Resource Allocation for MIMO Full-Duplex Backscatter Assisted Wireless-Powered Communication Network With Finite Alphabet Inputs

Abstract

With practical finite alphabet inputs, usually the throughput of a practical communication system cannot reach the capacity based on assumption of Gaussian inputs. In this paper, we study the resource allocation strategy for multiple-input multiple-output (MIMO) full-duplex backscatter assisted wireless-powered communication network (FD-BAWPCN) with finite alphabet inputs, to maximize the sum-throughput. Firstly, we propose a gradient-based resource allocation strategy (GBRA), which alternately optimizes the precoder and time allocation based on the two-block alternating direction method of multipliers (ADMM). Secondly, to reduce the computational complexity and improve the feasibility of the strategy in practical applications, we further propose a codebook-based resource allocation strategy (CBRA), which can run more than three orders of magnitude faster than GBRA at the expense of a small sum-rate degradation. Then, the performance of the full-duplex (FD) and half-duplex (HD) system with or without backscatter assistance using GBRA are compared and analyzed. Numerical results demonstrate that the GBRA strategy has great robustness under various conditions but suffers a high computational complexity, and the CBRA strategy is effective and converges speedily.