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
Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which ent...Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.展开更多
How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to t...How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to the large number of antennas. To reduce the overwhelming pilot overhead, a hybrid orthogonal and non-orthogonal pilot distribution at the base station(BS),which is a generalization of the existing pilot distribution scheme,is proposed by exploiting the common sparsity of channel due to the compact antenna arrangement. Then the block sparsity for antennas with hybrid pilot distribution is derived respectively and can be used to obtain channel impulse response. By employing the theoretical analysis of block sparse recovery, the total coherence criterion is proposed to optimize the sensing matrix composed by orthogonal pilots. Due to the huge complexity of optimal pilot acquisition, a genetic algorithm based pilot allocation(GAPA) algorithm is proposed to acquire optimal pilot distribution locations with fast convergence. Furthermore, the Cramer Rao lower bound is derived for non-orthogonal pilot-based channel estimation and can be asymptotically approached by the prior support set, especially when the optimized pilot is employed.展开更多
Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar sys...Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound(PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization(MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search(ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.展开更多
Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at...Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error(MMSE) signal detection using the accelerated overrelaxation(AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.展开更多
This paper presents a co-time co-frequency fullduplex(CCFD)massive multiple-input multiple-output(MIMO)system to meet high spectrum efficiency requirements for beyond the fifth-generation(5G)and the forthcoming the si...This paper presents a co-time co-frequency fullduplex(CCFD)massive multiple-input multiple-output(MIMO)system to meet high spectrum efficiency requirements for beyond the fifth-generation(5G)and the forthcoming the sixth-generation(6G)networks.To achieve equilibrium of energy consumption,system resource utilization,and overall transmission capacity,an energy-efficient resource management strategy concerning power allocation and antenna selection is designed.A continuous quantum-inspired termite colony optimization(CQTCO)algorithm is proposed as a solution to the resource management considering the communication reliability while promoting energy conservation for the CCFD massive MIMO system.The effectiveness of CQTCO compared with other algorithms is evaluated through simulations.The results reveal that the proposed resource management scheme under CQTCO can obtain a superior performance in different communication scenarios,which can be considered as an eco-friendly solution for promoting reliable and efficient communication in future wireless networks.展开更多
A joint resource allocation scheme concerned with the sensor subset,power and bandwidth for range-only target tracking in multiple-input multiple-output(MIMO)radar systems is proposed.By selecting an optimal subset of...A joint resource allocation scheme concerned with the sensor subset,power and bandwidth for range-only target tracking in multiple-input multiple-output(MIMO)radar systems is proposed.By selecting an optimal subset of sensors with the predetermined size and implementing the power allocation and bandwidth strategies among them,this algorithm can help achieving a better performance within the same resource constraints.Firstly,the Bayesian Cramer-Rao bound(BCRB)is derived from it.Secondly,a criterion for minimizing the BCRB at the target location among all targets tracking in a certain range is derived.Thirdly,the optimization problem involved with three variable vectors is formulated,which can be simplified by deriving the relationship between the optimal power allocation vector and the bandwidth allocation vector.Then,the simplified optimization problem is solved by the cyclic minimization algorithm incorporated with the sequential parametric convex approximation(SPCA)algorithm.Finally,the validity of the proposed method is demonstrated with simulation results.展开更多
A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output ( MIMO ) orthogonal frequency division multiplexing ( OFDM ) systems. Based on the basis expansion mo...A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output ( MIMO ) orthogonal frequency division multiplexing ( OFDM ) systems. Based on the basis expansion model (BEM) of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing (DCS) problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing (CS) theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.展开更多
Introducing frequency agility into a distributed multipleinput multiple-output(MIMO)radar can significantly enhance its anti-jamming ability.However,it would cause the sidelobe pedestal problem in multi-target paramet...Introducing frequency agility into a distributed multipleinput multiple-output(MIMO)radar can significantly enhance its anti-jamming ability.However,it would cause the sidelobe pedestal problem in multi-target parameter estimation.Sparse recovery is an effective way to address this problem,but it cannot be directly utilized for multi-target parameter estimation in frequency-agile distributed MIMO radars due to spatial diversity.In this paper,we propose an algorithm for multi-target parameter estimation according to the signal model of frequency-agile distributed MIMO radars,by modifying the orthogonal matching pursuit(OMP)algorithm.The effectiveness of the proposed method is then verified by simulation results.展开更多
Pilot contamination can bring up a grave impairment in the performance of massive multiple-input multiple-output(MIMO)systems.In this paper,an improved time-shifted pilot scheme is proposed to reduce the pilot contami...Pilot contamination can bring up a grave impairment in the performance of massive multiple-input multiple-output(MIMO)systems.In this paper,an improved time-shifted pilot scheme is proposed to reduce the pilot contamination,where orthogonal pilots are employed in the same group to eliminate the residual intragroup interference existing in the original time-shifted pilot scheme.Meanwhile,the rigorous closed-form expressions of both downlink and uplink transmission rates with a finite number of antennas are derived,and it is shown that the intra-group interference can be completely eliminated by the proposed scheme.Simulation results demonstrate that both downlink and uplink transmission rates are significantly improved by employing the proposed scheme.展开更多
Location-based services have become an important part of the daily life.Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich ...Location-based services have become an important part of the daily life.Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich scattering environment.In this paper,a single-site multiple-input multiple-output(MIMO)orthogonal frequency division multiplexing(OFDM)system is modeled,from which an angle delay channel power matrix(ADCPM)is extracted.Considering the changing environment,auto encoders are used to generate new fingerprints based on ADCPM fingerprints to improve the robustness of the fingerprints.When the scattering environment has changed beyond a certain extent,the robustness will not be able to make up for the positioning error.Under this circumstance,an updating of the fingerprint database is imperative.A new fingerprint database updating algorithm which combines a new clustering method and an updating rule based on probability is proposed.Simulation results show the desirable performance of the proposed methods.展开更多
The mobile data traffic has been exponentially growing during the last several decades.This was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UD...The mobile data traffic has been exponentially growing during the last several decades.This was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service variations.Cell-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this issue.In such systems,each User Equipment(UE)is served by a preferred set of surrounding APs cooperatively.In this paper,we provide a survey of the state-of-the-art literature on CF mMIMO.As a starting point,the significance and the basic properties of CF mMIMO are highlighted.We then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and algorithms.After that,we discuss the practical issues in implementing CF mMIMO and point out the potential future directions.Finally,we conclude this paper with a summary of the key lessons learned in this field.展开更多
This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station(BS)is equipped with a very large number of antennas(also referred to as"ma...This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station(BS)is equipped with a very large number of antennas(also referred to as"massive multiple-input multiple-output(MIMO)").We consider a time-division duplexing(TDD)scheme,in which reciprocity between the uplink and downlink channels can be assumed.Channel estimation is essential for downlink beamforming in massive MIMO,nevertheless,the pilot contamination effect hinders accurate channel estimation,which leads to overall performance degradation.Benefitted from the asymptotic orthogonality between signal and interference subspaces for non-overlapping angle-of arrivals(AOAs)in the large-scale antenna system,we propose a multiple signals classification(MUSIC)based channel estimation algorithm during the uplink transmission.Analytical and numerical results verify complete pilot decontamination and the effectiveness of the proposed channel estimation algorithm in the multiuser multi-cell massive MIMO system.展开更多
Based on massive MIMO(multiple-input multiple-output)multipoint-to-multipoint(M2M) systems,in order to avoid pilot contamination and improve the performance of capacity,a pilot training transmission scheme was designe...Based on massive MIMO(multiple-input multiple-output)multipoint-to-multipoint(M2M) systems,in order to avoid pilot contamination and improve the performance of capacity,a pilot training transmission scheme was designed for pilot decontamination by utilizing orthogonal subcarriers of OFDM(orthogonal frequency division multiplexing) during pilot transmission phase and a joint optimized transceiver design for multi-antenna user pairs was proposed during the data transmission phase.The massive M2 M system included a single relay station,multiple paired source nodes and destination nodes.Source nodes precoding matrices and relay station precoding matrix were jointly optimized by maximizing the weighted sum-rate in OFDM systems.After some mathematical manipulation to sum-rate,the cost function of sum-rate was expressed as quadratic optimizing expressions which could be solved by regular convex optimization softwares.Different from existing algorithms,the proposed precoding design was based on massive MIMO OFDM systems with multi-antenna users pairs together pilot decontamination transmission arrangement.Simulations indicate the effectiveness of the proposed optimal precoding system.The proposed scheme not only can reduce pilot contamination,but also can improve performance of bit-error-rate(BER) as well as sumrate contrast to existing algorithms.In addition,it shows that the proposed M2 M massive MIMO system works steadily when the number of users increases in large scale.展开更多
Asymmetric massive multiple-input multiple-output(MIMO)systems have been proposed to reduce the burden of data processing and hardware cost in sixth-generation mobile networks(6G).However,in the asymmetric massive MIM...Asymmetric massive multiple-input multiple-output(MIMO)systems have been proposed to reduce the burden of data processing and hardware cost in sixth-generation mobile networks(6G).However,in the asymmetric massive MIMO system,reciprocity between the uplink(UL)and downlink(DL)wireless channels is not valid.As a result,pilots are required to be sent by both the base station(BS)and user equipment(UE)to predict doubledirectional channels,which consumes more transmission and computational resources.In this paper we propose an ensemble-transfer-learning-based channel parameter prediction method for asymmetric massive MIMO systems.It can predict multiple DL channel parameters including path loss(PL),multipath number,delay spread(DS),and angular spread.Both the UL channel parameters and environment features are chosen to predict the DL parameters.Also,we propose a two-step feature selection algorithm based on the SHapley Additive exPlanations(SHAP)value and the minimum description length(MDL)criterion to reduce the computation complexity and negative impact on model accuracy caused by weakly correlated or uncorrelated features.In addition,the instance transfer method is introduced to support the prediction model in new propagation conditions,where it is difficult to collect enough training data in a short time.Simulation results show that the proposed method is more accurate than the back propagation neural network(BPNN)and the 3GPP TR 38.901 channel model.Additionally,the proposed instancetransfer-based method outperforms the method without transfer learning in predicting DL parameters when the beamwidth or the communication sector changes.展开更多
Millimeter wave(mm Wave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5 G and beyond. To realize applications in future vehicular communications, it is important to take a rob...Millimeter wave(mm Wave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5 G and beyond. To realize applications in future vehicular communications, it is important to take a robust mm Wave vehicular network into consideration. However, one challenge in such a network is that mm Wave should provide an ultra-fast and high-rate data exchange among vehicles or vehicle-to-infrastructure(V2 I). Moreover,traditional real-time channel estimation strategies are unavailable because vehicle mobility leads to a fast variation mm Wave channel. To overcome these issues, a channel estimation approach for mm Wave V2 I communications is proposed in this paper. Specifically, by considering a fast-moving vehicle secnario, a corresponding mathematical model for a fast time-varying channel is first established. Then, the temporal variation rule between the base station and each mobile user and the determined direction-of-arrival are used to predict the time-varying channel prior information(PI). Finally, by exploiting the PI and the characteristics of the channel, the time-varying channel is estimated. The simulation results show that the scheme in this paper outperforms traditional ones in both normalized mean square error and sum-rate performance in the mm Wave time-varying vehicular system.展开更多
In this study,we consider a multi-cell millimeter-wave(mmWave)massive multiple-input multiple-output(MIMO)system with a mixed analog-to-digital converter(mixed-ADC)and hybrid beamforming architecture,in which antenna ...In this study,we consider a multi-cell millimeter-wave(mmWave)massive multiple-input multiple-output(MIMO)system with a mixed analog-to-digital converter(mixed-ADC)and hybrid beamforming architecture,in which antenna selection is applied to achieve intelligent assignment of high-and low-resolution ADCs.Both exact and approximate closed-form expressions for the uplink achievable rate are derived in the case of maximum-ratio combining reception.The impacts on the achievable rate of user transmit power,number of radio frequency chains at a base station,ratio of high-resolution ADCs,number of propagation paths,and number of quantization bits are analyzed.It is shown that the user transmit power can be scaled down inversely proportional to the number of antennas at the base station.We propose an efficient power allocation scheme by solving a complementary geometric programming problem.In addition,the energy efficiency is investigated,and an optimal tradeoff between the achievable rate and power consumption is discussed.Our results will provide a useful reference for the study of mixed-ADC multi-cell mmWave massive MIMO systems with antenna selection.展开更多
We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO...We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closedform expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems.Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.展开更多
The present paper is concerned with stability and L_(2) gain analysis of switched distributed parameter system(SDPS)with time delay.First,exponential stability is discussed,and the state decay estimate of the system i...The present paper is concerned with stability and L_(2) gain analysis of switched distributed parameter system(SDPS)with time delay.First,exponential stability is discussed,and the state decay estimate of the system is explicitly given by using linear matrix inequalities(LMIs)incorporating with the average dwell time(ADT)method.Second,L_(2) gain analysis is also studied.At last,illustrative examples are given to show the effectiveness of the proposed method.The main contribution of the paper is:some criteria of exponential stability and L_(2) gain for multiple-input multiple-output(MIMO)switched PDE are developed in the form of LMIs and ADT signal for the first time.The advantage of the work is we generalize the application range of the related research.The proposed method is expected to provide an effective tool for stability and H∞control analysis of SDPS.展开更多
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.
基金supported by National Natural Science Foundation of China(62371225,62371227)。
文摘Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.
基金supported by the National Natural Science Foundation of China(61671176 61671173)the Fundamental Research Funds for the Center Universities(HIT.MKSTISP.2016 13)
文摘How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to the large number of antennas. To reduce the overwhelming pilot overhead, a hybrid orthogonal and non-orthogonal pilot distribution at the base station(BS),which is a generalization of the existing pilot distribution scheme,is proposed by exploiting the common sparsity of channel due to the compact antenna arrangement. Then the block sparsity for antennas with hybrid pilot distribution is derived respectively and can be used to obtain channel impulse response. By employing the theoretical analysis of block sparse recovery, the total coherence criterion is proposed to optimize the sensing matrix composed by orthogonal pilots. Due to the huge complexity of optimal pilot acquisition, a genetic algorithm based pilot allocation(GAPA) algorithm is proposed to acquire optimal pilot distribution locations with fast convergence. Furthermore, the Cramer Rao lower bound is derived for non-orthogonal pilot-based channel estimation and can be asymptotically approached by the prior support set, especially when the optimized pilot is employed.
基金supported by the National Natural Science Foundation of China(61601504)。
文摘Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound(PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization(MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search(ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.
基金supported by the key project of the National Natural Science Foundation of China (No. 61431001)Huawei Innovation Research Program, the 5G research program of China Mobile Research Institute (Grant No. [2015] 0615)+2 种基金the open research fund of National Mobile Communications Research Laboratory Southeast University (No.2017D02)Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (Guilin University of Electronic Technology)the Foundation of Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services, and Keysight
文摘Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error(MMSE) signal detection using the accelerated overrelaxation(AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.
基金supported by the Ph.D.Student Research and Innovation Fund of the Fundamental Research Funds for the Central Universities(3072020GIP0803)Heilongjiang Province Key Laboratory Fund of High Accuracy Satellite Navigation and Marine Application Laboratory(HKL-2020-Y01)+2 种基金the National Natural Science Foundation of China(61571149)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology。
文摘This paper presents a co-time co-frequency fullduplex(CCFD)massive multiple-input multiple-output(MIMO)system to meet high spectrum efficiency requirements for beyond the fifth-generation(5G)and the forthcoming the sixth-generation(6G)networks.To achieve equilibrium of energy consumption,system resource utilization,and overall transmission capacity,an energy-efficient resource management strategy concerning power allocation and antenna selection is designed.A continuous quantum-inspired termite colony optimization(CQTCO)algorithm is proposed as a solution to the resource management considering the communication reliability while promoting energy conservation for the CCFD massive MIMO system.The effectiveness of CQTCO compared with other algorithms is evaluated through simulations.The results reveal that the proposed resource management scheme under CQTCO can obtain a superior performance in different communication scenarios,which can be considered as an eco-friendly solution for promoting reliable and efficient communication in future wireless networks.
基金supported by the National Natural Science Foundation of China(615015136140146941301481)
文摘A joint resource allocation scheme concerned with the sensor subset,power and bandwidth for range-only target tracking in multiple-input multiple-output(MIMO)radar systems is proposed.By selecting an optimal subset of sensors with the predetermined size and implementing the power allocation and bandwidth strategies among them,this algorithm can help achieving a better performance within the same resource constraints.Firstly,the Bayesian Cramer-Rao bound(BCRB)is derived from it.Secondly,a criterion for minimizing the BCRB at the target location among all targets tracking in a certain range is derived.Thirdly,the optimization problem involved with three variable vectors is formulated,which can be simplified by deriving the relationship between the optimal power allocation vector and the bandwidth allocation vector.Then,the simplified optimization problem is solved by the cyclic minimization algorithm incorporated with the sequential parametric convex approximation(SPCA)algorithm.Finally,the validity of the proposed method is demonstrated with simulation results.
基金Supported by the National Natural Science Foundation of China(61077022)
文摘A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output ( MIMO ) orthogonal frequency division multiplexing ( OFDM ) systems. Based on the basis expansion model (BEM) of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing (DCS) problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing (CS) theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.
文摘Introducing frequency agility into a distributed multipleinput multiple-output(MIMO)radar can significantly enhance its anti-jamming ability.However,it would cause the sidelobe pedestal problem in multi-target parameter estimation.Sparse recovery is an effective way to address this problem,but it cannot be directly utilized for multi-target parameter estimation in frequency-agile distributed MIMO radars due to spatial diversity.In this paper,we propose an algorithm for multi-target parameter estimation according to the signal model of frequency-agile distributed MIMO radars,by modifying the orthogonal matching pursuit(OMP)algorithm.The effectiveness of the proposed method is then verified by simulation results.
基金Supported by Beijing Natural Science Foundation(4194087)。
文摘Pilot contamination can bring up a grave impairment in the performance of massive multiple-input multiple-output(MIMO)systems.In this paper,an improved time-shifted pilot scheme is proposed to reduce the pilot contamination,where orthogonal pilots are employed in the same group to eliminate the residual intragroup interference existing in the original time-shifted pilot scheme.Meanwhile,the rigorous closed-form expressions of both downlink and uplink transmission rates with a finite number of antennas are derived,and it is shown that the intra-group interference can be completely eliminated by the proposed scheme.Simulation results demonstrate that both downlink and uplink transmission rates are significantly improved by employing the proposed scheme.
基金supported by Jiangsu Province Key Research and Development Program(BE2018704)Technical Innovation Project of The Ministry of Public Security(20170001)+1 种基金Fundamental Research Funds for the Central Universities(2242022k30001)National Science Foundation of China(CN)(Grant No.61871111).
文摘Location-based services have become an important part of the daily life.Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich scattering environment.In this paper,a single-site multiple-input multiple-output(MIMO)orthogonal frequency division multiplexing(OFDM)system is modeled,from which an angle delay channel power matrix(ADCPM)is extracted.Considering the changing environment,auto encoders are used to generate new fingerprints based on ADCPM fingerprints to improve the robustness of the fingerprints.When the scattering environment has changed beyond a certain extent,the robustness will not be able to make up for the positioning error.Under this circumstance,an updating of the fingerprint database is imperative.A new fingerprint database updating algorithm which combines a new clustering method and an updating rule based on probability is proposed.Simulation results show the desirable performance of the proposed methods.
基金This work was supported in part by National Key R&D Program of China under Grant 2020YFB1807201in part by National Natural Science Foundation of China under Grants 61971027,U1834210,and 61961130391+2 种基金in part by Beijing Natural Science Foundation under Grant L202013in part by Frontiers Science Center for Smart High-speed Railway System under Grant 2020JBZD005in part by the Royal Society Newton Advanced Fellowship under Grant NA191006.E.Björnson was supported by the Grant 2019-05068 from the Swedish Research Council.
文摘The mobile data traffic has been exponentially growing during the last several decades.This was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service variations.Cell-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this issue.In such systems,each User Equipment(UE)is served by a preferred set of surrounding APs cooperatively.In this paper,we provide a survey of the state-of-the-art literature on CF mMIMO.As a starting point,the significance and the basic properties of CF mMIMO are highlighted.We then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and algorithms.After that,we discuss the practical issues in implementing CF mMIMO and point out the potential future directions.Finally,we conclude this paper with a summary of the key lessons learned in this field.
文摘This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station(BS)is equipped with a very large number of antennas(also referred to as"massive multiple-input multiple-output(MIMO)").We consider a time-division duplexing(TDD)scheme,in which reciprocity between the uplink and downlink channels can be assumed.Channel estimation is essential for downlink beamforming in massive MIMO,nevertheless,the pilot contamination effect hinders accurate channel estimation,which leads to overall performance degradation.Benefitted from the asymptotic orthogonality between signal and interference subspaces for non-overlapping angle-of arrivals(AOAs)in the large-scale antenna system,we propose a multiple signals classification(MUSIC)based channel estimation algorithm during the uplink transmission.Analytical and numerical results verify complete pilot decontamination and the effectiveness of the proposed channel estimation algorithm in the multiuser multi-cell massive MIMO system.
基金National Natural Science Foundations of China(Nos.61505035,81470661,11604057)Science and Technology Project of Guangdong Province,China(No.2016A010101024)
文摘Based on massive MIMO(multiple-input multiple-output)multipoint-to-multipoint(M2M) systems,in order to avoid pilot contamination and improve the performance of capacity,a pilot training transmission scheme was designed for pilot decontamination by utilizing orthogonal subcarriers of OFDM(orthogonal frequency division multiplexing) during pilot transmission phase and a joint optimized transceiver design for multi-antenna user pairs was proposed during the data transmission phase.The massive M2 M system included a single relay station,multiple paired source nodes and destination nodes.Source nodes precoding matrices and relay station precoding matrix were jointly optimized by maximizing the weighted sum-rate in OFDM systems.After some mathematical manipulation to sum-rate,the cost function of sum-rate was expressed as quadratic optimizing expressions which could be solved by regular convex optimization softwares.Different from existing algorithms,the proposed precoding design was based on massive MIMO OFDM systems with multi-antenna users pairs together pilot decontamination transmission arrangement.Simulations indicate the effectiveness of the proposed optimal precoding system.The proposed scheme not only can reduce pilot contamination,but also can improve performance of bit-error-rate(BER) as well as sumrate contrast to existing algorithms.In addition,it shows that the proposed M2 M massive MIMO system works steadily when the number of users increases in large scale.
基金Project supported by the National Key Research and Development Program of China(No.2020YFB1804901)the National Natural Science Foundation of China(Nos.62271051 and 61871035)。
文摘Asymmetric massive multiple-input multiple-output(MIMO)systems have been proposed to reduce the burden of data processing and hardware cost in sixth-generation mobile networks(6G).However,in the asymmetric massive MIMO system,reciprocity between the uplink(UL)and downlink(DL)wireless channels is not valid.As a result,pilots are required to be sent by both the base station(BS)and user equipment(UE)to predict doubledirectional channels,which consumes more transmission and computational resources.In this paper we propose an ensemble-transfer-learning-based channel parameter prediction method for asymmetric massive MIMO systems.It can predict multiple DL channel parameters including path loss(PL),multipath number,delay spread(DS),and angular spread.Both the UL channel parameters and environment features are chosen to predict the DL parameters.Also,we propose a two-step feature selection algorithm based on the SHapley Additive exPlanations(SHAP)value and the minimum description length(MDL)criterion to reduce the computation complexity and negative impact on model accuracy caused by weakly correlated or uncorrelated features.In addition,the instance transfer method is introduced to support the prediction model in new propagation conditions,where it is difficult to collect enough training data in a short time.Simulation results show that the proposed method is more accurate than the back propagation neural network(BPNN)and the 3GPP TR 38.901 channel model.Additionally,the proposed instancetransfer-based method outperforms the method without transfer learning in predicting DL parameters when the beamwidth or the communication sector changes.
基金Project supported by the National Natural Science Foundation of China (No. 61971063)。
文摘Millimeter wave(mm Wave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5 G and beyond. To realize applications in future vehicular communications, it is important to take a robust mm Wave vehicular network into consideration. However, one challenge in such a network is that mm Wave should provide an ultra-fast and high-rate data exchange among vehicles or vehicle-to-infrastructure(V2 I). Moreover,traditional real-time channel estimation strategies are unavailable because vehicle mobility leads to a fast variation mm Wave channel. To overcome these issues, a channel estimation approach for mm Wave V2 I communications is proposed in this paper. Specifically, by considering a fast-moving vehicle secnario, a corresponding mathematical model for a fast time-varying channel is first established. Then, the temporal variation rule between the base station and each mobile user and the determined direction-of-arrival are used to predict the time-varying channel prior information(PI). Finally, by exploiting the PI and the characteristics of the channel, the time-varying channel is estimated. The simulation results show that the scheme in this paper outperforms traditional ones in both normalized mean square error and sum-rate performance in the mm Wave time-varying vehicular system.
基金Project supported by the National Key R&D Program of China(No.2018YFB1801101)the National Natural Science Foundation of China(Nos.62071031 and 61960206006)+4 种基金the Beijing Municipal Natural Science Foundation,China(No.4212006)the Center of National Railway Intelligent Transportation System Engineering and Technology,China Academy of Railway Sciences(Nos.RITS2019KF01 and 2019YJ188)the Research Fund of the National Mobile Communications Research Laboratory,Southeast University,China(Nos.2020B01 and 2021D01)the Fundamental Research Funds for the Central Universities,China(No.2242020R30001)the Huawei Cooperation Project,China,and the EU H2020 RISE TESTBED2 Project(No.872172)。
文摘In this study,we consider a multi-cell millimeter-wave(mmWave)massive multiple-input multiple-output(MIMO)system with a mixed analog-to-digital converter(mixed-ADC)and hybrid beamforming architecture,in which antenna selection is applied to achieve intelligent assignment of high-and low-resolution ADCs.Both exact and approximate closed-form expressions for the uplink achievable rate are derived in the case of maximum-ratio combining reception.The impacts on the achievable rate of user transmit power,number of radio frequency chains at a base station,ratio of high-resolution ADCs,number of propagation paths,and number of quantization bits are analyzed.It is shown that the user transmit power can be scaled down inversely proportional to the number of antennas at the base station.We propose an efficient power allocation scheme by solving a complementary geometric programming problem.In addition,the energy efficiency is investigated,and an optimal tradeoff between the achievable rate and power consumption is discussed.Our results will provide a useful reference for the study of mixed-ADC multi-cell mmWave massive MIMO systems with antenna selection.
基金supported by Ericsson and the National Natural Science Foundation of China(No.61371075)
文摘We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closedform expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems.Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.
基金This work was supported by the China Scholarship Council Fund(No.201808140223)the National Natural Science Foundation of China(No.11661020).
文摘The present paper is concerned with stability and L_(2) gain analysis of switched distributed parameter system(SDPS)with time delay.First,exponential stability is discussed,and the state decay estimate of the system is explicitly given by using linear matrix inequalities(LMIs)incorporating with the average dwell time(ADT)method.Second,L_(2) gain analysis is also studied.At last,illustrative examples are given to show the effectiveness of the proposed method.The main contribution of the paper is:some criteria of exponential stability and L_(2) gain for multiple-input multiple-output(MIMO)switched PDE are developed in the form of LMIs and ADT signal for the first time.The advantage of the work is we generalize the application range of the related research.The proposed method is expected to provide an effective tool for stability and H∞control analysis of SDPS.