{"title":"CBAM-VAE based CSI feedback for NR 5G compliant system","authors":"Anusaya Swain , Shrishail M. Hiremath , Sarat Kumar Patra , Shivashankar Hiremath","doi":"10.1016/j.phycom.2025.102816","DOIUrl":null,"url":null,"abstract":"<div><div>The promising performance gains of massive multiple-input and multiple-output (M-MIMO) rely on the accurate downlink channel state information (CSI) at the base station (BS). In the case of frequency division duplex (FDD) systems, the user equipment (UE) has to feed the estimated downlink CSI matrix to the BS precisely due to the absence of the principle of reciprocity. However, M-MIMO systems have a large number of antennas which leads to a significant amount of CSI data. Sending all this data back to the BS creates a bottleneck, consuming a large portion of the limited bandwidth resources available. In this paper, CBAM-VAE, a novel deep learning (DL) framework that complies with the 3GPP specifications is proposed to effectively analyze the objective of CSI feedback. The model is designed to incorporate the key features of the convolutional block attention module (CBAM) integrated with the variational autoencoder (VAE) hence, termed CBAM-VAE. The experimental outcomes show the superior performance of the designed architecture in comparison to the baseline networks using cosine similarity (<span><math><mi>ρ</mi></math></span>) and normalized mean square error (NMSE) as the key performance indicators for four distinct lengths of codeword <span><math><mrow><mo>(</mo><msub><mrow><mi>N</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>)</mo></mrow></math></span>. In addition, CBAM-VAE also has less computational overhead making it acceptable for real-time scenarios.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102816"},"PeriodicalIF":2.2000,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Communication","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874490725002198","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The promising performance gains of massive multiple-input and multiple-output (M-MIMO) rely on the accurate downlink channel state information (CSI) at the base station (BS). In the case of frequency division duplex (FDD) systems, the user equipment (UE) has to feed the estimated downlink CSI matrix to the BS precisely due to the absence of the principle of reciprocity. However, M-MIMO systems have a large number of antennas which leads to a significant amount of CSI data. Sending all this data back to the BS creates a bottleneck, consuming a large portion of the limited bandwidth resources available. In this paper, CBAM-VAE, a novel deep learning (DL) framework that complies with the 3GPP specifications is proposed to effectively analyze the objective of CSI feedback. The model is designed to incorporate the key features of the convolutional block attention module (CBAM) integrated with the variational autoencoder (VAE) hence, termed CBAM-VAE. The experimental outcomes show the superior performance of the designed architecture in comparison to the baseline networks using cosine similarity () and normalized mean square error (NMSE) as the key performance indicators for four distinct lengths of codeword . In addition, CBAM-VAE also has less computational overhead making it acceptable for real-time scenarios.
期刊介绍:
PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published.
Topics of interest include but are not limited to:
Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.