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论文类型：期刊论文

第一作者：Li, Xuanheng

通讯作者：Li, XH (reprint author), Dalian Univ Technol, Sch Informat & Commun Engn, Dalian 116024, Peoples R China.

合写作者：Zhao, Nan,Sun, Yi,Yu, F. Richard

发表时间：2016-07-01

发表刊物：IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

收录刊物：SCIE、EI、ESI高被引论文

卷号：65

期号：7

页面范围：5497-5511

ISSN号：0018-9545

关键字：Antenna selection (AS); cognitive radio (CR); discrete stochastic optimization (DSO); imperfect channel state information (CSI); interference alignment (IA); time-varying channel

摘要：Interference alignment (IA) is a promising technique that can eliminate interference in wireless networks effectively and has been applied to spectrum sharing in cognitive radio (CR) networks. However, most existing IA schemes neglect the quality of the desired signal, which may lead to poor performance, particularly at poor channel status. In this paper, we analyze the problem of the decrease in the signal-to-interference-plus-noise ratio (SINR) of the desired signal and propose a novel IA scheme based on antenna selection (AS) to improve the received SINR of each user in IA-based CR networks. In the proposed scheme, multiple antennas are equipped at each secondary receiver, and some of them are chosen to achieve optimal performance. Furthermore, the condition of imperfect channel state information (CSI) is also considered, which can impact the performance of IA-AS. To face this problem, a scheme called CSI filtering is proposed to weaken the influence of the imperfect CSI. Moreover, considering the considerable computational complexity brought by the selection among mass of antenna combinations, an efficient IA-AS algorithm based on discrete stochastic optimization (DSO) is thus proposed, which can converge quickly to the optimum with low computational complexity. To further improve the tracking performance of the algorithm under a time-varying channel environment, we propose an adaptive DSO scheme with window CSI filtering for IA-AS to give the algorithm a good tracking capability. Simulation results are presented to show that the proposed schemes can significantly improve the performance of IA-based CR networks.