{"title":"Wideband Beamforming With RIS: A Unified Framework via Space-Frequency Transformation","authors":"Xiaowei Qian;Xiaoling Hu;Chenxi Liu;Mugen Peng","doi":"10.1109/TSP.2024.3515102","DOIUrl":null,"url":null,"abstract":"The spectrum shift from sub-6G bands to high-frequency bands has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is particularly challenging in reconfigurable intelligent surface (RIS)-assisted systems, due to that the RIS is not capable of performing frequency-dependent phase shift, therefore inducing high signal processing complexity. In this paper, we propose a simple-yet-efficient wideband beamforming design for RIS-assisted systems, in which a transmitter sends wideband signals to a desired target with the aid of the RIS. In the proposed design, we exploit space-frequency Fourier transformation and stationary phase method to derive an approximate closed-form solution of RIS phase shifts, which significantly reduces the signal processing complexity compared to existing approaches. The obtained solution is then used to generate a large and flat beampattern over the desired frequency band. Through numerical results, we validate the effectiveness of our proposed beamforming design and demonstrate how it can improve system performance in terms of communication rate and sensing resolution. Beyond generating the flat beampattern, we highlight that our proposed design is capable of mimicking any desired beampattern by matching the RIS phase shift with the amplitude modulation function, thus providing valuable insights into the design of novel wideband beamforming for RIS-assisted systems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"173-187"},"PeriodicalIF":4.6000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10791862/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The spectrum shift from sub-6G bands to high-frequency bands has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is particularly challenging in reconfigurable intelligent surface (RIS)-assisted systems, due to that the RIS is not capable of performing frequency-dependent phase shift, therefore inducing high signal processing complexity. In this paper, we propose a simple-yet-efficient wideband beamforming design for RIS-assisted systems, in which a transmitter sends wideband signals to a desired target with the aid of the RIS. In the proposed design, we exploit space-frequency Fourier transformation and stationary phase method to derive an approximate closed-form solution of RIS phase shifts, which significantly reduces the signal processing complexity compared to existing approaches. The obtained solution is then used to generate a large and flat beampattern over the desired frequency band. Through numerical results, we validate the effectiveness of our proposed beamforming design and demonstrate how it can improve system performance in terms of communication rate and sensing resolution. Beyond generating the flat beampattern, we highlight that our proposed design is capable of mimicking any desired beampattern by matching the RIS phase shift with the amplitude modulation function, thus providing valuable insights into the design of novel wideband beamforming for RIS-assisted systems.
期刊介绍:
The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.