2019/11/27 by Hang Ruan, H. Ruan, Ruan, H. +3
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Cooperative Communication and Network Coding #FOS: Computer and information sciences #FOS: Electrical engineering #Full-Duplex Wireless Communications #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #cs.IT #cs.LG #eess.SP #electronic engineering #information engineering #math.IT #stat.ML
paper · pdf · doi:10.48550/arxiv.1912.01506
14 pages, 9 figures. arXiv admin note: text overlap with arXiv:1712.01115
arxiv created 2019/11/27 · openalex publication_date 2019/11/27 · arxiv updated 2019/12/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques. The relay nodes are equipped with an amplify-and-forward (AF) protocol and the channel errors are modeled using an additive matrix perturbation, which results in degradation of the system performance. The proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB), considers a total relay transmit power constraint in the system and the goal of maximizing the output signal-to-interference-plus-noise ratio (SINR). We carry out a performance analysis of the proposed LRCC-RDB technique along with a computational complexity study. The proposed LRCC-RDB does not require any costly online optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.