Pub Date : 2025-12-15DOI: 10.1109/lwc.2025.3644268
Hala Mostafa, Mohamed Marey
{"title":"Iterative Delay-Aided Decoding and Estimation for BICM-ID Over AF Full-Duplex Cooperative Transmissions","authors":"Hala Mostafa, Mohamed Marey","doi":"10.1109/lwc.2025.3644268","DOIUrl":"https://doi.org/10.1109/lwc.2025.3644268","url":null,"abstract":"","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"35 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1109/lwc.2025.3644478
Huanzhi Wang, Can Wang, Youyun Xu
{"title":"Two-Phase Optimization for NOMA-Assisted Downlink Pinching-Antenna Systems Under QoS Guarantee","authors":"Huanzhi Wang, Can Wang, Youyun Xu","doi":"10.1109/lwc.2025.3644478","DOIUrl":"https://doi.org/10.1109/lwc.2025.3644478","url":null,"abstract":"","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"77 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1109/LWC.2025.3644690
Dukai Xiang;Songjie Yang;Xinyang Li;Hua Chen
This letter investigates the effect of movable antennas on improving spectral efficiency in multi-subarray beam training of hybrid beamforming systems, forming movable subarrays (MSAs). During beam training, a joint search for both position and beam is conducted, but this increases the codebook dimension and search complexity. To solve this, we propose two low-complexity algorithms. First, an iterative one-sided search (IOSS) is developed to jointly optimize the configuration for each subarray, achieving rapid convergence. To further reduce training overhead, we also introduce a hierarchical angle-position search (HAPS), which offers performance comparable to IOSS with even lower complexity. Numerical results demonstrate that MSAs achieve gains of 39% over traditional fixed subarrays (FSAs) with exhaustive search. Our proposed IOSS algorithm secures a 42% gain, while HAPS achieves a comparable gain of 33%. These findings highlight the significant potential of MSAs.
{"title":"Multi-Beam Training for Movable Antenna Enhanced Wireless Communications","authors":"Dukai Xiang;Songjie Yang;Xinyang Li;Hua Chen","doi":"10.1109/LWC.2025.3644690","DOIUrl":"10.1109/LWC.2025.3644690","url":null,"abstract":"This letter investigates the effect of movable antennas on improving spectral efficiency in multi-subarray beam training of hybrid beamforming systems, forming movable subarrays (MSAs). During beam training, a joint search for both position and beam is conducted, but this increases the codebook dimension and search complexity. To solve this, we propose two low-complexity algorithms. First, an iterative one-sided search (IOSS) is developed to jointly optimize the configuration for each subarray, achieving rapid convergence. To further reduce training overhead, we also introduce a hierarchical angle-position search (HAPS), which offers performance comparable to IOSS with even lower complexity. Numerical results demonstrate that MSAs achieve gains of 39% over traditional fixed subarrays (FSAs) with exhaustive search. Our proposed IOSS algorithm secures a 42% gain, while HAPS achieves a comparable gain of 33%. These findings highlight the significant potential of MSAs.","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"15 ","pages":"1020-1024"},"PeriodicalIF":5.5,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1109/LWC.2025.3644153
Junyong Shin;Jihun Park;Jinsung Park;Yo-Seb Jeon
This letter presents a conditional entropy-constrained multi-stage vector quantization (CEC-MSVQ) framework for semantic communication (SC). The proposed method integrates multi-stage VQ (MSVQ) for fine-grained rate control and entropy-constrained VQ (ECVQ) for improved rate–distortion efficiency. By modeling stage-wise quantization outputs as a Markovian sequence with conditional entropy modeling, CEC-MSVQ jointly trains semantic encoder–decoder networks and VQ codebooks while explicitly optimizing the rate–distortion objective. During inference, multi-rate transmission is supported by selectively activating VQ modules. Simulation results show that CEC-MSVQ achieves superior task performance over existing VQ-based SC, confirming the effectiveness of the proposed conditional entropy modeling.
{"title":"Conditional Entropy-Constrained Multi-Stage Vector Quantization for Semantic Communication","authors":"Junyong Shin;Jihun Park;Jinsung Park;Yo-Seb Jeon","doi":"10.1109/LWC.2025.3644153","DOIUrl":"10.1109/LWC.2025.3644153","url":null,"abstract":"This letter presents a conditional entropy-constrained multi-stage vector quantization (CEC-MSVQ) framework for semantic communication (SC). The proposed method integrates multi-stage VQ (MSVQ) for fine-grained rate control and entropy-constrained VQ (ECVQ) for improved rate–distortion efficiency. By modeling stage-wise quantization outputs as a Markovian sequence with conditional entropy modeling, CEC-MSVQ jointly trains semantic encoder–decoder networks and VQ codebooks while explicitly optimizing the rate–distortion objective. During inference, multi-rate transmission is supported by selectively activating VQ modules. Simulation results show that CEC-MSVQ achieves superior task performance over existing VQ-based SC, confirming the effectiveness of the proposed conditional entropy modeling.","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"15 ","pages":"885-889"},"PeriodicalIF":5.5,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 10.1109/LWC.2025.3643501
Shachar Shayovitz;Doron Ezri;Yoav Levinbook
MIMO systems enable simultaneous multi-stream transmission but make detection challenging under interference and noise. Building on Expectation Propagation (EP), we propose Gaussian Mixture EP (GMEP), which curbs EP’s reliance on multiple iterations caused by negative-variance updates. GMEP replaces only unstable symbol-wise Gaussian messages with a small, bounded-order mixture, so most messages remain single-Gaussian and per-iteration cost stays close to MMSE/EP. This stabilizes detection early, avoids repeated matrix inversions, and achieves EP-level accuracy with fewer iterations and lower overall complexity. Benefits grow with antenna count and modulation order, making GMEP especially effective for large-scale MIMO. Results on large arrays and high-order constellations confirm consistent accuracy gains under tight iteration budgets with minimal overhead.
{"title":"MIMO Detection via Gaussian Mixture Expectation Propagation","authors":"Shachar Shayovitz;Doron Ezri;Yoav Levinbook","doi":"10.1109/LWC.2025.3643501","DOIUrl":"10.1109/LWC.2025.3643501","url":null,"abstract":"MIMO systems enable simultaneous multi-stream transmission but make detection challenging under interference and noise. Building on Expectation Propagation (EP), we propose Gaussian Mixture EP (GMEP), which curbs EP’s reliance on multiple iterations caused by negative-variance updates. GMEP replaces only unstable symbol-wise Gaussian messages with a small, bounded-order mixture, so most messages remain single-Gaussian and per-iteration cost stays close to MMSE/EP. This stabilizes detection early, avoids repeated matrix inversions, and achieves EP-level accuracy with fewer iterations and lower overall complexity. Benefits grow with antenna count and modulation order, making GMEP especially effective for large-scale MIMO. Results on large arrays and high-order constellations confirm consistent accuracy gains under tight iteration budgets with minimal overhead.","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"15 ","pages":"980-984"},"PeriodicalIF":5.5,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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