Joint Beamforming and Antenna Position Optimization for Movable Antenna-Assisted Spectrum Sharing

• Published in: IEEE wireless communications letters Vol. 13; no. 9; pp. 2502 - 2506
• Main Authors: Wei, Xin, Mei, Weidong, Wang, Dong, Ning, Boyu, Chen, Zhi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; alternating optimization; Antennas; Array signal processing; Beamforming; Cochannel interference; Cognitive radio; Communications systems; Fluid antennas; Interchannel interference; interference mitigation; movable antennas; Optimization; Receiving antennas; spectrum sharing; Transmitting antennas; Vectors; Wireless communications
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2024.3421636

Fluid antennas (FAs) and movable antennas (MAs) have drawn increasing attention in wireless communications recently due to their ability to create favorable channel conditions via local antenna movement within a confined region. In this letter, we advance their application for cognitive radio to facilitate efficient spectrum sharing between primary and secondary communication systems. In particular, we aim to jointly optimize the transmit beamforming and MA positions at a secondary transmitter (ST) to maximize the received signal power at a secondary receiver (SR) subject to the constraints on its imposed co-channel interference power with multiple primary receivers (PRs). However, such an optimization problem is difficult to be optimally solved due to the highly nonlinear functions of the received signal/interference power at the SR/all PRs in terms of the MA positions. To drive useful insights, we first perform theoretical analyses to unveil MAs' capability to achieve maximum-ratio transmission with the SR and effective interference mitigation for all PRs at the same time. To solve the MA position optimization problem, we propose an alternating optimization (AO) algorithm to obtain a high-quality suboptimal solution. Numerical results demonstrate that our proposed algorithms can significantly outperform the conventional fixed-position antennas (FPAs) and other baseline schemes.