{"title":"基于流形优化的纯相位零波束形成合成","authors":"Yang Cong, Jinfeng Hu, Kai Zhong, Jie Wu","doi":"10.1109/IGARSS46834.2022.9883605","DOIUrl":null,"url":null,"abstract":"The phase-only beamforming synthesis is widely applied in millimeter wave communication, radar and sonar. Due to the CMC, the problem is non-convex. The most current methods solve the problem by designing the phase, which either degrades the performance or needs huge complexity. To address this issue, a low-complexity Riemannian Manifold Optimization based Conjugate Gradient (RMOCG) method is proposed. First, the original problem is transformed into an unconstrained prob-lem on a complex circle manifold. Then, a RMOCG algorithm is derived, by deriving the gradient descent direction and the step size for ensuring the cost function non-increasing. Comparing with the existing methods, the proposed method has the following advantages: 1) the null depth is respectively 8 dB deeper than [6] and 3 dB deeper than [12]. 2) The computational cost is 2 magnitude lower than [6] and 1 magnitude lower than [12].","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The phase-only null beamforming synthesis via manifold optimization\",\"authors\":\"Yang Cong, Jinfeng Hu, Kai Zhong, Jie Wu\",\"doi\":\"10.1109/IGARSS46834.2022.9883605\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The phase-only beamforming synthesis is widely applied in millimeter wave communication, radar and sonar. Due to the CMC, the problem is non-convex. The most current methods solve the problem by designing the phase, which either degrades the performance or needs huge complexity. To address this issue, a low-complexity Riemannian Manifold Optimization based Conjugate Gradient (RMOCG) method is proposed. First, the original problem is transformed into an unconstrained prob-lem on a complex circle manifold. Then, a RMOCG algorithm is derived, by deriving the gradient descent direction and the step size for ensuring the cost function non-increasing. Comparing with the existing methods, the proposed method has the following advantages: 1) the null depth is respectively 8 dB deeper than [6] and 3 dB deeper than [12]. 2) The computational cost is 2 magnitude lower than [6] and 1 magnitude lower than [12].\",\"PeriodicalId\":426003,\"journal\":{\"name\":\"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS46834.2022.9883605\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS46834.2022.9883605","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The phase-only null beamforming synthesis via manifold optimization
The phase-only beamforming synthesis is widely applied in millimeter wave communication, radar and sonar. Due to the CMC, the problem is non-convex. The most current methods solve the problem by designing the phase, which either degrades the performance or needs huge complexity. To address this issue, a low-complexity Riemannian Manifold Optimization based Conjugate Gradient (RMOCG) method is proposed. First, the original problem is transformed into an unconstrained prob-lem on a complex circle manifold. Then, a RMOCG algorithm is derived, by deriving the gradient descent direction and the step size for ensuring the cost function non-increasing. Comparing with the existing methods, the proposed method has the following advantages: 1) the null depth is respectively 8 dB deeper than [6] and 3 dB deeper than [12]. 2) The computational cost is 2 magnitude lower than [6] and 1 magnitude lower than [12].