Pub Date : 2025-12-22DOI: 10.1109/TWC.2025.3643833
Xianling Wang;Zihang Zhang;Yousi Lin;Yue Tian;Kyeong Jin Kim;Yuanwei Liu
In fog radio access networks (F-RANs), caching popular content at edge fog access points (FAPs) helps alleviate the burden on base stations and backhaul links. To improve signal quality and connectivity, this work integrates cooperative communication and non-orthogonal multiple access (NOMA) into content-centric F-RANs with unreliable backhauls. Specifically, a NOMA-enabled joint transmission scheme is considered, where cache-enabled FAPs are coordinated into clusters to perform non-coherent joint transmission and NOMA, enabling multiplexing signals for multiple users. Under a hybrid caching policy and non-uniform Nakagami-$m$ fading channels, the system performance is analyzed in terms of the successful content delivery probability and outage achievable rate. To optimize FAP coordination, the clustering problem is formulated as a coalitional game, and a low-complexity transfer-based clustering algorithm is designed. Furthermore, a hierarchical hybrid NOMA-based algorithm is developed to enhance multi-user access efficiency. Simulation results demonstrate that: 1) The NOMA-enabled joint transmission scheme allows the FAPs efficiently leverage the cached content to mitigate backhaul unreliability; 2) The NOMA-based design outperforms the orthogonal multiple access-based design by guaranteeing improved signal quality while maintaining the efficiency of multi-user access; 3) The coalitional game-based clustering algorithm effectively manages co-channel interference and improves spectrum utilization.
{"title":"Clustered Joint Transmission for NOMA-Enabled Content-Centric Fog Radio Access Networks","authors":"Xianling Wang;Zihang Zhang;Yousi Lin;Yue Tian;Kyeong Jin Kim;Yuanwei Liu","doi":"10.1109/TWC.2025.3643833","DOIUrl":"10.1109/TWC.2025.3643833","url":null,"abstract":"In fog radio access networks (F-RANs), caching popular content at edge fog access points (FAPs) helps alleviate the burden on base stations and backhaul links. To improve signal quality and connectivity, this work integrates cooperative communication and non-orthogonal multiple access (NOMA) into content-centric F-RANs with unreliable backhauls. Specifically, a NOMA-enabled joint transmission scheme is considered, where cache-enabled FAPs are coordinated into clusters to perform non-coherent joint transmission and NOMA, enabling multiplexing signals for multiple users. Under a hybrid caching policy and non-uniform Nakagami-<inline-formula> <tex-math>$m$ </tex-math></inline-formula> fading channels, the system performance is analyzed in terms of the successful content delivery probability and outage achievable rate. To optimize FAP coordination, the clustering problem is formulated as a coalitional game, and a low-complexity transfer-based clustering algorithm is designed. Furthermore, a hierarchical hybrid NOMA-based algorithm is developed to enhance multi-user access efficiency. Simulation results demonstrate that: 1) The NOMA-enabled joint transmission scheme allows the FAPs efficiently leverage the cached content to mitigate backhaul unreliability; 2) The NOMA-based design outperforms the orthogonal multiple access-based design by guaranteeing improved signal quality while maintaining the efficiency of multi-user access; 3) The coalitional game-based clustering algorithm effectively manages co-channel interference and improves spectrum utilization.","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"25 ","pages":"9731-9746"},"PeriodicalIF":10.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-12-22DOI: 10.1109/TWC.2025.3643647
Changhao Liu;Weidong Mei;Peilan Wang;Yinuo Meng;Zhi Chen;Boyu Ning
Intelligent reflecting surface (IRS) is composed of numerous passive reflecting elements and can be mounted on uncrewed aerial vehicles (UAVs) to achieve six-dimensional (6D) movement by adjusting the UAV’s three-dimensional (3D) location and 3D orientation simultaneously. Hence, in this paper, we investigate a new UAV-enabled passive 6D movable antenna (6DMA) architecture by mounting an IRS on a UAV and address the associated joint deployment and beamforming optimization problem. In particular, we consider a passive 6DMA-aided multicast system with a multi-antenna base station (BS) and multiple remote users, aiming to jointly optimize the IRS’s location and 3D orientation, as well as its passive beamforming to maximize the minimum received signal-to-noise ratio (SNR) among all users under the practical angle-dependent signal reflection model. However, this optimization problem is challenging to be optimally solved due to the intricate relationship between the users’ SNRs and the IRS’s location and orientation. To tackle this challenge, we first focus on a simplified case with a single user, showing that one-dimensional (1D) orientation suffices to achieve the optimal performance. Next, we show that for any given IRS’s location, the optimal 1D orientation can be derived in closed form, based on which several useful insights are drawn. To solve the max-min SNR problem in the general multi-user case, we propose an alternating optimization (AO) algorithm by alternately optimizing the IRS’s beamforming and location/orientation via successive convex approximation (SCA) and hybrid coarse- and fine-grained search, respectively. To avoid undesirable local sub-optimal solutions, a Gibbs sampling (GS) method is proposed to generate new IRS locations and orientations for exploration in each AO iteration. Numerical results validate our theoretical analyses and demonstrate the superiority of our proposed AO algorithm with GS to conventional AO and other baseline deployment strategies with location or orientation optimization only.
{"title":"UAV-Enabled Passive 6D Movable Antennas: Joint Deployment and Beamforming Optimization","authors":"Changhao Liu;Weidong Mei;Peilan Wang;Yinuo Meng;Zhi Chen;Boyu Ning","doi":"10.1109/TWC.2025.3643647","DOIUrl":"10.1109/TWC.2025.3643647","url":null,"abstract":"Intelligent reflecting surface (IRS) is composed of numerous passive reflecting elements and can be mounted on uncrewed aerial vehicles (UAVs) to achieve six-dimensional (6D) movement by adjusting the UAV’s three-dimensional (3D) location and 3D orientation simultaneously. Hence, in this paper, we investigate a new UAV-enabled passive 6D movable antenna (6DMA) architecture by mounting an IRS on a UAV and address the associated joint deployment and beamforming optimization problem. In particular, we consider a passive 6DMA-aided multicast system with a multi-antenna base station (BS) and multiple remote users, aiming to jointly optimize the IRS’s location and 3D orientation, as well as its passive beamforming to maximize the minimum received signal-to-noise ratio (SNR) among all users under the practical angle-dependent signal reflection model. However, this optimization problem is challenging to be optimally solved due to the intricate relationship between the users’ SNRs and the IRS’s location and orientation. To tackle this challenge, we first focus on a simplified case with a single user, showing that one-dimensional (1D) orientation suffices to achieve the optimal performance. Next, we show that for any given IRS’s location, the optimal 1D orientation can be derived in closed form, based on which several useful insights are drawn. To solve the max-min SNR problem in the general multi-user case, we propose an alternating optimization (AO) algorithm by alternately optimizing the IRS’s beamforming and location/orientation via successive convex approximation (SCA) and hybrid coarse- and fine-grained search, respectively. To avoid undesirable local sub-optimal solutions, a Gibbs sampling (GS) method is proposed to generate new IRS locations and orientations for exploration in each AO iteration. Numerical results validate our theoretical analyses and demonstrate the superiority of our proposed AO algorithm with GS to conventional AO and other baseline deployment strategies with location or orientation optimization only.","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"25 ","pages":"9765-9781"},"PeriodicalIF":10.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-12-22DOI: 10.1109/twc.2025.3643892
Min Qiu, Ming-Chun Lee, Yu-Chih Huang, Jinhong Yuan
{"title":"Scaling Law Tradeoff Between Throughput and Sensing Distance in Large ISAC Networks","authors":"Min Qiu, Ming-Chun Lee, Yu-Chih Huang, Jinhong Yuan","doi":"10.1109/twc.2025.3643892","DOIUrl":"https://doi.org/10.1109/twc.2025.3643892","url":null,"abstract":"","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"21 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-12-19DOI: 10.1109/TWC.2025.3643786
Atchutaram K. Kocharlakota;Sergiy A. Vorobyov;Robert W. Heath
Learning-based downlink power control in cell-free massive multiple-input multiple-output (CFmMIMO) systems offers a promising alternative to conventional iterative optimization algorithms, which are computationally intensive due to online iterative steps. Existing learning-based methods, however, often fail to exploit the intrinsic structure of channel data and neglect pilot allocation information, leading to suboptimal performance, especially in large-scale networks with many users. This paper introduces the pilot contamination-aware power control (PAPC) transformer neural network, a novel approach that integrates pilot allocation data into the network, effectively handling pilot contamination scenarios. PAPC employs the attention mechanism with a custom masking technique to utilize structural information and pilot data. The architecture includes tailored preprocessing and post-processing stages for efficient feature extraction and adherence to power constraints. Trained in an unsupervised learning framework, PAPC is evaluated against the accelerated proximal gradient (APG) algorithm, showing comparable spectral efficiency fairness performance, while significantly improving computational efficiency. Simulations demonstrate PAPC’s superior performance over fully connected networks (FCNs) that lack pilot information, its scalability to large-scale CFmMIMO networks, and its computational efficiency improvement over APG. PAPC is further validated through ablation studies and evaluated across several representative CFmMIMO scenarios, demonstrating robustness to pilot contamination, scalability, and adaptability to varying user counts without retraining.
{"title":"Pilot Contamination Aware Transformer for Downlink Power Control in Cell-Free Massive MIMO Networks","authors":"Atchutaram K. Kocharlakota;Sergiy A. Vorobyov;Robert W. Heath","doi":"10.1109/TWC.2025.3643786","DOIUrl":"10.1109/TWC.2025.3643786","url":null,"abstract":"Learning-based downlink power control in cell-free massive multiple-input multiple-output (CFmMIMO) systems offers a promising alternative to conventional iterative optimization algorithms, which are computationally intensive due to online iterative steps. Existing learning-based methods, however, often fail to exploit the intrinsic structure of channel data and neglect pilot allocation information, leading to suboptimal performance, especially in large-scale networks with many users. This paper introduces the pilot contamination-aware power control (PAPC) transformer neural network, a novel approach that integrates pilot allocation data into the network, effectively handling pilot contamination scenarios. PAPC employs the attention mechanism with a custom masking technique to utilize structural information and pilot data. The architecture includes tailored preprocessing and post-processing stages for efficient feature extraction and adherence to power constraints. Trained in an unsupervised learning framework, PAPC is evaluated against the accelerated proximal gradient (APG) algorithm, showing comparable spectral efficiency fairness performance, while significantly improving computational efficiency. Simulations demonstrate PAPC’s superior performance over fully connected networks (FCNs) that lack pilot information, its scalability to large-scale CFmMIMO networks, and its computational efficiency improvement over APG. PAPC is further validated through ablation studies and evaluated across several representative CFmMIMO scenarios, demonstrating robustness to pilot contamination, scalability, and adaptability to varying user counts without retraining.","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"25 ","pages":"9656-9671"},"PeriodicalIF":10.7,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11306259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1109/twc.2025.3642630
Xiaowei Qian, Xiaoling Hu, Chenxi Liu
{"title":"RIS in Space: Modeling and Communication Performance Analysis","authors":"Xiaowei Qian, Xiaoling Hu, Chenxi Liu","doi":"10.1109/twc.2025.3642630","DOIUrl":"https://doi.org/10.1109/twc.2025.3642630","url":null,"abstract":"","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"9 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-12-17DOI: 10.1109/TWC.2025.3641575
Yang Fu;Peng Qin;Yueyue Zhang;Pao Cheng;Jun Lu;Yifei Wang
6G networks are envisioned to support on-demand AI model downloading to accommodate diverse inference requirements of end users. By proactively caching models at edge nodes, users can retrieve the requested models with low latency for on-device AI inference. However, the substantial size of contemporary AI models poses significant challenges for edge caching under limited storage capacity, as well as for the concurrent delivery of heterogeneous models over wireless channels. To address these challenges, we propose a fine-grained AI model caching and downloading system that exploits parameter reusability, stemming from the common practice of fine-tuning task-specific models from a shared pre-trained model with frozen parameters. This system selectively caches model parameter blocks (PBs) at edge nodes, eliminating redundant storage of reusable parameters across different cached models. Additionally, it incorporates coordinated multipoint (CoMP) broadcasting to simultaneously deliver reusable PBs to multiple users, thereby enhancing downlink spectrum utilization. Under this arrangement, we formulate a model downloading delay minimization problem to jointly optimize PB caching, migration (among edge nodes), and broadcasting beamforming. To tackle this intractable problem, we develop a distributed multi-agent learning framework that enables edge nodes to explicitly learn mutual influence among their actions, thereby facilitating cooperation. Furthermore, a data augmentation approach is proposed to adaptively generate synthetic training samples through a predictive model, boosting sample efficiency and accelerating policy learning. Both theoretical analysis and simulation experiments validate the superior convergence performance of the proposed learning framework. Moreover, experimental results demonstrate that our scheme significantly reduces model downloading delay compared to benchmark methods.
{"title":"Fine-Grained AI Model Caching and Downloading With Coordinated Multipoint Broadcasting in Multi-Cell Edge Networks","authors":"Yang Fu;Peng Qin;Yueyue Zhang;Pao Cheng;Jun Lu;Yifei Wang","doi":"10.1109/TWC.2025.3641575","DOIUrl":"10.1109/TWC.2025.3641575","url":null,"abstract":"6G networks are envisioned to support on-demand AI model downloading to accommodate diverse inference requirements of end users. By proactively caching models at edge nodes, users can retrieve the requested models with low latency for on-device AI inference. However, the substantial size of contemporary AI models poses significant challenges for edge caching under limited storage capacity, as well as for the concurrent delivery of heterogeneous models over wireless channels. To address these challenges, we propose a fine-grained AI model caching and downloading system that exploits parameter reusability, stemming from the common practice of fine-tuning task-specific models from a shared pre-trained model with frozen parameters. This system selectively caches model parameter blocks (PBs) at edge nodes, eliminating redundant storage of reusable parameters across different cached models. Additionally, it incorporates coordinated multipoint (CoMP) broadcasting to simultaneously deliver reusable PBs to multiple users, thereby enhancing downlink spectrum utilization. Under this arrangement, we formulate a model downloading delay minimization problem to jointly optimize PB caching, migration (among edge nodes), and broadcasting beamforming. To tackle this intractable problem, we develop a distributed multi-agent learning framework that enables edge nodes to explicitly learn mutual influence among their actions, thereby facilitating cooperation. Furthermore, a data augmentation approach is proposed to adaptively generate synthetic training samples through a predictive model, boosting sample efficiency and accelerating policy learning. Both theoretical analysis and simulation experiments validate the superior convergence performance of the proposed learning framework. Moreover, experimental results demonstrate that our scheme significantly reduces model downloading delay compared to benchmark methods.","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"25 ","pages":"9814-9829"},"PeriodicalIF":10.7,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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