Dual-function communication radar systems use common Radio Frequency(RF)signals are used for both communication and detection.For better compatibility with existing communication systems,we adopt Multiple-Input Multip...Dual-function communication radar systems use common Radio Frequency(RF)signals are used for both communication and detection.For better compatibility with existing communication systems,we adopt Multiple-Input Multiple-Output(MIMO)Orthogonal Frequency Division Multiplexing(OFDM)signals as integrated signals and investigate the estimation performance of MIMO-OFDM signals.First,we analyze the Cramer-Rao Lower Bound(CRLB)of parameter estimation.Then,the transmit powers over different subcarriers are optimized to achieve the best tradeoff between the transmission rate and the estimation performance.Finally,we propose a more accurate estimation method that uses Canonical Polyadic Decomposition(CPD)of the third-order tensor to obtain the parameter matrices.Due to the characteristic of the column structure of the parameter matrices,we only need to use DFT/IDFT to recover the parameters of multiple targets.The simulation results show that tensor-based estimation method can achieve a performance close to CRLB,and the estimation performance can be improved by optimizing the transmit powers.展开更多
In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication a...In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication and sensing in different subcarrier sets.To obtain the best tradeoff between communication and sensing performance,we first derive Cramer-Rao Bound(CRB) of targets in detection area,and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power.To tackle the non-convex mixed integer programming problem,we decompose the original problem into a semidefinite programming(SDP) problem and a convex quadratic integer problem and solve them iteratively.The numerical results demonstrate the effectiveness of our proposed algorithm,as well as the performance improvement brought by optimizing radar receivers selection.展开更多
The convergence of information,communication,and data technologies(ICDT)has been identified as one of the developing trends of the sixth generation(6G)network.Service-based architecture(SBA)as one of the promising inf...The convergence of information,communication,and data technologies(ICDT)has been identified as one of the developing trends of the sixth generation(6G)network.Service-based architecture(SBA)as one of the promising information technology,has been preliminarily introduced into the fifth generation(5G)core network(CN)and successfully commercialized,which verifies its feasibility and effectiveness.However,SBA mainly focuses on the control plane of CN at present and the SBA-CN user plane is being studied by the industry.In addition to further evolving the SBA-CN,SBA radio access network(RAN)should also be considered to enable an end-toend SBA,so as to meet more comprehensive and extreme requirements of future applications,as well as support fast rollout requirements of RAN devices.展开更多
Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective...Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective implementation of massive MIMO challenging,due to the size and weight limits of the masssive MIMO that are located on each BS.Therefore,in order to miniaturize the massive MIMO,it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis.In this paper,a multiple-pattern synthesis is considered towards convex optimization(CO).The joint convex optimization(JCO)based synthesis is proposed to construct a codebook for beamforming.Then,a criterion containing multiple constraints is developed,in which the sparse array is required to fullfill all constraints.Finally,extensive evaluations are performed under realistic simulation settings.The results show that with the same number of antenna elements,sparse array using the proposed JCO-based synthesis outperforms not only the uniform array,but also the sparse array with the existing CO-based synthesis method.Furthermore,with a half of the number of antenna elements that on the uniform array,the performance of the JCO-based sparse array approaches to that of the uniform array.展开更多
In 5G networks,optimization of antenna beam weights of base stations has become the key application of AI for network optimization.For 6G,higher frequency bands and much denser cells are expected,and the importance of...In 5G networks,optimization of antenna beam weights of base stations has become the key application of AI for network optimization.For 6G,higher frequency bands and much denser cells are expected,and the importance of automatic and accurate beamforming assisted by AI will become more prominent.In existing network,servers are“patched”to network equipment to act as a centralized brain for model training and inference leading to high transmission overhead,large inference latency and potential risks of data security.Decentralized architectures have been proposed to achieve flexible parameter configuration and fast local response,but it is inefficient in collecting and sharing global information among base stations.In this paper,we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization.We analyze the performance and costs of the proposed solution with two other architectural solutions by simulation.Compared with the centralized solution,our solution improves prediction accuracy by 24.66%,and reduces storage cost by 83.82%.Compared with the decentralized solution,our solution improves prediction accuracy by 68.26%,and improves coverage performance by 0.4 dB.At last,the future research work is prospected.展开更多
Task diversity is one of the biggest challenges for future sixth-generation(6G)networks.Taking the task as the center and driving the dynamic 6G radio access network(RAN)with artificial intelligence(AI)are necessary t...Task diversity is one of the biggest challenges for future sixth-generation(6G)networks.Taking the task as the center and driving the dynamic 6G radio access network(RAN)with artificial intelligence(AI)are necessary to accurately meet the personalized demands of users.However,AI can only configure the parameters of a monolithic RAN and cannot schedule the functions.The development trend of 6G RANs is to enhance dynamic capability and scheduling ease.In this paper,we propose a service-based RAN architecture that can deploy decoupled RAN functions and customize networks according to tasks.Protocol analysis shows that the interactive relationship between RAN control plane(CP)functions is complex and needs to be decoupled according to the principles of high cohesion and low coupling.Based on the graph theory rather than expert experience,we design a RAN decoupling scheme.The functional connection and interaction of the CP are represented by constructing an undirected weighted graph,followed by achieving decoupling of the CP through a minimum spanning tree.Then an integration decoupling scheme of a RAN-CN(core network)is introduced considering the duplicate and redundant functions of the RAN and CN.The granularity of decoupling in a service-based RAN is determined by analyzing the flexibility of decoupling,complexity of signaling,and processing delay.We find that it is more appropriate to decouple the RAN CP into four services.The integration decoupling of the RAN-CN resolves the technical bottleneck of low serial efficiency in the Ng interface,supporting AI-based global service scheduling.展开更多
The rapid development of communications industry has spawned more new services and applications.The sixth-generation wireless communication system(6G)network is faced with more stringent and diverse requirements.While...The rapid development of communications industry has spawned more new services and applications.The sixth-generation wireless communication system(6G)network is faced with more stringent and diverse requirements.While ensuring performance requirements,such as high data rate and low latency,the problem of high energy consumption in the fifth-generation wireless communication system(5G)network has also become one of the problems to be solved in 6G.The wide-area coverage signaling cell technology conforms to the future development trend of radio access networks,and has the advantages of reducing network energy consumption and improving resource utilization.In wide-area coverage signaling cells,on-demand multi-dimensional resource allocation is an important technical means to ensure the ultimate performance requirements of users,and its effect will affect the efficiency of network resource utilization.This paper constructs a user-centric dynamic allocation model of wireless resources,and proposes a deep Q-network based dynamic resource allocation algorithm.The algorithm can realize dynamic and flexible admission control and multi-dimensional resource allocation in wide-area coverage signaling cells according to the data rate and latency demands of users.According to the simulation results,the proposed algorithm can effectively improve the average user experience on a long time scale,and ensure network users a high data rate and low energy consumption.展开更多
The application of the artificial intelligence(AI) technology in the 5 th generation mobile communication system(5 G) networks promotes the development of the mobile communication network and its application in vertic...The application of the artificial intelligence(AI) technology in the 5 th generation mobile communication system(5 G) networks promotes the development of the mobile communication network and its application in vertical industries, however, the application models of "patching" and "plug-in" have hindered the effect of AI applications. Meanwhile, the application of AI in all walks of life puts forward requirements for new capabilities of the future network, such as distributed training, real-time collaborative inference, local data processing, etc., which require "native intelligence design" in future networks. This paper discusses the requirements of native intelligence in the 6 th generation mobile communication system(6 G) networks from the perspectives of 5 G intelligent network challenges and the "ubiquitous intelligence" vision of 6 G, and analyzes the technical challenges of the AI workflows in its lifecycle and the AI as a service(AIaaS) in cloud network. The progress and deficiencies of the current research on AI functional architecture in various industry organizations are summarized. The end-to-end functional architecture for native AI for 6 G network and its three key technical characteristics are proposed: quality of AI services(QoAIS) based AI service orchestration for its full lifecycle, deep integration of native AI computing and communication, and integration of native AI and digital twin network. The directions of future research are also prospected.展开更多
As a candidate technique to achieve sixth-generation wireless communication(6G),reconfigurable intelligent surface(RIS)has become popular in both academia and industry.For better exploration of the advantages of RIS,w...As a candidate technique to achieve sixth-generation wireless communication(6G),reconfigurable intelligent surface(RIS)has become popular in both academia and industry.For better exploration of the advantages of RIS,we compare the performances of RIS and network-controlled repeater(NCR)in 3GPP release-18.We first theoretically analyze the received signal power and signal-to-noise ratio performances for both RIS and NCR.Then,we simulate the reference signal received power and signal-to-interference-and-noise ratio performances at the system level for both RIS and NCR in the frequency range 1 and frequency range 2 bands.Finally,several insights on engineering applications are given based on the comparison between RIS and NCR.展开更多
基金supported by the National Natural Science Foundation of China under grants 62072229,U1936201,62071220,61976113joint project of China Mobile Research Institute&X-NET。
文摘Dual-function communication radar systems use common Radio Frequency(RF)signals are used for both communication and detection.For better compatibility with existing communication systems,we adopt Multiple-Input Multiple-Output(MIMO)Orthogonal Frequency Division Multiplexing(OFDM)signals as integrated signals and investigate the estimation performance of MIMO-OFDM signals.First,we analyze the Cramer-Rao Lower Bound(CRLB)of parameter estimation.Then,the transmit powers over different subcarriers are optimized to achieve the best tradeoff between the transmission rate and the estimation performance.Finally,we propose a more accurate estimation method that uses Canonical Polyadic Decomposition(CPD)of the third-order tensor to obtain the parameter matrices.Due to the characteristic of the column structure of the parameter matrices,we only need to use DFT/IDFT to recover the parameters of multiple targets.The simulation results show that tensor-based estimation method can achieve a performance close to CRLB,and the estimation performance can be improved by optimizing the transmit powers.
基金supported by the National Key R&D Program of China (2023YFB2905605)the National Natural Science Foundation of China (62072229)。
文摘In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication and sensing in different subcarrier sets.To obtain the best tradeoff between communication and sensing performance,we first derive Cramer-Rao Bound(CRB) of targets in detection area,and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power.To tackle the non-convex mixed integer programming problem,we decompose the original problem into a semidefinite programming(SDP) problem and a convex quadratic integer problem and solve them iteratively.The numerical results demonstrate the effectiveness of our proposed algorithm,as well as the performance improvement brought by optimizing radar receivers selection.
基金supported by the National Key R&D Program of China(2020YFB1806800)。
文摘The convergence of information,communication,and data technologies(ICDT)has been identified as one of the developing trends of the sixth generation(6G)network.Service-based architecture(SBA)as one of the promising information technology,has been preliminarily introduced into the fifth generation(5G)core network(CN)and successfully commercialized,which verifies its feasibility and effectiveness.However,SBA mainly focuses on the control plane of CN at present and the SBA-CN user plane is being studied by the industry.In addition to further evolving the SBA-CN,SBA radio access network(RAN)should also be considered to enable an end-toend SBA,so as to meet more comprehensive and extreme requirements of future applications,as well as support fast rollout requirements of RAN devices.
文摘Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective implementation of massive MIMO challenging,due to the size and weight limits of the masssive MIMO that are located on each BS.Therefore,in order to miniaturize the massive MIMO,it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis.In this paper,a multiple-pattern synthesis is considered towards convex optimization(CO).The joint convex optimization(JCO)based synthesis is proposed to construct a codebook for beamforming.Then,a criterion containing multiple constraints is developed,in which the sparse array is required to fullfill all constraints.Finally,extensive evaluations are performed under realistic simulation settings.The results show that with the same number of antenna elements,sparse array using the proposed JCO-based synthesis outperforms not only the uniform array,but also the sparse array with the existing CO-based synthesis method.Furthermore,with a half of the number of antenna elements that on the uniform array,the performance of the JCO-based sparse array approaches to that of the uniform array.
基金supported by the National Key Research and Development Program of China(2020YFB1806800)funded by Beijing University of Posts and Telecommuns(BUPT)China Mobile Research Institute Joint Innoviation Center。
文摘In 5G networks,optimization of antenna beam weights of base stations has become the key application of AI for network optimization.For 6G,higher frequency bands and much denser cells are expected,and the importance of automatic and accurate beamforming assisted by AI will become more prominent.In existing network,servers are“patched”to network equipment to act as a centralized brain for model training and inference leading to high transmission overhead,large inference latency and potential risks of data security.Decentralized architectures have been proposed to achieve flexible parameter configuration and fast local response,but it is inefficient in collecting and sharing global information among base stations.In this paper,we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization.We analyze the performance and costs of the proposed solution with two other architectural solutions by simulation.Compared with the centralized solution,our solution improves prediction accuracy by 24.66%,and reduces storage cost by 83.82%.Compared with the decentralized solution,our solution improves prediction accuracy by 68.26%,and improves coverage performance by 0.4 dB.At last,the future research work is prospected.
基金funded by Institute of Computing Technology,Chinese Academy of Sciences–China Mobile Communications Group Co.,Ltd.Joint Institute。
文摘Task diversity is one of the biggest challenges for future sixth-generation(6G)networks.Taking the task as the center and driving the dynamic 6G radio access network(RAN)with artificial intelligence(AI)are necessary to accurately meet the personalized demands of users.However,AI can only configure the parameters of a monolithic RAN and cannot schedule the functions.The development trend of 6G RANs is to enhance dynamic capability and scheduling ease.In this paper,we propose a service-based RAN architecture that can deploy decoupled RAN functions and customize networks according to tasks.Protocol analysis shows that the interactive relationship between RAN control plane(CP)functions is complex and needs to be decoupled according to the principles of high cohesion and low coupling.Based on the graph theory rather than expert experience,we design a RAN decoupling scheme.The functional connection and interaction of the CP are represented by constructing an undirected weighted graph,followed by achieving decoupling of the CP through a minimum spanning tree.Then an integration decoupling scheme of a RAN-CN(core network)is introduced considering the duplicate and redundant functions of the RAN and CN.The granularity of decoupling in a service-based RAN is determined by analyzing the flexibility of decoupling,complexity of signaling,and processing delay.We find that it is more appropriate to decouple the RAN CP into four services.The integration decoupling of the RAN-CN resolves the technical bottleneck of low serial efficiency in the Ng interface,supporting AI-based global service scheduling.
基金Project supported by the National Key Research and Development Program of China(No.2020YFB1806800)。
文摘The rapid development of communications industry has spawned more new services and applications.The sixth-generation wireless communication system(6G)network is faced with more stringent and diverse requirements.While ensuring performance requirements,such as high data rate and low latency,the problem of high energy consumption in the fifth-generation wireless communication system(5G)network has also become one of the problems to be solved in 6G.The wide-area coverage signaling cell technology conforms to the future development trend of radio access networks,and has the advantages of reducing network energy consumption and improving resource utilization.In wide-area coverage signaling cells,on-demand multi-dimensional resource allocation is an important technical means to ensure the ultimate performance requirements of users,and its effect will affect the efficiency of network resource utilization.This paper constructs a user-centric dynamic allocation model of wireless resources,and proposes a deep Q-network based dynamic resource allocation algorithm.The algorithm can realize dynamic and flexible admission control and multi-dimensional resource allocation in wide-area coverage signaling cells according to the data rate and latency demands of users.According to the simulation results,the proposed algorithm can effectively improve the average user experience on a long time scale,and ensure network users a high data rate and low energy consumption.
基金supported by the National Key R&D Program of China (2020YFB1806800)。
文摘The application of the artificial intelligence(AI) technology in the 5 th generation mobile communication system(5 G) networks promotes the development of the mobile communication network and its application in vertical industries, however, the application models of "patching" and "plug-in" have hindered the effect of AI applications. Meanwhile, the application of AI in all walks of life puts forward requirements for new capabilities of the future network, such as distributed training, real-time collaborative inference, local data processing, etc., which require "native intelligence design" in future networks. This paper discusses the requirements of native intelligence in the 6 th generation mobile communication system(6 G) networks from the perspectives of 5 G intelligent network challenges and the "ubiquitous intelligence" vision of 6 G, and analyzes the technical challenges of the AI workflows in its lifecycle and the AI as a service(AIaaS) in cloud network. The progress and deficiencies of the current research on AI functional architecture in various industry organizations are summarized. The end-to-end functional architecture for native AI for 6 G network and its three key technical characteristics are proposed: quality of AI services(QoAIS) based AI service orchestration for its full lifecycle, deep integration of native AI computing and communication, and integration of native AI and digital twin network. The directions of future research are also prospected.
文摘As a candidate technique to achieve sixth-generation wireless communication(6G),reconfigurable intelligent surface(RIS)has become popular in both academia and industry.For better exploration of the advantages of RIS,we compare the performances of RIS and network-controlled repeater(NCR)in 3GPP release-18.We first theoretically analyze the received signal power and signal-to-noise ratio performances for both RIS and NCR.Then,we simulate the reference signal received power and signal-to-interference-and-noise ratio performances at the system level for both RIS and NCR in the frequency range 1 and frequency range 2 bands.Finally,several insights on engineering applications are given based on the comparison between RIS and NCR.
