In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was ...In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the eigenbases.Extensive measurements were carried out to evaluate the performance of this proposed model.Furthermore,the physical implications of this model were illustrated and the capacities are analyzed.In addition,the azimuthal power spectrum(APS)of several models was analyzed.Finally,the channel hardening effect was simulated and discussed.Results showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger model.Moreover,the proposed model can provide better tradeoff between accuracy and complexity in channel synthesis.This CIRM model can be used for massive MIMO design in the future communication system design.展开更多
Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capa...Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.展开更多
In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem i...In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem is formulated under the constraints of maximum power and minimum rate of each user.Then,we develop a near-optimal power allocation(PA)scheme by using the successive convex approximation(SCA)method,Lagrange multiplier method,and block coordinate descent(BCD)method,and it can obtain almost the same SE as the benchmark scheme with lower complexity.Since this scheme needs three-layer iteration,a suboptimal PA scheme is developed to further reduce the complexity,where the characteristic of massive MIMO(i.e.,numerous receive antennas)is utilized for convex reformulation,and the rate constraint is converted to linear constraints.This suboptimal scheme only needs single-layer iteration,thus has lower complexity than the near-optimal scheme.Finally,we joint design the pilot power and data power to further improve the performance,and propose an two-stage algorithm to obtain joint PA.Simulation results verify the effectiveness of the proposed schemes,and superior SE performance is achieved.展开更多
This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst inte...This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.展开更多
In this paper,ambient IoT is used as a typical use case of massive connections for the sixth generation(6G)mobile communications where we derive the performance requirements to facilitate the evaluation of technical s...In this paper,ambient IoT is used as a typical use case of massive connections for the sixth generation(6G)mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions.A rather complete design of unsourced multiple access is proposed in which two key parts:a compressed sensing module for active user detection,and a sparse interleaver-division multiple access(SIDMA)module are simulated side by side on a same platform at balanced signal to noise ratio(SNR)operating points.With a proper combination of compressed sensing matrix,a convolutional encoder,receiver algorithms,the simulated performance results appear superior to the state-of-the-art benchmark,yet with relatively less complicated processing.展开更多
Bakwanga kimberlite massive 5 in Kasai Oriental is part of a set of 13 kimberlite massives numbered according to the order in which they were discovered. They are located on an alignment with a more or less W-E direct...Bakwanga kimberlite massive 5 in Kasai Oriental is part of a set of 13 kimberlite massives numbered according to the order in which they were discovered. They are located on an alignment with a more or less W-E direction making up the Northern group known as Bakwanga. The importance of the Bakwanga kimberlite massives on the country’s economy in the production of diamonds sufficiently proves the interest of geological research work in this area. The objective of this work is to determine a mathematical model of the shape of the massive as close as possible to reality and through cartography. The cartographic study and modeling of this kimberlite massive were carried out using data from core samples taken on longitudinal and transverse profiles of the 50 × 50 meter mesh drilling plan intersecting this kimberlite massive. We intend to deduce the structure and lithostratigraphy of the kim-berlitic facies and the direct environment of massive 5. As a result, we note that the majority of surveys on the extent of this massive have intersected: Red clayey sand - Polymorphic sandstone - Nodular sandstone, with kaolin blocks and nodules - Epiclastic Kimberlite - Xenokimberlite - Massive Kimberlite - Mesozoic sandstone - Dolomite (enclosing). The shape of the Massive 5 model is vaguely elliptical with a W-E longitudinal axis of 575 m and N-S axis of 275 meters. Surveys have shown that Massive 5 is in fact composed of two pipes, located in the W (western pipe) and E (eastern pipe) ends of the massif. The two chimneys of the two pipes have walls ranging from subvertical at the eastern pipe to very steep walls of around 70˚ to 80˚ for the western pipe and the average diameter of the two pipes is ±50 meters. At level 600, the massive has an area of ±10.5 hectares and it gradually decreases in depth and the modeling of the latter shows a concentric decrease in the volume of the massive from the surface to depth in the shape of a mushroom. 3 eruptive phases established this Kimber-litic massive, the first two phases (old) of which formed the crater of the western pipe and the third formed the crater of the eastern pipe in the dolomites. These dolomites constitute everywhere the surrounding area of the massive;the distinction of these 3 phases is made possible thanks to Epiclastic deposits, Xenokim-berlites and massive Kimberlites.展开更多
Unsourced random access(URA)is a new perspective of massive access which aims at supporting numerous machine-type users.With the appearance of carrier frequency offset(CFO),joint activity detection and channel estimat...Unsourced random access(URA)is a new perspective of massive access which aims at supporting numerous machine-type users.With the appearance of carrier frequency offset(CFO),joint activity detection and channel estimation,which is vital for multiple-input and multiple-output URA,is a challenging task.To handle the phase corruption of channel measurements under CFO,a novel compressed sensing algorithm is proposed,leveraging the parametric bilinear generalized approximate message passing framework with a Markov chain support model that captures the block sparsity structure of the considered angular domain channel.An uncoupled transmission scheme is proposed to reduce system complexity,where slot-emitted messages are reorganized relying on clustering unique user channels.Simulation results reveal that the proposed transmission design for URA under CFO outperforms other potential methods.展开更多
Ultra-reliable and low-latency communication(URLLC)is still in the early stage of research due to its two strict and conflicting requirements,i.e.,ultra-low latency and ultra-high reliability,and its impact on securit...Ultra-reliable and low-latency communication(URLLC)is still in the early stage of research due to its two strict and conflicting requirements,i.e.,ultra-low latency and ultra-high reliability,and its impact on security performance is still unclear.Specifically,short-packet communication is expected to meet the delay requirement of URLLC,while the degradation of reliability caused by it makes traditional physical-layer security metrics not applicable.In this paper,we investigate the secure short-packet transmission in uplink massive multiuser multiple-inputmultiple-output(MU-MIMO)system under imperfect channel state information(CSI).We propose an artificial noise scheme to improve the security performance of the system and use the system average secrecy throughput(AST)as the analysis metric.We derive the approximate closed-form expression of the system AST and further analyze the system asymptotic performance in two regimes.Furthermore,a one-dimensional search method is used to optimize the maximum system AST for a given pilot length.Numerical results verify the correctness of theoretical analysis,and show that there are some parameters that affect the tradeoff between security and latency.Moreover,appropriately increasing the number of antennas at the base station(BS)and transmission power at user devices(UDs)can increase the system AST to achieve the required threshold.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
Dear editor,Acute massive pulmonary embolism(PE) is a common life-threatening disease with high mortality of up to 30%–50%.[1,2]Potential heterogeneous reasons for PE remain controversial, and its treatment strategie...Dear editor,Acute massive pulmonary embolism(PE) is a common life-threatening disease with high mortality of up to 30%–50%.[1,2]Potential heterogeneous reasons for PE remain controversial, and its treatment strategies mainly include antithrombotics, fibrinolytics, and embolectomy.展开更多
Connected and autonomous vehicle(CAV)vehicle to infrastructure(V2I)scenarios have more stringent requirements on the communication rate,delay,and reliability of the Internet of vehicles(Io V).New radio vehicle to ever...Connected and autonomous vehicle(CAV)vehicle to infrastructure(V2I)scenarios have more stringent requirements on the communication rate,delay,and reliability of the Internet of vehicles(Io V).New radio vehicle to everything(NR-V2X)adopts link adaptation(LA)to improve the efficiency and reliability of road safety information transmission.In order to solve the problem that the existing LA scheduling algorithms cannot adapt to the Doppler shift and complex fast time-varying channel in V2I scenario,resulting in low reliability of information transmission,this paper proposes a deep Q-learning(DQL)-based massive multiple-input multiple-output(MIMO)LA scheduling algorithm for autonomous driving V2I scenario.The algorithm combines deep neural network(DNN)with Q-learning(QL)algorithm,which is used for joint scheduling of modulation and coding scheme(MCS)and space division multiplexing(SDM).The system simulation results show that the algorithm proposed in this paper can fully adapt to the different channel environment in the V2I scenario,and select the optimal MCS and SDM for the transmission of road safety information,thereby the accuracy of road safety information transmission is improved,collision accidents can be avoided,and bring a good autonomous driving experience.展开更多
Grant-free random access(RA)is attractive for future network due to the minimized access delay.In this paper,we investigate the grantfree RA in multicell massive multiple-input multipleoutput(MIMO)systems with pilot r...Grant-free random access(RA)is attractive for future network due to the minimized access delay.In this paper,we investigate the grantfree RA in multicell massive multiple-input multipleoutput(MIMO)systems with pilot reuse.With backoff mechanism,user equipments(UEs)in each cell are randomly activated,and active UEs randomly select orthogonal pilots from a predefined pilot pool,which results in a random pilot contamination among cells.With the help of indicators that capture the uncertainties of UE activation and pilot selection,we derive a closed-form approximation of the spectral efficiency per cell after averaging over the channel fading as well as UEs’random behaviors.Based on the analysis,the optimal backoff parameter and pilot length that maximize the spectral efficiency can be obtained.We find that the backoff mechanism is necessary for the system with large number of UEs,as it can bring significant gains on the spectral efficiency.Moreover,as UE number grows,more backoff time is needed.展开更多
This paper proposes the alternating direction method of multipliers-based infinity-norm(ADMIN) with threshold(ADMIN-T) and with percentage(ADMIN-P) detection algorithms,which make full use of the distribution of the s...This paper proposes the alternating direction method of multipliers-based infinity-norm(ADMIN) with threshold(ADMIN-T) and with percentage(ADMIN-P) detection algorithms,which make full use of the distribution of the signal to interference plus noise ratio(SINR) for an uplink massive MIMO system.The ADMIN-T and ADMIN-P detection algorithms are improved visions of the ADMIN detection algorithm,in which an appropriate SINR threshold in the ADMIN-T detection algorithm and a certain percentage in the ADMIN-P detection algorithm are designed to reduce the overall computational complexity.The detected symbols are divided into two parts by the SINR threshold which is based on the cumulative probability density function(CDF) of SINR and a percentage,respectively.The symbols in higher SINR part are detected by MMSE.The interference of these symbols is then cancelled by successive interference cancellation(SIC).Afterwards the remaining symbols with low SINR are iteratively detected by ADMIN.The simulation results show that the ADMIIN-T and the ADMIN-P detection algorithms provide a significant performance gain compared with some recently proposed detection algorithms.In addition,the computational complexity of ADMIN-T and ADMIN-P are significantly reduced.Furthermore,in the case of same number of transceiver antennas,the proposed algorithms have a higher performance compared with the case of asymmetric transceiver antennas.展开更多
In this article,novel emulation strategies for the sectored multiple probe anechoic chamber(SMPAC)are proposed to enable the reliable evaluation of the massive multiple-input multiple-output(MIMO)device operating at b...In this article,novel emulation strategies for the sectored multiple probe anechoic chamber(SMPAC)are proposed to enable the reliable evaluation of the massive multiple-input multiple-output(MIMO)device operating at beamforming mode,which requires a realistic non-stationary channel environment.For the dynamic propagation emulation,an efficient closed-form probe weighting strategy minimizing the power angular spectrum(PAS)emulation errors is derived,substantially reducing the associated computational complexity.On the other hand,a novel probe selection algorithm is proposed to reproduce a more accurate fading environment.Various standard channel models and setup configurations are comprehensively simulated to validate the capacity of the proposed methods.The simulation results show that more competent active probes are selected with the proposed method compared to the conventional algorithms.Furthermore,the derived closedform probe weighting strategy offers identical accuracy to that obtained with complicated numerical optimization.Moreover,a realistic dynamic channel measured in an indoor environment is reconstructed with the developed methodologies,and 95.6%PAS similarity can be achieved with 6 active probes.The satisfactory results demonstrate that the proposed algorithms are suitable for arbitrary channel emulation.展开更多
In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve th...In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation.The optimization algorithms such as Gradient Descent(GD)method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation.However,they converge slowly when the condition number of the MMSE filtering matrix(the coefficient matrix)increases,which can compromise the efficiency of their implementation.Moreover,their soft information computation also involves a large-scale matrix-matrix multiplication operation.In this paper,a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent(APGD-SOD)method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems.In the proposed detector,an Adaptive Pre-conditioner(AP)matrix obtained through the Quasi-Newton Symmetric Rank One(QN-SR1)update in each iteration is used to accelerate the convergence of the GD method.The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix,since then the condition number would be close to unity and the convergence would be rapid.By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems,the computational complexity of the soft information is simplified.The proposed AP matrix is applied to the GD method as a showcase.However,it also can be used by Conjugate Gradient(CG)method due to its generality.It is demonstrated that the proposed detector is robust and its convergence rate is superlinear.Simulation results show that the proposed detector converges at most four iterations.Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation,which entails a big challenge for the efficient implementation.展开更多
In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)syst...In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)systems.Specifically,we use orthogonal approximate message passing(OAMP)technique to develop OAMPNet,which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples.For OAMPNet,the prior probability of the transmit signal has a significant impact on the obtainable performance.For this reason,in our design,we first derive the prior probability of transmitting signals on each antenna for SDIMMIMO systems,which is different from the conventional massive MIMO systems.Then,for massive MIMO scenarios,we propose two novel algorithms to avoid pre-storing all active antenna combinations,thus considerably improving the memory efficiency and reducing the related overhead.Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.展开更多
Network-assisted full duplex(NAFD)cellfree(CF)massive MIMO has drawn increasing attention in 6G evolvement.In this paper,we build an NAFD CF system in which the users and access points(APs)can flexibly select their du...Network-assisted full duplex(NAFD)cellfree(CF)massive MIMO has drawn increasing attention in 6G evolvement.In this paper,we build an NAFD CF system in which the users and access points(APs)can flexibly select their duplex modes to increase the link spectral efficiency.Then we formulate a joint flexible duplexing and power allocation problem to balance the user fairness and system spectral efficiency.We further transform the problem into a probability optimization to accommodate the shortterm communications.In contrast with the instant performance optimization,the probability optimization belongs to a sequential decision making problem,and thus we reformulate it as a Markov Decision Process(MDP).We utilizes deep reinforcement learning(DRL)algorithm to search the solution from a large state-action space,and propose an asynchronous advantage actor-critic(A3C)-based scheme to reduce the chance of converging to the suboptimal policy.Simulation results demonstrate that the A3C-based scheme is superior to the baseline schemes in term of the complexity,accumulated log spectral efficiency,and stability.展开更多
At present,the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity.The bacterial foraging optimization(BFO)-based algorithm has been applied in w...At present,the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity.The bacterial foraging optimization(BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability.But the BFO-based algorithm is easy to fall into local optimum.Therefore,this paper proposes the quantum bacterial foraging optimization(QBFO)-binary orthogonal matching pursuit(BOMP)channel estimation algorithm to the problem of local optimization.Firstly,the binary matrix is constructed according to whether atoms are selected or not.And the support set of the sparse signal is recovered according to the BOMP-based algorithm.Then,the QBFO-based algorithm is used to obtain the estimated channel matrix.The optimization function of the least squares method is taken as the fitness function.Based on the communication between the quantum bacteria and the fitness function value,chemotaxis,reproduction and dispersion operations are carried out to update the bacteria position.Simulation results showthat compared with other algorithms,the estimationmechanism based onQBFOBOMP algorithm can effectively improve the channel estimation performance of millimeter wave(mmWave)massive multiple input multiple output(MIMO)systems.Meanwhile,the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity.展开更多
基金supported by the Key R&D Project of Jiangsu Province(Modern Agriculture)under Grant BE2022322 the"Pilot Plan"Internet of Things special project(China Institute of Io T(wuxi)and Wuxi Internet of Things Innovation Promotion Center)under Grant 2022SP-T16-Bin part by the 111 Project under Grant B12018+2 种基金in part by the Six talent peaks project in Jiangsu Provincein part by the open foundation of Key Laboratory of Wireless Sensor Network and Communication,Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences under Grant 20190917in part by the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications,Ministry of Education)。
文摘In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the eigenbases.Extensive measurements were carried out to evaluate the performance of this proposed model.Furthermore,the physical implications of this model were illustrated and the capacities are analyzed.In addition,the azimuthal power spectrum(APS)of several models was analyzed.Finally,the channel hardening effect was simulated and discussed.Results showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger model.Moreover,the proposed model can provide better tradeoff between accuracy and complexity in channel synthesis.This CIRM model can be used for massive MIMO design in the future communication system design.
基金supported in part by the Beijing Natural Science Foundation under Grant No.L202003the National Natural Science Foundation of China under Grant U22B2001 and 62271065the Project of China Railway Corporation under Grant N2022G048.
文摘Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.
基金supported by the Fundamental Research Funds for the Central Universities of NUAA(No.kfjj20200414)Natural Science Foundation of Jiangsu Province in China(No.BK20181289).
文摘In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem is formulated under the constraints of maximum power and minimum rate of each user.Then,we develop a near-optimal power allocation(PA)scheme by using the successive convex approximation(SCA)method,Lagrange multiplier method,and block coordinate descent(BCD)method,and it can obtain almost the same SE as the benchmark scheme with lower complexity.Since this scheme needs three-layer iteration,a suboptimal PA scheme is developed to further reduce the complexity,where the characteristic of massive MIMO(i.e.,numerous receive antennas)is utilized for convex reformulation,and the rate constraint is converted to linear constraints.This suboptimal scheme only needs single-layer iteration,thus has lower complexity than the near-optimal scheme.Finally,we joint design the pilot power and data power to further improve the performance,and propose an two-stage algorithm to obtain joint PA.Simulation results verify the effectiveness of the proposed schemes,and superior SE performance is achieved.
基金supported by the National Key Laboratory of Wireless Communications Foundation,China (IFN20230204)。
文摘This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.
文摘In this paper,ambient IoT is used as a typical use case of massive connections for the sixth generation(6G)mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions.A rather complete design of unsourced multiple access is proposed in which two key parts:a compressed sensing module for active user detection,and a sparse interleaver-division multiple access(SIDMA)module are simulated side by side on a same platform at balanced signal to noise ratio(SNR)operating points.With a proper combination of compressed sensing matrix,a convolutional encoder,receiver algorithms,the simulated performance results appear superior to the state-of-the-art benchmark,yet with relatively less complicated processing.
文摘Bakwanga kimberlite massive 5 in Kasai Oriental is part of a set of 13 kimberlite massives numbered according to the order in which they were discovered. They are located on an alignment with a more or less W-E direction making up the Northern group known as Bakwanga. The importance of the Bakwanga kimberlite massives on the country’s economy in the production of diamonds sufficiently proves the interest of geological research work in this area. The objective of this work is to determine a mathematical model of the shape of the massive as close as possible to reality and through cartography. The cartographic study and modeling of this kimberlite massive were carried out using data from core samples taken on longitudinal and transverse profiles of the 50 × 50 meter mesh drilling plan intersecting this kimberlite massive. We intend to deduce the structure and lithostratigraphy of the kim-berlitic facies and the direct environment of massive 5. As a result, we note that the majority of surveys on the extent of this massive have intersected: Red clayey sand - Polymorphic sandstone - Nodular sandstone, with kaolin blocks and nodules - Epiclastic Kimberlite - Xenokimberlite - Massive Kimberlite - Mesozoic sandstone - Dolomite (enclosing). The shape of the Massive 5 model is vaguely elliptical with a W-E longitudinal axis of 575 m and N-S axis of 275 meters. Surveys have shown that Massive 5 is in fact composed of two pipes, located in the W (western pipe) and E (eastern pipe) ends of the massif. The two chimneys of the two pipes have walls ranging from subvertical at the eastern pipe to very steep walls of around 70˚ to 80˚ for the western pipe and the average diameter of the two pipes is ±50 meters. At level 600, the massive has an area of ±10.5 hectares and it gradually decreases in depth and the modeling of the latter shows a concentric decrease in the volume of the massive from the surface to depth in the shape of a mushroom. 3 eruptive phases established this Kimber-litic massive, the first two phases (old) of which formed the crater of the western pipe and the third formed the crater of the eastern pipe in the dolomites. These dolomites constitute everywhere the surrounding area of the massive;the distinction of these 3 phases is made possible thanks to Epiclastic deposits, Xenokim-berlites and massive Kimberlites.
基金supported by the ZTE Industry⁃University⁃Institute Cooperation Funds under Grant No.HC⁃CN⁃20201116001.
文摘Unsourced random access(URA)is a new perspective of massive access which aims at supporting numerous machine-type users.With the appearance of carrier frequency offset(CFO),joint activity detection and channel estimation,which is vital for multiple-input and multiple-output URA,is a challenging task.To handle the phase corruption of channel measurements under CFO,a novel compressed sensing algorithm is proposed,leveraging the parametric bilinear generalized approximate message passing framework with a Markov chain support model that captures the block sparsity structure of the considered angular domain channel.An uncoupled transmission scheme is proposed to reduce system complexity,where slot-emitted messages are reorganized relying on clustering unique user channels.Simulation results reveal that the proposed transmission design for URA under CFO outperforms other potential methods.
基金supported by the National Key R&D Program of China under Grant 2018YFB1801103the National Natural Science Foundation of China under Grant(no.62171464,no.62122094)。
文摘Ultra-reliable and low-latency communication(URLLC)is still in the early stage of research due to its two strict and conflicting requirements,i.e.,ultra-low latency and ultra-high reliability,and its impact on security performance is still unclear.Specifically,short-packet communication is expected to meet the delay requirement of URLLC,while the degradation of reliability caused by it makes traditional physical-layer security metrics not applicable.In this paper,we investigate the secure short-packet transmission in uplink massive multiuser multiple-inputmultiple-output(MU-MIMO)system under imperfect channel state information(CSI).We propose an artificial noise scheme to improve the security performance of the system and use the system average secrecy throughput(AST)as the analysis metric.We derive the approximate closed-form expression of the system AST and further analyze the system asymptotic performance in two regimes.Furthermore,a one-dimensional search method is used to optimize the maximum system AST for a given pilot length.Numerical results verify the correctness of theoretical analysis,and show that there are some parameters that affect the tradeoff between security and latency.Moreover,appropriately increasing the number of antennas at the base station(BS)and transmission power at user devices(UDs)can increase the system AST to achieve the required threshold.
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.
基金This work was partly supported by grant from the Youth Fund of National Natural Science Foundation of China(81800441)Start-up Fund from Shanghai Fourth People’s Hospital Affiliated to Tongji University,Clinical Science and Technology Innovation Project of Shanghai Hospital Development Center,Construction and Application of Emergency and Critical Care Integrated Management Mode(SHDC22021211).
文摘Dear editor,Acute massive pulmonary embolism(PE) is a common life-threatening disease with high mortality of up to 30%–50%.[1,2]Potential heterogeneous reasons for PE remain controversial, and its treatment strategies mainly include antithrombotics, fibrinolytics, and embolectomy.
基金supported by the Natural Science Foundation of Chongqing(No.cstc2019jcyjmsxmX0017)。
文摘Connected and autonomous vehicle(CAV)vehicle to infrastructure(V2I)scenarios have more stringent requirements on the communication rate,delay,and reliability of the Internet of vehicles(Io V).New radio vehicle to everything(NR-V2X)adopts link adaptation(LA)to improve the efficiency and reliability of road safety information transmission.In order to solve the problem that the existing LA scheduling algorithms cannot adapt to the Doppler shift and complex fast time-varying channel in V2I scenario,resulting in low reliability of information transmission,this paper proposes a deep Q-learning(DQL)-based massive multiple-input multiple-output(MIMO)LA scheduling algorithm for autonomous driving V2I scenario.The algorithm combines deep neural network(DNN)with Q-learning(QL)algorithm,which is used for joint scheduling of modulation and coding scheme(MCS)and space division multiplexing(SDM).The system simulation results show that the algorithm proposed in this paper can fully adapt to the different channel environment in the V2I scenario,and select the optimal MCS and SDM for the transmission of road safety information,thereby the accuracy of road safety information transmission is improved,collision accidents can be avoided,and bring a good autonomous driving experience.
基金supported in part by the National Natural Science Foundation of China under Grant 62171231 and 62071247in part by the National Key Research & Development Program of China under Grant No. 2020YFB1807202 and 2020YFB1804900
文摘Grant-free random access(RA)is attractive for future network due to the minimized access delay.In this paper,we investigate the grantfree RA in multicell massive multiple-input multipleoutput(MIMO)systems with pilot reuse.With backoff mechanism,user equipments(UEs)in each cell are randomly activated,and active UEs randomly select orthogonal pilots from a predefined pilot pool,which results in a random pilot contamination among cells.With the help of indicators that capture the uncertainties of UE activation and pilot selection,we derive a closed-form approximation of the spectral efficiency per cell after averaging over the channel fading as well as UEs’random behaviors.Based on the analysis,the optimal backoff parameter and pilot length that maximize the spectral efficiency can be obtained.We find that the backoff mechanism is necessary for the system with large number of UEs,as it can bring significant gains on the spectral efficiency.Moreover,as UE number grows,more backoff time is needed.
基金This work was supported in part by the National Natural Science Foundation of China(NSFC)under grant numbers 61671047,61775015 and U2006217.
文摘This paper proposes the alternating direction method of multipliers-based infinity-norm(ADMIN) with threshold(ADMIN-T) and with percentage(ADMIN-P) detection algorithms,which make full use of the distribution of the signal to interference plus noise ratio(SINR) for an uplink massive MIMO system.The ADMIN-T and ADMIN-P detection algorithms are improved visions of the ADMIN detection algorithm,in which an appropriate SINR threshold in the ADMIN-T detection algorithm and a certain percentage in the ADMIN-P detection algorithm are designed to reduce the overall computational complexity.The detected symbols are divided into two parts by the SINR threshold which is based on the cumulative probability density function(CDF) of SINR and a percentage,respectively.The symbols in higher SINR part are detected by MMSE.The interference of these symbols is then cancelled by successive interference cancellation(SIC).Afterwards the remaining symbols with low SINR are iteratively detected by ADMIN.The simulation results show that the ADMIIN-T and the ADMIN-P detection algorithms provide a significant performance gain compared with some recently proposed detection algorithms.In addition,the computational complexity of ADMIN-T and ADMIN-P are significantly reduced.Furthermore,in the case of same number of transceiver antennas,the proposed algorithms have a higher performance compared with the case of asymmetric transceiver antennas.
基金supported by National Natural Science Foundation of China(No.62090015,No.61821001)BUPT Excellent Ph.D.Students Foundation under Grant(CX2021216)。
文摘In this article,novel emulation strategies for the sectored multiple probe anechoic chamber(SMPAC)are proposed to enable the reliable evaluation of the massive multiple-input multiple-output(MIMO)device operating at beamforming mode,which requires a realistic non-stationary channel environment.For the dynamic propagation emulation,an efficient closed-form probe weighting strategy minimizing the power angular spectrum(PAS)emulation errors is derived,substantially reducing the associated computational complexity.On the other hand,a novel probe selection algorithm is proposed to reproduce a more accurate fading environment.Various standard channel models and setup configurations are comprehensively simulated to validate the capacity of the proposed methods.The simulation results show that more competent active probes are selected with the proposed method compared to the conventional algorithms.Furthermore,the derived closedform probe weighting strategy offers identical accuracy to that obtained with complicated numerical optimization.Moreover,a realistic dynamic channel measured in an indoor environment is reconstructed with the developed methodologies,and 95.6%PAS similarity can be achieved with 6 active probes.The satisfactory results demonstrate that the proposed algorithms are suitable for arbitrary channel emulation.
基金supported by National Natural Science Foundation of China under Grant 61501072 and 61701062Chongqing Research Program of Basic Research and Frontier Technology under Grant cstc2019jcyj-msxmX0079Program for Changjiang Scholars and Innovative Research Team in University under Grant IRT16R72.
文摘In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation.The optimization algorithms such as Gradient Descent(GD)method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation.However,they converge slowly when the condition number of the MMSE filtering matrix(the coefficient matrix)increases,which can compromise the efficiency of their implementation.Moreover,their soft information computation also involves a large-scale matrix-matrix multiplication operation.In this paper,a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent(APGD-SOD)method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems.In the proposed detector,an Adaptive Pre-conditioner(AP)matrix obtained through the Quasi-Newton Symmetric Rank One(QN-SR1)update in each iteration is used to accelerate the convergence of the GD method.The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix,since then the condition number would be close to unity and the convergence would be rapid.By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems,the computational complexity of the soft information is simplified.The proposed AP matrix is applied to the GD method as a showcase.However,it also can be used by Conjugate Gradient(CG)method due to its generality.It is demonstrated that the proposed detector is robust and its convergence rate is superlinear.Simulation results show that the proposed detector converges at most four iterations.Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation,which entails a big challenge for the efficient implementation.
基金supported by the National Natural Science Foundation of China under Grant U19B2014the Sichuan Science and Technology Program under Grant 2023NSFSC0457the Fundamental Research Funds for the Central Universities under Grant 2242022k60006.
文摘In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)systems.Specifically,we use orthogonal approximate message passing(OAMP)technique to develop OAMPNet,which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples.For OAMPNet,the prior probability of the transmit signal has a significant impact on the obtainable performance.For this reason,in our design,we first derive the prior probability of transmitting signals on each antenna for SDIMMIMO systems,which is different from the conventional massive MIMO systems.Then,for massive MIMO scenarios,we propose two novel algorithms to avoid pre-storing all active antenna combinations,thus considerably improving the memory efficiency and reducing the related overhead.Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.
基金supported by the National Key R&D Program of China under Grant 2020YFB1807204the BUPT Excellent Ph.D.Students Foundation under Grant CX2022306。
文摘Network-assisted full duplex(NAFD)cellfree(CF)massive MIMO has drawn increasing attention in 6G evolvement.In this paper,we build an NAFD CF system in which the users and access points(APs)can flexibly select their duplex modes to increase the link spectral efficiency.Then we formulate a joint flexible duplexing and power allocation problem to balance the user fairness and system spectral efficiency.We further transform the problem into a probability optimization to accommodate the shortterm communications.In contrast with the instant performance optimization,the probability optimization belongs to a sequential decision making problem,and thus we reformulate it as a Markov Decision Process(MDP).We utilizes deep reinforcement learning(DRL)algorithm to search the solution from a large state-action space,and propose an asynchronous advantage actor-critic(A3C)-based scheme to reduce the chance of converging to the suboptimal policy.Simulation results demonstrate that the A3C-based scheme is superior to the baseline schemes in term of the complexity,accumulated log spectral efficiency,and stability.
基金supported by the National Natural Science Foundation of China(Nos.61861015,62061013 and 61961013)Key Research and Development Program of Hainan Province(No.ZDYF2019011)+3 种基金National Key Research and Development Program of China(No.2019CXTD400)Young Elite Scientists Sponsorship Program by CAST(No.2018QNRC001)Scientific Research Setup Fund of Hainan University(No.KYQD(ZR)1731)the Natural Science Foundation High-Level Talent Project of Hainan Province(No.622RC619).
文摘At present,the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity.The bacterial foraging optimization(BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability.But the BFO-based algorithm is easy to fall into local optimum.Therefore,this paper proposes the quantum bacterial foraging optimization(QBFO)-binary orthogonal matching pursuit(BOMP)channel estimation algorithm to the problem of local optimization.Firstly,the binary matrix is constructed according to whether atoms are selected or not.And the support set of the sparse signal is recovered according to the BOMP-based algorithm.Then,the QBFO-based algorithm is used to obtain the estimated channel matrix.The optimization function of the least squares method is taken as the fitness function.Based on the communication between the quantum bacteria and the fitness function value,chemotaxis,reproduction and dispersion operations are carried out to update the bacteria position.Simulation results showthat compared with other algorithms,the estimationmechanism based onQBFOBOMP algorithm can effectively improve the channel estimation performance of millimeter wave(mmWave)massive multiple input multiple output(MIMO)systems.Meanwhile,the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity.
