In spectrum sharing networks, a base station (BS) needs to mitigate the interference to users associated with other coexisting network in the same band. The BS can achieve this by transmitting its downlink signal in the null space of channels to such users. However, under a wideband scenario, the BS needs to estimate null space matrices using the received signal from such non-cooperative users in each frequency bin where the users are active. To reduce the computational complexity of this operation, we propose a frequency clustering algorithm that exploits the channel correlations among adjacent frequency bins. The proposed algorithm forms clusters of frequency bins with correlated channel vectors without prior knowledge of the channels and obtains a single null space matrix for each cluster. We show that the number of matrices and the number of eigenvalue decompositions required to obtain the null space significantly reduce using the proposed clustering algorithm.
A Self-Calibrating SDR for High Fidelity Beam- and Null-forming Arrays
KAIST School of Electrical Engineering（韩国科学技术院电气工程学院）；Pi-Radio；NYU Tandon School of Engineering（纽约大学坦登工程学院）；Old Dominion University Department of Electrical and Computer Engineering（老道明大学电气与计算机工程系）；NAVSEA Crane Division（美国海军海上系统司令部克兰分部）
Deep Reinforcement Learning for Interference Suppression in RIS-Aided Space-Air-Ground Integrated Networks
Department of Artificial Intelligence, Indian Institute of Technology Hyderabad, Telangana, India（印度海得拉巴印度理工学院人工智能系）；School of Informatics, Kami, Kochi University of Technology, Kochi, Japan（日本科技大学信息学院）；Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan（东北大学信息科学研究生院）
