximize the sum rate and energy efficiency under ULU achievable information throughput constraints by jointly optimizing beamforming and time allocation. The utility functions of interest are nonconcave and the involved constraints are nonconvex, so these problems are computationally troublesome. To address them, path-following algorithms are proposed to arrive at least at local optima. The proposed algorithms iteratively improve the objectives with convergence guaranteed. Simulation results demonstrate that they achieve rapid convergence and outperform conventional solutions.

References

39