Dynamic spectrum control is a viable anti-jamming scheme; however, frequency switching (FS) has relatively high time overhead in practice. Moreover, if the jamming variation cycle is shorter than that of the FS, it can cause communication degradation or even failure. Therefore, we employ both link layer FS and low-overhead physical layer actions for joint anti-jamming and establish distinct decision timescales based on these overheads. Specifically, we design distinct networks for different actions, utilizing cognitive information as rules to guide the agent optimization direction for resource deployment. Simulation results indicate that our method improves the throughput by 112.87% compared to the same timescale algorithm, which demonstrates the effectiveness of associating cross-layer actions at different timescales under highly dynamic interference conditions.
{"title":"A Multi-Timescale Cross-Layer Anti-Jamming Scheme Under Rule Guidance","authors":"Haoqin Zhao;Jiangbo Si;Zan Li;Xiaoting Wang;Naofal Al-Dhahir","doi":"10.1109/LCOMM.2024.3510878","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3510878","url":null,"abstract":"Dynamic spectrum control is a viable anti-jamming scheme; however, frequency switching (FS) has relatively high time overhead in practice. Moreover, if the jamming variation cycle is shorter than that of the FS, it can cause communication degradation or even failure. Therefore, we employ both link layer FS and low-overhead physical layer actions for joint anti-jamming and establish distinct decision timescales based on these overheads. Specifically, we design distinct networks for different actions, utilizing cognitive information as rules to guide the agent optimization direction for resource deployment. Simulation results indicate that our method improves the throughput by 112.87% compared to the same timescale algorithm, which demonstrates the effectiveness of associating cross-layer actions at different timescales under highly dynamic interference conditions.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 2","pages":"259-263"},"PeriodicalIF":3.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403854","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 : 2024-12-03DOI: 10.1109/LCOMM.2024.3510334
Qian Zhang;Mingjie Shao;Tong Zhang;Gaojie Chen;Ju Liu;P. C. Ching
This letter investigates a fluid antenna (FA)-assisted integrated sensing and communication (ISAC) system, with joint antenna position optimization and waveform design. We consider enhancing the sum-rate maximization (SRM) and sensing performance with the aid of FAs. Although the introduction of FAs brings more degrees of freedom for performance optimization, its position optimization poses a non-convex programming problem and brings great computational challenges. This letter contributes to building an efficient design algorithm by the block successive upper bound minimization and majorization-minimization principles, with each step admitting closed-form update for the ISAC waveform design. In addition, the extrapolation technique is exploited further to speed up the empirical convergence of FA position design. Simulation results show that the proposed design can achieve state-of-the-art sum-rate performance with at least 60% computation cutoff compared to existing works with successive convex approximation (SCA) and particle swarm optimization (PSO) algorithms.
{"title":"An Efficient Sum-Rate Maximization Algorithm for Fluid Antenna-Assisted ISAC System","authors":"Qian Zhang;Mingjie Shao;Tong Zhang;Gaojie Chen;Ju Liu;P. C. Ching","doi":"10.1109/LCOMM.2024.3510334","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3510334","url":null,"abstract":"This letter investigates a fluid antenna (FA)-assisted integrated sensing and communication (ISAC) system, with joint antenna position optimization and waveform design. We consider enhancing the sum-rate maximization (SRM) and sensing performance with the aid of FAs. Although the introduction of FAs brings more degrees of freedom for performance optimization, its position optimization poses a non-convex programming problem and brings great computational challenges. This letter contributes to building an efficient design algorithm by the block successive upper bound minimization and majorization-minimization principles, with each step admitting closed-form update for the ISAC waveform design. In addition, the extrapolation technique is exploited further to speed up the empirical convergence of FA position design. Simulation results show that the proposed design can achieve state-of-the-art sum-rate performance with at least 60% computation cutoff compared to existing works with successive convex approximation (SCA) and particle swarm optimization (PSO) algorithms.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 1","pages":"200-204"},"PeriodicalIF":3.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938359","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}
To ensure ultra-reliable, low-latency communications (URLLC) despite blockages, we propose using simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RISs). These surfaces can transmit and reflect signals, expanding coverage on both sides of the RIS. However, phase shifter adjustments in STAR-RIS are limited by coupling constraints. We introduce a codebook design for phase-shift adjustment that accounts for these constraints. Leveraging this design, we jointly optimize base station beamforming and STAR-RIS phase-shifting to maximize the number of admitted URLLC users in a downlink multi-user communication system. Numerical results show that the proposed STAR-RIS codebook surpasses existing designs and effectively admits more URLLC users.
{"title":"Efficient Codebook Design and User Scheduling for Large STAR-RIS-Aided Downlink URLLC Transmission","authors":"Mostafa Darabi;Ata Khalili;Lutz Lampe;Robert Schober","doi":"10.1109/LCOMM.2024.3510464","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3510464","url":null,"abstract":"To ensure ultra-reliable, low-latency communications (URLLC) despite blockages, we propose using simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RISs). These surfaces can transmit and reflect signals, expanding coverage on both sides of the RIS. However, phase shifter adjustments in STAR-RIS are limited by coupling constraints. We introduce a codebook design for phase-shift adjustment that accounts for these constraints. Leveraging this design, we jointly optimize base station beamforming and STAR-RIS phase-shifting to maximize the number of admitted URLLC users in a downlink multi-user communication system. Numerical results show that the proposed STAR-RIS codebook surpasses existing designs and effectively admits more URLLC users.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 1","pages":"205-209"},"PeriodicalIF":3.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938360","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}
We discuss the user localization problem assisted by reconfigurable intelligent surfaces (RIS) in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. By exploring the inherent sparse scattering characteristics of mmWave channels, we propose a tensor decomposition based method to obtain the channel parameters (i.e. angle, delay) required for user localization. Specifically, the received signal is modeled as a low-rank third-order tensor fitting a typical polyhedral model which contains a factor matrix of channel parameters. Then, we design a structured tensor decomposition method to estimate the channel parameters by using the Vandermonde property of factor matrices. Finally, based on the least squares criterion, the location of user are obtained from the estimated channel parameters. Simulation results show that the proposed estimation scheme can achieve a centimeter-level resolution of user localization.
{"title":"User Localization for Reconfigurable Intelligent Surface-Assisted mmWave MIMO-OFDM Systems","authors":"Menglei Sheng;Youming Li;Wanyuan Cai;Qinke Qi;Zhenqian Wu;Yonghong Wu","doi":"10.1109/LCOMM.2024.3509621","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3509621","url":null,"abstract":"We discuss the user localization problem assisted by reconfigurable intelligent surfaces (RIS) in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. By exploring the inherent sparse scattering characteristics of mmWave channels, we propose a tensor decomposition based method to obtain the channel parameters (i.e. angle, delay) required for user localization. Specifically, the received signal is modeled as a low-rank third-order tensor fitting a typical polyhedral model which contains a factor matrix of channel parameters. Then, we design a structured tensor decomposition method to estimate the channel parameters by using the Vandermonde property of factor matrices. Finally, based on the least squares criterion, the location of user are obtained from the estimated channel parameters. Simulation results show that the proposed estimation scheme can achieve a centimeter-level resolution of user localization.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 1","pages":"190-194"},"PeriodicalIF":3.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938386","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 : 2024-12-02DOI: 10.1109/LCOMM.2024.3509916
Qurrat-Ul-Ain Nadeem;Anas Chaaban;Mérouane Debbah
This work considers a multi-cell multi-user multiple-input multiple-output (MIMO) system that employs low resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) at each base station (BS) to limit the power consumption. Existing precoder designs for quantization-free systems are sub-optimal for such quantized systems, while the existing precoder designs for quantized systems consider single-cell settings and perfect channel state information (CSI). To address these gaps, we study the downlink linear precoder optimization problem in a cellular system under the distortions introduced by low resolution DACs based on a minimum mean square error (MMSE) approach, while accounting for imperfect CSI obtained in the uplink under distortions introduced by low resolution ADCs. The problem is analytically solved resulting in an optimal quantized multi-cell MMSE precoder that reduces both intra-cell and inter-cell interference under quantization errors, and yields better bit error rate performance than applying the existing conventional multi-cell and quantized single-cell linear precoders to a quantized multi-cell massive MIMO system.
{"title":"Optimal Quantized Multi-Cell MMSE Precoding With Low Resolution Data Converters","authors":"Qurrat-Ul-Ain Nadeem;Anas Chaaban;Mérouane Debbah","doi":"10.1109/LCOMM.2024.3509916","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3509916","url":null,"abstract":"This work considers a multi-cell multi-user multiple-input multiple-output (MIMO) system that employs low resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) at each base station (BS) to limit the power consumption. Existing precoder designs for quantization-free systems are sub-optimal for such quantized systems, while the existing precoder designs for quantized systems consider single-cell settings and perfect channel state information (CSI). To address these gaps, we study the downlink linear precoder optimization problem in a cellular system under the distortions introduced by low resolution DACs based on a minimum mean square error (MMSE) approach, while accounting for imperfect CSI obtained in the uplink under distortions introduced by low resolution ADCs. The problem is analytically solved resulting in an optimal quantized multi-cell MMSE precoder that reduces both intra-cell and inter-cell interference under quantization errors, and yields better bit error rate performance than applying the existing conventional multi-cell and quantized single-cell linear precoders to a quantized multi-cell massive MIMO system.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 1","pages":"195-199"},"PeriodicalIF":3.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938356","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 : 2024-11-29DOI: 10.1109/LCOMM.2024.3502423
Ning Rao;Hua Xu;Zisen Qi;Dan Wang;Xiang Peng;Lei Jiang
Reinforcement learning (RL)’s powerful optimization capabilities have been extensively applied in the field of wireless communication jamming decision-making. However, the generalization of jamming policies has rarely been explored, and most existing studies rely on task-customized reward functions, which are often intractable to design. To address these issues, we propose a meta RL method for frequency-hopping spread spectrum (FHSS) jamming decision-making, aided by inexpert demonstrations. Firstly, the policy network is meta-trained with multiple diverse tasks to obtain initial network parameters with good generalization. Subsequently, we combine RL and behavioral cloning (BC) to extract useful information from demonstrations, along with learning rate adaptation to achieve efficient policy exploration without the task-customized jamming reward. Simulations confirm that our proposed method not only adapts to unseen jamming tasks with just a few fine-tuning steps under general binary rewards condition, but also achieves higher accumulated jamming rewards and results in lower normalized throughput for users, outperforming state-of-the-art methods.
{"title":"Adaptive Jamming Decision-Making Against FHSS Communications via Inexpert Demonstrations Assisted Meta Reinforcement Learning","authors":"Ning Rao;Hua Xu;Zisen Qi;Dan Wang;Xiang Peng;Lei Jiang","doi":"10.1109/LCOMM.2024.3502423","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3502423","url":null,"abstract":"Reinforcement learning (RL)’s powerful optimization capabilities have been extensively applied in the field of wireless communication jamming decision-making. However, the generalization of jamming policies has rarely been explored, and most existing studies rely on task-customized reward functions, which are often intractable to design. To address these issues, we propose a meta RL method for frequency-hopping spread spectrum (FHSS) jamming decision-making, aided by inexpert demonstrations. Firstly, the policy network is meta-trained with multiple diverse tasks to obtain initial network parameters with good generalization. Subsequently, we combine RL and behavioral cloning (BC) to extract useful information from demonstrations, along with learning rate adaptation to achieve efficient policy exploration without the task-customized jamming reward. Simulations confirm that our proposed method not only adapts to unseen jamming tasks with just a few fine-tuning steps under general binary rewards condition, but also achieves higher accumulated jamming rewards and results in lower normalized throughput for users, outperforming state-of-the-art methods.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 1","pages":"105-109"},"PeriodicalIF":3.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940708","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 : 2024-11-29DOI: 10.1109/LCOMM.2024.3508848
Weicong Chen;Xinyi Yang;Chao-Kai Wen;Wankai Tang;Jinghe Wang;Yifei Yuan;Xiao Li;Shi Jin
The passive reconfigurable intelligent surface (RIS) requires numerous elements to achieve adequate array gain, which linearly increases power consumption (PC) with the number of reflection phases. To address this PC problem, this letter introduces a rotatable block-controlled RIS (BC-RIS) that preserves spectral efficiency (SE) while reducing power costs. Unlike the element-controlled RIS (EC-RIS), which necessitates independent phase control for each element, the BC-RIS uses a single phase control circuit for each block, substantially lowering power requirements. In the maximum ratio transmission, utilizing statistical channel state information (CSI) to rotate blocks and coherently superimpose signals with optimized reflection phase of blocks, the BC-RIS achieves the same averaged SE as the EC-RIS. Rotating RIS blocks with the statistical CSI allows for slow mechanical rotation speeds. To counteract the added power demands from rotation, influenced by block size, we have developed a segmentation scheme to minimize overall PC. Furthermore, constraints for rotation power-related parameters have been established to enhance the energy efficiency of the BC-RIS compared to the EC-RIS. Numerical results confirm that this approach significantly improves energy efficiency while maintaining performance.
{"title":"Rotatable Block-Controlled RIS: Bridging the Performance Gap to Element-Controlled Systems","authors":"Weicong Chen;Xinyi Yang;Chao-Kai Wen;Wankai Tang;Jinghe Wang;Yifei Yuan;Xiao Li;Shi Jin","doi":"10.1109/LCOMM.2024.3508848","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3508848","url":null,"abstract":"The passive reconfigurable intelligent surface (RIS) requires numerous elements to achieve adequate array gain, which linearly increases power consumption (PC) with the number of reflection phases. To address this PC problem, this letter introduces a rotatable block-controlled RIS (BC-RIS) that preserves spectral efficiency (SE) while reducing power costs. Unlike the element-controlled RIS (EC-RIS), which necessitates independent phase control for each element, the BC-RIS uses a single phase control circuit for each block, substantially lowering power requirements. In the maximum ratio transmission, utilizing statistical channel state information (CSI) to rotate blocks and coherently superimpose signals with optimized reflection phase of blocks, the BC-RIS achieves the same averaged SE as the EC-RIS. Rotating RIS blocks with the statistical CSI allows for slow mechanical rotation speeds. To counteract the added power demands from rotation, influenced by block size, we have developed a segmentation scheme to minimize overall PC. Furthermore, constraints for rotation power-related parameters have been established to enhance the energy efficiency of the BC-RIS compared to the EC-RIS. Numerical results confirm that this approach significantly improves energy efficiency while maintaining performance.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 1","pages":"185-189"},"PeriodicalIF":3.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938385","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 : 2024-11-27DOI: 10.1109/LCOMM.2024.3507176
Qigao Zhou;Feng Shen;Dingjie Xu;Sai Ma;Feihu Liu;Qiangqiang Sui
Current schemes are inadequate for achieving low bit error rate (BER) communication under extreme interference and limited pilot samples. Therefore, we propose a receiver scheme based on a spiral multi-hybrid convolutional network (SMMCNet). Specifically, the SMMCNet framework enhances decoding capability at low signal-to-noise ratios (SNR) by leveraging the statistical characteristics of offline white noise. The Spiral Multi-scale Hybrid Convolutions (SMMCov) reduce feature channel dimensions in multi-scale convolutions, enabling a lightweight deep network. The dual-layer shared connection mode allows deep-level, small-channel convolutions to capture diverse depth, multi-channel, and multi-scale target signal features, enhancing SMMCNet’s feature learning capability with limited samples. In extreme multipath simulations, the receiver achieves a bit error rate two orders of magnitude lower than that of a traditional receiver, with significantly fewer parameters than other deep learning receivers.
{"title":"The Intelligent Receiver Scheme With Joint Training for UWB","authors":"Qigao Zhou;Feng Shen;Dingjie Xu;Sai Ma;Feihu Liu;Qiangqiang Sui","doi":"10.1109/LCOMM.2024.3507176","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3507176","url":null,"abstract":"Current schemes are inadequate for achieving low bit error rate (BER) communication under extreme interference and limited pilot samples. Therefore, we propose a receiver scheme based on a spiral multi-hybrid convolutional network (SMMCNet). Specifically, the SMMCNet framework enhances decoding capability at low signal-to-noise ratios (SNR) by leveraging the statistical characteristics of offline white noise. The Spiral Multi-scale Hybrid Convolutions (SMMCov) reduce feature channel dimensions in multi-scale convolutions, enabling a lightweight deep network. The dual-layer shared connection mode allows deep-level, small-channel convolutions to capture diverse depth, multi-channel, and multi-scale target signal features, enhancing SMMCNet’s feature learning capability with limited samples. In extreme multipath simulations, the receiver achieves a bit error rate two orders of magnitude lower than that of a traditional receiver, with significantly fewer parameters than other deep learning receivers.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 2","pages":"254-258"},"PeriodicalIF":3.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403961","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 : 2024-11-26DOI: 10.1109/LCOMM.2024.3506696
Luis F. Abanto-Leon;Setareh Maghsudi
We investigate the joint admission control and discrete-phase multicast beamforming design for integrated sensing and commmunications (ISAC) systems, where sensing and communications functionalities have different hierarchies. Specifically, the ISAC system first allocates resources to the higher-hierarchy functionality and opportunistically uses the remaining resources to support the lower-hierarchy one. This resource allocation problem is a nonconvex mixed-integer nonlinear program (MINLP). We propose an exact mixed-integer linear program (MILP) reformulation, leading to a globally optimal solution. In addition, we implemented three baselines for comparison, which our proposed method outperforms by more than 39%.
{"title":"Hierarchical Functionality Prioritization in Multicast ISAC: Optimal Admission Control and Discrete-Phase Beamforming","authors":"Luis F. Abanto-Leon;Setareh Maghsudi","doi":"10.1109/LCOMM.2024.3506696","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3506696","url":null,"abstract":"We investigate the joint admission control and discrete-phase multicast beamforming design for integrated sensing and commmunications (ISAC) systems, where sensing and communications functionalities have different hierarchies. Specifically, the ISAC system first allocates resources to the higher-hierarchy functionality and opportunistically uses the remaining resources to support the lower-hierarchy one. This resource allocation problem is a nonconvex mixed-integer nonlinear program (MINLP). We propose an exact mixed-integer linear program (MILP) reformulation, leading to a globally optimal solution. In addition, we implemented three baselines for comparison, which our proposed method outperforms by more than 39%.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 1","pages":"180-184"},"PeriodicalIF":3.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938383","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}
Federated learning is an effective solution to protect data privacy, but the efficiency and performance of the entire federated system are challenging to balance due to the heterogeneity of client resources and data. To alleviate this dilemma, we propose a new training intensity allocation framework based on model structure. First, we design an optimization model that considers both time and energy consumption constraints, minimizing energy consumption under time constraints and accelerating the convergence of federated training. Then we construct a reinforcement learning-based model allocation strategy for realistic dynamic environments, automatically allocating appropriate model sizes to clients under complex communication conditions and heterogeneous computing resources. Finally, a large number of experiments demonstrate the feasibility and effectiveness of the proposed framework.
{"title":"Resource-Aware Personalized Federated Learning Based on Reinforcement Learning","authors":"Tingting Wu;Xiao Li;Pengpei Gao;Wei Yu;Lun Xin;Manxue Guo","doi":"10.1109/LCOMM.2024.3506015","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3506015","url":null,"abstract":"Federated learning is an effective solution to protect data privacy, but the efficiency and performance of the entire federated system are challenging to balance due to the heterogeneity of client resources and data. To alleviate this dilemma, we propose a new training intensity allocation framework based on model structure. First, we design an optimization model that considers both time and energy consumption constraints, minimizing energy consumption under time constraints and accelerating the convergence of federated training. Then we construct a reinforcement learning-based model allocation strategy for realistic dynamic environments, automatically allocating appropriate model sizes to clients under complex communication conditions and heterogeneous computing resources. Finally, a large number of experiments demonstrate the feasibility and effectiveness of the proposed framework.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 1","pages":"175-179"},"PeriodicalIF":3.7,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938517","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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