Sparsity-based joint active user detection and channel estimation(JADCE)algorithms are crucial in grant-free massive machine-type communication(mMTC)systems.The conventional compressed sensing algorithms are tailored ...Sparsity-based joint active user detection and channel estimation(JADCE)algorithms are crucial in grant-free massive machine-type communication(mMTC)systems.The conventional compressed sensing algorithms are tailored for noncoherent communication systems,where the correlation between any two measurements is as minimal as possible.However,existing sparsity-based JADCE approaches may not achieve optimal performance in strongly coherent systems,especially with a small number of pilot subcarriers.To tackle this challenge,we formulate JADCE as a joint sparse signal recovery problem,leveraging the block-type row-sparse structure of millimeter-wave(mmWave)channels in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMOOFDM)systems.Then,we propose an efficient difference-of-convex function algorithm(DCA)based JADCE algorithm with multiple measurement vector(MMV)frameworks,promoting the row-sparsity of the channel matrix.To mitigate the computational complexity further,we introduce a fast DCA-based JADCE algorithm via a proximal operator,which allows a low-complexity alternating direction multiplier method(ADMM)to resolve the optimization problem directly.Finally,simulation results demonstrate that the two proposed difference-of-convex(DC)algorithms achieve effective active user detection and accurate channel estimation compared with state-of-the-art compressed sensing based JADCE techniques.展开更多
This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and m...This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.展开更多
The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,th...The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.展开更多
基金supported by the Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515140074)and the Natural Science Foundation of Liaoning Province,China(No.2023-MS-108)。
文摘Sparsity-based joint active user detection and channel estimation(JADCE)algorithms are crucial in grant-free massive machine-type communication(mMTC)systems.The conventional compressed sensing algorithms are tailored for noncoherent communication systems,where the correlation between any two measurements is as minimal as possible.However,existing sparsity-based JADCE approaches may not achieve optimal performance in strongly coherent systems,especially with a small number of pilot subcarriers.To tackle this challenge,we formulate JADCE as a joint sparse signal recovery problem,leveraging the block-type row-sparse structure of millimeter-wave(mmWave)channels in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMOOFDM)systems.Then,we propose an efficient difference-of-convex function algorithm(DCA)based JADCE algorithm with multiple measurement vector(MMV)frameworks,promoting the row-sparsity of the channel matrix.To mitigate the computational complexity further,we introduce a fast DCA-based JADCE algorithm via a proximal operator,which allows a low-complexity alternating direction multiplier method(ADMM)to resolve the optimization problem directly.Finally,simulation results demonstrate that the two proposed difference-of-convex(DC)algorithms achieve effective active user detection and accurate channel estimation compared with state-of-the-art compressed sensing based JADCE techniques.
基金supported by National Natural Science Foundation of China(NSFC)under Grant No.62001190The work of J.Wen was supported by NSFC(Nos.11871248,61932010,61932011)+3 种基金the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019),Guangdong Major Project of Basic and Applied Basic Research(2019B030302008)the Fundamental Research Funds for the Central Universities(No.21618329)The work of P.Fan was supported by National Key R&D Project(No.2018YFB1801104)NSFC Project(No.6202010600).
文摘This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.
基金supported by National Key Research and Development Program of China under Grants 2021YFB1600500,2021YFB3201502,and 2022YFB3207704Natural Science Foundation of China(NSFC)under Grants U2233216,62071044,61827901,62088101 and 62201056+1 种基金supported by Shandong Province Natural Science Foundation under Grant ZR2022YQ62supported by Beijing Nova Program,Beijing Institute of Technology Research Fund Program for Young Scholars under grant XSQD-202121009.
文摘The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.
