The authors regret that there were errors in the affiliations and the funding declaration in the original published version.The affiliations a and b of the original manuscript are"School of Information Engineerin...The authors regret that there were errors in the affiliations and the funding declaration in the original published version.The affiliations a and b of the original manuscript are"School of Information Engineering,Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications,Nanchang University,Nanchang 330031,China",and"School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China",respectively.The order of the two affiliations are not correct.展开更多
Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably incr...Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably increases computing demands,risking real-time system stability.Vehicle Edge Computing(VEC)addresses this by offloading tasks to Road Side Units(RSUs),ensuring timely services.Our previous work,the FLSimCo algorithm,which uses local resources for federated Self-Supervised Learning(SSL),has a limitation:vehicles often can’t complete all iteration tasks.Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power,CPU frequency,and task assignment ratios,balancing local and RSU-based training.Meanwhile,setting an offloading threshold further prevents inefficiencies.Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.展开更多
This study explored the transformative potential of artificial intelligence(AI)in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output(UM-MIMO)systems.It begins by outlining the ch...This study explored the transformative potential of artificial intelligence(AI)in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output(UM-MIMO)systems.It begins by outlining the characteristics of terahertz UM-MIMO systems and identifies three primary challenges for transceiver design:computational complexity,modeling difficulty,and measurement limitations.The study posits that AI provides a promising solution to these challenges.Three systematic research roadmaps are proposed for developing AI algorithms tailored to terahertz UM-MIMO systems.The first roadmap,model-driven deep learning(DL),emphasizes the importance of leveraging available domain knowledge and advocates the adoption of AI only to enhance bottleneck modules within an established signal processing or optimization framework.Four essential steps are discussed:algorithmic frameworks,basis algorithms,loss function design,and neural architecture design.The second roadmap presents channel state information(CSI)foundation models,aimed at unifying the design of different transceiver modules by focusing on their shared foundation,that is,the wireless channel.The training of a single compact foundation model is proposed to estimate the score function of wireless channels,which serve as a versatile prior for designing a wide variety of transceiver modules.Four essential steps are outlined:general frameworks,conditioning,site-specific adaptation,and the joint design of CSI foundation models and model-driven DL.The third roadmap aims to explore potential directions for applying pretrained large language models(LLMs)to terahertz UM-MIMO systems.Several application scenarios are envisioned,including LLM-based estimation,optimization,search,network management,and protocol understanding.Finally,the study highlights open problems and future research directions.展开更多
The recently commercialized fifth-generation(5G)wireless networks have achieved many improvements,including air interface enhancement,spectrum expansion,and network intensification by several key technologies,such as ...The recently commercialized fifth-generation(5G)wireless networks have achieved many improvements,including air interface enhancement,spectrum expansion,and network intensification by several key technologies,such as massive multiple-input multipleoutput(MIMO),millimeter-wave communications,and ultra-dense networking.Despite the deployment of 5G commercial systems,wireless communications is still facing many challenges to enable connected intelligence and a myriad of applications such as industrial Internet-ofthings,autonomous systems,brain-computer interfaces,digital twin,tactile Internet,etc.Therefore,it is urgent to start research on the sixth-generation(6G)wireless communication systems.Among the candidate technologies for 6G,cell-free massive MIMO,which combines the advantages of distributed systems and massive MIMO,is a promising solution to enhance the wireless transmission efficiency and provide better coverage.In this paper,we present a comprehensive study on cell-free massive MIMO for 6G wireless communication networks with a special focus on the signal processing perspective.Specifically,we introduce enabling physical layer technologies for cell-free massive MIMO,such as user association,pilot assignment,transmitter,and receiver design,as well as power control and allocation.Furthermore,some current and future research problems are described.展开更多
Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter-wave(mmWave)communications,but its precoder design is highly complicated.In this paper,we propose a new hybrid precoder...Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter-wave(mmWave)communications,but its precoder design is highly complicated.In this paper,we propose a new hybrid precoder implementation,namely the double phase shifter(DPS)implementation,which enables highly tractable hybrid precoder design.Efficient algorithms are then developed for two popular hybrid precoder structures,i.e.,the fully-and partially-connected structures.For the fully-connected one,the RF-only precoding and hybrid precoding problems are formulated as a least absolute shrinkage and selection operator problem and a low-rank matrix approximation problem,respectively.In this way,computationally efficient algorithms are provided to approach the performance of the fully digital one with a small number of radio frequency(RF)chains.On the other hand,the hybrid precoder design in the partially-connected structure is identified as an eigenvalue problem.To enhance the performance of this cost-effective structure,dynamic mapping from RF chains to antennas is further proposed,for which a greedy algorithm and a modified K-means algorithm are developed.Simulation results demonstrate the performance gains of the proposed hybrid precoding algorithms over existing ones.It shows that,with the proposed DPS implementation,the fully-connected structure enjoys both satisfactory performance and low design complexity while the partially-connected one serves as an economic solution with low hardware complexity.展开更多
With the rapid upsurge of deep learning tasks at the network edge,effective edge artificial intelligence(AI)inference becomes critical to provide lowlatency intelligent services for mobile users via leveraging the edg...With the rapid upsurge of deep learning tasks at the network edge,effective edge artificial intelligence(AI)inference becomes critical to provide lowlatency intelligent services for mobile users via leveraging the edge computing capability.In such scenarios,energy efficiency becomes a primary concern.In this paper,we present a joint inference task selection and downlink beamforming strategy to achieve energy-efficient edge AI inference through minimizing the overall power consumption consisting of both computation and transmission power consumption,yielding a mixed combinatorial optimization problem.By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector,we reformulate the optimization as a group sparse beamforming problem.To solve this challenging problem,we propose a logsum function based three-stage approach.By adopting the log-sum function to enhance the group sparsity,a proximal iteratively reweighted algorithm is developed.Furthermore,we establish the global convergence analysis and provide the ergodic worst-case convergence rate for this algorithm.Simulation results will demonstrate the effectiveness of the proposed approach for improving energy efficiency in edge AI inference systems.展开更多
In multi-user wireless communication systems,dynamic power allocation is an important means to deal with the time-varying nature of the physical and network layers.However,the current layer optimization approach to po...In multi-user wireless communication systems,dynamic power allocation is an important means to deal with the time-varying nature of the physical and network layers.However,the current layer optimization approach to power allocation cannot achieve the global optimum of the overall system performance.To solve this problem,a cross-layer optimization framework is presented for downlink power allocation,which takes both the channel and buffer states into account.A cross-layer optimization problem is formulated to optimize the total throughput with queue length and power constraints.An analytical solution and a low complexity dynamic programming algorithm,which are referred as water-filling in cellar(WFIC)policy,are presented to optimize the downlink power allocation.Finally,simulation results are presented to demonstrate the potential of the proposed method.展开更多
文摘The authors regret that there were errors in the affiliations and the funding declaration in the original published version.The affiliations a and b of the original manuscript are"School of Information Engineering,Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications,Nanchang University,Nanchang 330031,China",and"School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China",respectively.The order of the two affiliations are not correct.
文摘Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably increases computing demands,risking real-time system stability.Vehicle Edge Computing(VEC)addresses this by offloading tasks to Road Side Units(RSUs),ensuring timely services.Our previous work,the FLSimCo algorithm,which uses local resources for federated Self-Supervised Learning(SSL),has a limitation:vehicles often can’t complete all iteration tasks.Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power,CPU frequency,and task assignment ratios,balancing local and RSU-based training.Meanwhile,setting an offloading threshold further prevents inefficiencies.Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.
基金supported in part by the Hong Kong Research Grant Council(16209023)。
文摘This study explored the transformative potential of artificial intelligence(AI)in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output(UM-MIMO)systems.It begins by outlining the characteristics of terahertz UM-MIMO systems and identifies three primary challenges for transceiver design:computational complexity,modeling difficulty,and measurement limitations.The study posits that AI provides a promising solution to these challenges.Three systematic research roadmaps are proposed for developing AI algorithms tailored to terahertz UM-MIMO systems.The first roadmap,model-driven deep learning(DL),emphasizes the importance of leveraging available domain knowledge and advocates the adoption of AI only to enhance bottleneck modules within an established signal processing or optimization framework.Four essential steps are discussed:algorithmic frameworks,basis algorithms,loss function design,and neural architecture design.The second roadmap presents channel state information(CSI)foundation models,aimed at unifying the design of different transceiver modules by focusing on their shared foundation,that is,the wireless channel.The training of a single compact foundation model is proposed to estimate the score function of wireless channels,which serve as a versatile prior for designing a wide variety of transceiver modules.Four essential steps are outlined:general frameworks,conditioning,site-specific adaptation,and the joint design of CSI foundation models and model-driven DL.The third roadmap aims to explore potential directions for applying pretrained large language models(LLMs)to terahertz UM-MIMO systems.Several application scenarios are envisioned,including LLM-based estimation,optimization,search,network management,and protocol understanding.Finally,the study highlights open problems and future research directions.
文摘The recently commercialized fifth-generation(5G)wireless networks have achieved many improvements,including air interface enhancement,spectrum expansion,and network intensification by several key technologies,such as massive multiple-input multipleoutput(MIMO),millimeter-wave communications,and ultra-dense networking.Despite the deployment of 5G commercial systems,wireless communications is still facing many challenges to enable connected intelligence and a myriad of applications such as industrial Internet-ofthings,autonomous systems,brain-computer interfaces,digital twin,tactile Internet,etc.Therefore,it is urgent to start research on the sixth-generation(6G)wireless communication systems.Among the candidate technologies for 6G,cell-free massive MIMO,which combines the advantages of distributed systems and massive MIMO,is a promising solution to enhance the wireless transmission efficiency and provide better coverage.In this paper,we present a comprehensive study on cell-free massive MIMO for 6G wireless communication networks with a special focus on the signal processing perspective.Specifically,we introduce enabling physical layer technologies for cell-free massive MIMO,such as user association,pilot assignment,transmitter,and receiver design,as well as power control and allocation.Furthermore,some current and future research problems are described.
基金supported in part by the Hong Kong Research Grants Council under Grant No.16210216 and in part by the Alexander von Humboldt Foundation.
文摘Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter-wave(mmWave)communications,but its precoder design is highly complicated.In this paper,we propose a new hybrid precoder implementation,namely the double phase shifter(DPS)implementation,which enables highly tractable hybrid precoder design.Efficient algorithms are then developed for two popular hybrid precoder structures,i.e.,the fully-and partially-connected structures.For the fully-connected one,the RF-only precoding and hybrid precoding problems are formulated as a least absolute shrinkage and selection operator problem and a low-rank matrix approximation problem,respectively.In this way,computationally efficient algorithms are provided to approach the performance of the fully digital one with a small number of radio frequency(RF)chains.On the other hand,the hybrid precoder design in the partially-connected structure is identified as an eigenvalue problem.To enhance the performance of this cost-effective structure,dynamic mapping from RF chains to antennas is further proposed,for which a greedy algorithm and a modified K-means algorithm are developed.Simulation results demonstrate the performance gains of the proposed hybrid precoding algorithms over existing ones.It shows that,with the proposed DPS implementation,the fully-connected structure enjoys both satisfactory performance and low design complexity while the partially-connected one serves as an economic solution with low hardware complexity.
基金Part of this work was presented at the IEEE 90th Vehicu-lar Technology Conference(VTC2019-Fall)Honolulu,Hawaii,USA,Sept.2019[1]+1 种基金This work was supported in part by National Nature Science Foun-dation of China under Grant 61601290(Yuanming Shi)and a start-up fund of Hong Kong Polytechnic University(Project ID P0013883)(Jun Zhang)The associate editor coordinating the review of this paper and approving it for publication was R.Wang。
文摘With the rapid upsurge of deep learning tasks at the network edge,effective edge artificial intelligence(AI)inference becomes critical to provide lowlatency intelligent services for mobile users via leveraging the edge computing capability.In such scenarios,energy efficiency becomes a primary concern.In this paper,we present a joint inference task selection and downlink beamforming strategy to achieve energy-efficient edge AI inference through minimizing the overall power consumption consisting of both computation and transmission power consumption,yielding a mixed combinatorial optimization problem.By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector,we reformulate the optimization as a group sparse beamforming problem.To solve this challenging problem,we propose a logsum function based three-stage approach.By adopting the log-sum function to enhance the group sparsity,a proximal iteratively reweighted algorithm is developed.Furthermore,we establish the global convergence analysis and provide the ergodic worst-case convergence rate for this algorithm.Simulation results will demonstrate the effectiveness of the proposed approach for improving energy efficiency in edge AI inference systems.
基金supported by the National Natural Science Foundation of China(Grant No.60472027).
文摘In multi-user wireless communication systems,dynamic power allocation is an important means to deal with the time-varying nature of the physical and network layers.However,the current layer optimization approach to power allocation cannot achieve the global optimum of the overall system performance.To solve this problem,a cross-layer optimization framework is presented for downlink power allocation,which takes both the channel and buffer states into account.A cross-layer optimization problem is formulated to optimize the total throughput with queue length and power constraints.An analytical solution and a low complexity dynamic programming algorithm,which are referred as water-filling in cellar(WFIC)policy,are presented to optimize the downlink power allocation.Finally,simulation results are presented to demonstrate the potential of the proposed method.
