{"title":"基于干扰对准的MIMO雷达和通信系统频谱共享","authors":"Yuanhao Cui, V. Koivunen, Xiaojun Jing","doi":"10.1109/SPAWC.2018.8445973","DOIUrl":null,"url":null,"abstract":"When Multi-Input Multi-Output (MIMO) radar and communication transceivers are co-existing and operating simultaneously in the same frequency band, interference can be managed by designing signal spaces that facilitate spectrum sharing. In this paper, a spatial precoder-decoder design based on Interference Alignment (IA) is proposed assuming that an interference channel is shared by Kc communication users and Kr radar users. In order to find an IA based precoder-decoder solution guaranteeing desired multiplexing gain or diversity order, the design problem is formulated using a rank minimization criterion with rank constraint for the communication subsystem and rank minimization criterion with l0 norm constraint for the radar, respectively. To deal with the non-convex nature of the optimization problem, we relax the problem by using nuclear norm. A generalized likelihood ratio test for target detection and a maximum likelihood estimator for target direction parameter are derived. The performance of the proposed IA based precoder-decoder design is analyzed using the GLRT detector and the ML estimator for MIMO radar. Analytical and simulation results show that radar detection performance is similar to the interference-free case with desired diversity order if the decoder matrix is semi-unitary and IA is perfect.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Interference Alignment Based Spectrum Sharing for MIMO Radar and Communication Systems\",\"authors\":\"Yuanhao Cui, V. Koivunen, Xiaojun Jing\",\"doi\":\"10.1109/SPAWC.2018.8445973\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When Multi-Input Multi-Output (MIMO) radar and communication transceivers are co-existing and operating simultaneously in the same frequency band, interference can be managed by designing signal spaces that facilitate spectrum sharing. In this paper, a spatial precoder-decoder design based on Interference Alignment (IA) is proposed assuming that an interference channel is shared by Kc communication users and Kr radar users. In order to find an IA based precoder-decoder solution guaranteeing desired multiplexing gain or diversity order, the design problem is formulated using a rank minimization criterion with rank constraint for the communication subsystem and rank minimization criterion with l0 norm constraint for the radar, respectively. To deal with the non-convex nature of the optimization problem, we relax the problem by using nuclear norm. A generalized likelihood ratio test for target detection and a maximum likelihood estimator for target direction parameter are derived. The performance of the proposed IA based precoder-decoder design is analyzed using the GLRT detector and the ML estimator for MIMO radar. Analytical and simulation results show that radar detection performance is similar to the interference-free case with desired diversity order if the decoder matrix is semi-unitary and IA is perfect.\",\"PeriodicalId\":240036,\"journal\":{\"name\":\"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPAWC.2018.8445973\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPAWC.2018.8445973","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interference Alignment Based Spectrum Sharing for MIMO Radar and Communication Systems
When Multi-Input Multi-Output (MIMO) radar and communication transceivers are co-existing and operating simultaneously in the same frequency band, interference can be managed by designing signal spaces that facilitate spectrum sharing. In this paper, a spatial precoder-decoder design based on Interference Alignment (IA) is proposed assuming that an interference channel is shared by Kc communication users and Kr radar users. In order to find an IA based precoder-decoder solution guaranteeing desired multiplexing gain or diversity order, the design problem is formulated using a rank minimization criterion with rank constraint for the communication subsystem and rank minimization criterion with l0 norm constraint for the radar, respectively. To deal with the non-convex nature of the optimization problem, we relax the problem by using nuclear norm. A generalized likelihood ratio test for target detection and a maximum likelihood estimator for target direction parameter are derived. The performance of the proposed IA based precoder-decoder design is analyzed using the GLRT detector and the ML estimator for MIMO radar. Analytical and simulation results show that radar detection performance is similar to the interference-free case with desired diversity order if the decoder matrix is semi-unitary and IA is perfect.