The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of user...The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of users and devices.Researchers in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous vehicles.NextG continuously improves its network functionality to support these applications.Multiple input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in conjunctionwithNextG.This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks.The technique aims to create long-lasting and secure NextG networks using this extended approach.The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research.Moreover,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.展开更多
In this paper,we focus on the channel estimation for multi-user MIMO-OFDM systems in rich scattering environments.We find that channel sparsity in the delay-angle domain is severely compromised in rich scattering envi...In this paper,we focus on the channel estimation for multi-user MIMO-OFDM systems in rich scattering environments.We find that channel sparsity in the delay-angle domain is severely compromised in rich scattering environments,so that most existing compressed sensing(CS)based techniques can harvest a very limited gain(if any)in reducing the channel estimation overhead.To address the problem,we propose the learning-based turbo message passing(LTMP)algorithm.Instead of exploiting the channel sparsity,LTMP is able to efficiently extract the channel feature via deep learning as well as to exploit the channel continuity in the frequency domain via block-wise linear modelling.More specifically,as a component of LTMP,we develop a multi-scale parallel dilated convolutional neural network(MPDCNN),which leverages frequency-space channel correlation in different scales for channel denoising.We evaluate the LTMP’s performance in MIMO-OFDM channels using the 3rd generation partnership project(3GPP)clustered delay line(CDL)channel models.Simulation results show that the proposed channel estimation method has more than 5 dB power gain than the existing algorithms when the normalized mean-square error of the channel estimation is-20 dB.The proposed algorithm also exhibits strong robustness in various environments.展开更多
The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development.Due to the considerable mobility of trains and the complex nature of the environment,the...The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development.Due to the considerable mobility of trains and the complex nature of the environment,the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics,which presents significant hurdles in terms of channel estimation.In addition,the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation.To address the aforementioned issues,this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network.In this method,the time-frequency response of the channel is considered as a two-dimensional image.The Fast Super-Resolution Convolution Neural Network(FSRCNN)is used to first reconstruct channel images.Next,the Denoising Convolution Neural Network(DnCNN)is applied to reduce the channel noise and improve the accuracy of channel estimation.Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method,while also exhibiting reduced algorithmic complexity.展开更多
The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and ...The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.展开更多
Channel state information(CSI)is very important to sparse code multiple access combined with orthogonal frequency division multiplexing(SCMA-OFDM)systems for data detection.The main goal of this paper is to tackle the...Channel state information(CSI)is very important to sparse code multiple access combined with orthogonal frequency division multiplexing(SCMA-OFDM)systems for data detection.The main goal of this paper is to tackle the computational complexity and pilot overhead issues when estima-ting and tracking the channel frequency response of each user in uplink SCMA-OFDM systems.To this end,a new binary pilot structure is first designed to realize the initial channel estimation with significantly reduced computational complexity.Then,a channel tracking method is proposed to update the channel estimation in time-varying channels,which exploits a modified least mean square(LMS)technique with the feedback from the detector.Simulation results show that the pro-posed pilot structure can provide accurate channel estimation results.Moreover,the average bit error rate(BER)performance of the modified LMS algorithm can approach that of a detector with perfect CSI within 2 dB at the normalized Doppler frequency up to 6×10^(-6).展开更多
To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave(mmWave)massive Multiple-Input Multiple-Output(MIMO)systems in Internet of Vehicles(IoV)scenarios,the paper prop...To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave(mmWave)massive Multiple-Input Multiple-Output(MIMO)systems in Internet of Vehicles(IoV)scenarios,the paper proposes a deep learning(DL)algorithm,Squeeze-and-Excitation Attention Residual Network(SEARNet),which integrates Squeeze-and-Excitation Attention(SEAttention)mechanism and residual module.Specifically,SEARNet considers the channel information as an image matrix,and embeds a SEAttention module in residual module to construct the SEAttention-Residual block.Through a data-driven approach,SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism,thereby reducing noise interference and estimating the channel in an accurate and efficient manner.The simulation results show that compared to two traditional and two DL channel estimation algorithms,the proposed SEARNet can achieve a maximum reduction in normalized mean square error(NMSE)of 97.66%and 84.49%at SNR of-10 dB,78.18%at SNR of 5 dB,and 43.51%at SNR of 10 dB,respectively.展开更多
This paper considers the fundamental channel estimation problem for the multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system in the presence of multi-cell interference.Specificall...This paper considers the fundamental channel estimation problem for the multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system in the presence of multi-cell interference.Specifically,this paper focuses on both channel modelling and receiver design for interference estimation and mitigation.We propose a delay-calibrated block-wise linear model,which extracts the delay of the dominant tap of each interference as a key parameter and approximates the residual channel coefficients by the recently developed blockwise linear model.Based on the delay-calibrated block-wise linear model and the angle-domain channel sparsity,we further conceive a message passing algorithm to solve the channel estimation problem.Numerical results demonstrate the superior performance of the proposed algorithm over the state-of-the-art algorithms.展开更多
Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network.However,a significa...Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network.However,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability standards.To address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is proposed.This approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift matrix.Unlike previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC scheme.The effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.展开更多
The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional ch...The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN)neural network-based method that is used to solve this problem.Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then,the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN)with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.展开更多
A pilot pattern across two orthogonal frequency division multiplexing OFDM symbols with a special structure is designed for the channel estimation of OFDM systems with inphase and quadrature IQ imbalances at the recei...A pilot pattern across two orthogonal frequency division multiplexing OFDM symbols with a special structure is designed for the channel estimation of OFDM systems with inphase and quadrature IQ imbalances at the receiver.A high-efficiency time-domain TD least square LS channel estimator and a low-complexity frequency-domain Gaussian elimination GE equalizer are proposed to eliminate IQ distortion.The former estimator can significantly suppress channel noise by a factor N/L＋1 over the existing frequency-domain FD LS where N and L＋1 are the total number of subcarriers and the length of cyclic prefix and the proposed GE requires only 2N complex multiplications per OFDM symbol.Simulation results show that by exploiting the TD property of the channel the proposed TD-LS channel estimator obtains a significant signal-to-noise ratio gain over the existing FD-LS one whereas the proposed low-complexity GE compensation achieves the same bit error rate BER performance as the existing LS one.展开更多
For orthogonal frequency division multiplexing (OFDM) wireless communication, the system throughput and data rate are usually limited by pilots, especially in a high mobility environment. In this paper, an enhanced it...For orthogonal frequency division multiplexing (OFDM) wireless communication, the system throughput and data rate are usually limited by pilots, especially in a high mobility environment. In this paper, an enhanced iterative joint channel estimation and symbol detection algorithm is proposed to enhance the system throughput and data rate. With lower pilot power, the proposed scheme increases system throughput firstly, and then the channel estimation and symbol detection proceed iteratively within one OFDM symbol to improve the BER performance. In the proposed algorithm, the original channel estimate of each OFDM symbol is based on the channel estimate of the previous OFDM symbol, thus the variation of the mobile channel is traced efficiently, so the number of pilots in the time domain can be reduced greatly. Besides reducing the system overhead, the proposed algorithm is also shown by simulation to give much better BER performance than the conventional iterative algorithm does.展开更多
In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the Eur...In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the European Telecommunication Standards Institute（ETSI） digital radio mondiale （DRM） standard, the subspace pursuit （SP） algorithm is employed for delay spread and attenuation estimation of each path in the case where the channel profile is identified and the multipath number is known. The stop condition for SP is that the sparsity of the estimation equals the multipath number. For the case where the multipath number is unknown, the orthogonal matching pursuit （OMP） algorithm is employed for channel estimation, while the stop condition is that the estimation achieves the noise variance. Simulation results show that with the same number of pilots, CS algorithms outperform the traditional cubic-spline-interpolation-based least squares （LS） channel estimation. SP is also demonstrated to be better than OMP when the multipath number is known as a priori.展开更多
Under analyzing several characteristics of frequency-selective fast fading channels, such as large Doppler spread and multi-path interference, a low-dimensional Kalman filter method based on pilot signals is presented...Under analyzing several characteristics of frequency-selective fast fading channels, such as large Doppler spread and multi-path interference, a low-dimensional Kalman filter method based on pilot signals is presented for the channel estimation of orthogonal frequency division multiplexing （OFDM） systems. For simplicity, a one-dimensional autoregressive （AR） process is used to model the time-varying channel, and the least square （LS） algorithm based on pilot signals is adopted to track the time-varying channel fading factor a. The low-dimensional Kalman filter estimator greatly reduces the complexity of the high-dimensional Kalman filter. To utilize the relationship of fading channel in frequency domain, a minimum mean-square-error （MMSE） combiner is used to refine the estimation results. The simulation results in the frequency band of 5.5 GHz show that the proposed method achieves a good symbol error rate （SER） performance close to the theoretical bound of ideal channel estimation.展开更多
Based on the transform-domain characteristics of pilot signals,a band suppression filter is used as a transform-domain filter to restrain the interference of noise in channel estimation.The performance effect on chann...Based on the transform-domain characteristics of pilot signals,a band suppression filter is used as a transform-domain filter to restrain the interference of noise in channel estimation.The performance effect on channel estimation for an orthogonal frequency division multiplex （OFDM） system by different energy coefficients in the transform domain and the energy coefficient under the different signal-to-noise ratios （SNR） are also analyzed.A new energy coefficient expression is deduced.It is theoretically proven that dynamically selecting an energy coefficient can significantly improve the performance of channel estimation.Simulation results show that the proposed algorithm can achieve better performance close to the theoretic bounds of perfect channel estimation. The algorithm is adapted to single-input single-output （SISO） OFDM and multi-input multi-output （MIMO） OFDM systems.展开更多
In order to increase the transmission efficiency,a subspace-based algorithm for blind channel estimation using second-order statistics is proposed in orthogonal frequency division multiplexing (OFDM) systems.Because t...In order to increase the transmission efficiency,a subspace-based algorithm for blind channel estimation using second-order statistics is proposed in orthogonal frequency division multiplexing (OFDM) systems.Because the transmission equation of OFDM systems does not exactly have the desired structure to directly derive a subspace algorithm,the algorithm first divides the OFDM signals into three parts,then,by exploiting the redundancy introduced by the cyclic prefix (CP) in OFDM signals,a new equation with Toeplitz channel matrix is derived.Based on the equation,a new blind subspace algorithm is developed.Toeplitz structure eases the derivation of the subspace algorithm and practical computation.Moreover the algorithm does not change the existing OFDM system,is robust to channel order overdetermination,and the channel zero locations.The performances are demonstrated by simulation results.展开更多
Channel estimation techniques applied to cognitive radio networks (CRN) are analyzed for simultaneously primary and secondary channel estimations operating in underlay cognitive radio networks (uCRN). A complete base-...Channel estimation techniques applied to cognitive radio networks (CRN) are analyzed for simultaneously primary and secondary channel estimations operating in underlay cognitive radio networks (uCRN). A complete base-band transmission including pilot sequence transmission, channel matrix estimation and optimal precoder matrix generation based on imperfect channel estimation are described. Also, the effect of imperfect channel estimation has been studied to provide means of developing techniques to overcome problems while enhancing the MIMO communication performance.展开更多
Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next gene...Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.展开更多
Reconfigurable intelligent surface(RIS)can manipulate the wireless propagation environment by smartly adjusting the amplitude/phase in a programmable panel,enjoying the improved performance.The accurate acquisition of...Reconfigurable intelligent surface(RIS)can manipulate the wireless propagation environment by smartly adjusting the amplitude/phase in a programmable panel,enjoying the improved performance.The accurate acquisition of the instantaneous channel state information(CSI)in the cascaded RIS chain makes an indispensable contribution to the performance gains.However,it is quite challenging to estimate the CSI in a time-variant scenario due to the limited signal processing capability of the passive elements embedded in a RIS pannel.In this work,a channel estimation scheme for the RIS-assisted wireless communication system is proposed,which is demonstrated to perform well in a time-variant scenario.The cascaded RIS channel is modeled as a state-space model based upon the mobility situations.In addition,to fully exploit the time correlation of channel,Kalman filter is employed by taking the prior information of channels into account.Further,the optimal reflection coefficients are derived according to the minimum mean square error(MMSE)criterion.Numerical results show that the proposed methods exhibit superior performance if compared with a conventional channel estimation scheme.展开更多
Terahertz(THz)communication is considered to be a promising technology for future 6G network.To overcome the severe attenuation and relieve the high power consumption,massive multipleinput multiple-output(MIMO)with hy...Terahertz(THz)communication is considered to be a promising technology for future 6G network.To overcome the severe attenuation and relieve the high power consumption,massive multipleinput multiple-output(MIMO)with hybrid precoding has been widely considered for THz communication.However,accurate wideband channel estimation,which is essential for hybrid precoding,is challenging in THz massive MIMO systems.The existing wideband channel estimation schemes based on the ideal assumption of common sparse channel support will suffer from a severe performance loss due to the beam split effect.In this paper,we propose a beam split pattern detection based channel estimation scheme to realize reliable wideband channel estimation in THz massive MIMO systems.Specifically,a comprehensive analysis on the angle-domain sparse structure of the wideband channel is provided by considering the beam split effect.Based on the analysis,we define a series of index sets called as beam split patterns,which are proved to have a one-to-one match to different physical channel directions.Inspired by this one-to-one match,we propose to estimate the physical channel direction by exploiting beam split patterns at first.Then,the sparse channel supports at different subcarriers can be obtained by utilizing a support detection window.This support detection window is generated by expanding the beam split pattern which is determined by the obtained physical channel direction.The above estimation procedure will be repeated path by path until all path components are estimated.Finally,the wideband channel can be recovered by calculating the elements on the total sparse channel support at all subcarriers.The proposed scheme exploits the wideband channel property implied by the beam split effect,i.e.,beam split pattern,which can significantly improve the channel estimation accuracy.Simulation results show that the proposed scheme is able to achieve higher accuracy than existing schemes.展开更多
The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWa...The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training.Then accurate channel inference can be efficiently implemented using the trained network.The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas.It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.展开更多
基金funding from King Saud University through Researchers Supporting Project number(RSP2024R387),King Saud University,Riyadh,Saudi Arabia.
文摘The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of users and devices.Researchers in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous vehicles.NextG continuously improves its network functionality to support these applications.Multiple input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in conjunctionwithNextG.This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks.The technique aims to create long-lasting and secure NextG networks using this extended approach.The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research.Moreover,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1804800.
文摘In this paper,we focus on the channel estimation for multi-user MIMO-OFDM systems in rich scattering environments.We find that channel sparsity in the delay-angle domain is severely compromised in rich scattering environments,so that most existing compressed sensing(CS)based techniques can harvest a very limited gain(if any)in reducing the channel estimation overhead.To address the problem,we propose the learning-based turbo message passing(LTMP)algorithm.Instead of exploiting the channel sparsity,LTMP is able to efficiently extract the channel feature via deep learning as well as to exploit the channel continuity in the frequency domain via block-wise linear modelling.More specifically,as a component of LTMP,we develop a multi-scale parallel dilated convolutional neural network(MPDCNN),which leverages frequency-space channel correlation in different scales for channel denoising.We evaluate the LTMP’s performance in MIMO-OFDM channels using the 3rd generation partnership project(3GPP)clustered delay line(CDL)channel models.Simulation results show that the proposed channel estimation method has more than 5 dB power gain than the existing algorithms when the normalized mean-square error of the channel estimation is-20 dB.The proposed algorithm also exhibits strong robustness in various environments.
基金funded in part by the National Natural Science Foundation of China(62261024 and U2001213)in part by National Key Research and Development Project(2020YFB1807204)+2 种基金in part by Science and Technology Project of Education Department of Jiangxi Province(GJJ214606 and GJJ2205201)in part by Key Laboratory of Universal Wireless Communications(BUPT),Ministry of Education,P.R.China(KFKT-2022101)in part by the Jiangxi Provincial Natural Science Foundation(20212BAB212001)。
文摘The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development.Due to the considerable mobility of trains and the complex nature of the environment,the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics,which presents significant hurdles in terms of channel estimation.In addition,the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation.To address the aforementioned issues,this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network.In this method,the time-frequency response of the channel is considered as a two-dimensional image.The Fast Super-Resolution Convolution Neural Network(FSRCNN)is used to first reconstruct channel images.Next,the Denoising Convolution Neural Network(DnCNN)is applied to reduce the channel noise and improve the accuracy of channel estimation.Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method,while also exhibiting reduced algorithmic complexity.
文摘The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.
基金Supported by the National Natural Science Foundation of China(No.62171135)the Natural Science Foundation of Fujian Province(No.2023J01399)。
文摘Channel state information(CSI)is very important to sparse code multiple access combined with orthogonal frequency division multiplexing(SCMA-OFDM)systems for data detection.The main goal of this paper is to tackle the computational complexity and pilot overhead issues when estima-ting and tracking the channel frequency response of each user in uplink SCMA-OFDM systems.To this end,a new binary pilot structure is first designed to realize the initial channel estimation with significantly reduced computational complexity.Then,a channel tracking method is proposed to update the channel estimation in time-varying channels,which exploits a modified least mean square(LMS)technique with the feedback from the detector.Simulation results show that the pro-posed pilot structure can provide accurate channel estimation results.Moreover,the average bit error rate(BER)performance of the modified LMS algorithm can approach that of a detector with perfect CSI within 2 dB at the normalized Doppler frequency up to 6×10^(-6).
基金supported in part by the National Natural Science Foundation of China under Grants U2001213 and 62261024in part by National Key Research and Development Project under Grant 2020YFB1807204in part by Key Laboratory of Universal Wireless Communications(BUPT),Ministry of Education under Grant KFKT2022101.
文摘To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave(mmWave)massive Multiple-Input Multiple-Output(MIMO)systems in Internet of Vehicles(IoV)scenarios,the paper proposes a deep learning(DL)algorithm,Squeeze-and-Excitation Attention Residual Network(SEARNet),which integrates Squeeze-and-Excitation Attention(SEAttention)mechanism and residual module.Specifically,SEARNet considers the channel information as an image matrix,and embeds a SEAttention module in residual module to construct the SEAttention-Residual block.Through a data-driven approach,SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism,thereby reducing noise interference and estimating the channel in an accurate and efficient manner.The simulation results show that compared to two traditional and two DL channel estimation algorithms,the proposed SEARNet can achieve a maximum reduction in normalized mean square error(NMSE)of 97.66%and 84.49%at SNR of-10 dB,78.18%at SNR of 5 dB,and 43.51%at SNR of 10 dB,respectively.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1804800。
文摘This paper considers the fundamental channel estimation problem for the multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system in the presence of multi-cell interference.Specifically,this paper focuses on both channel modelling and receiver design for interference estimation and mitigation.We propose a delay-calibrated block-wise linear model,which extracts the delay of the dominant tap of each interference as a key parameter and approximates the residual channel coefficients by the recently developed blockwise linear model.Based on the delay-calibrated block-wise linear model and the angle-domain channel sparsity,we further conceive a message passing algorithm to solve the channel estimation problem.Numerical results demonstrate the superior performance of the proposed algorithm over the state-of-the-art algorithms.
基金supported in part by the National Natural Science Foundation of China under Grant 62001171in part by the Natural Science Foundation of Guangdong Province under Grant 2024A1515011172in part by the Henan Science and Technology Research and Development Program Joint Fund under Grant 235200810049。
文摘Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network.However,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability standards.To address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is proposed.This approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift matrix.Unlike previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC scheme.The effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.
基金supported by the National Key Scientific Instrument and Equipment Development Project(61827801).
文摘The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN)neural network-based method that is used to solve this problem.Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then,the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN)with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.
基金The Open Research Fund of National Mobile Communications Research Laboratory of Southeast University(No.2013D02)the Fundamental Research Funds for the Central Universities(No.30920130122004)the National Natural Science Foundation of China(No.61271230,61472190)
文摘A pilot pattern across two orthogonal frequency division multiplexing OFDM symbols with a special structure is designed for the channel estimation of OFDM systems with inphase and quadrature IQ imbalances at the receiver.A high-efficiency time-domain TD least square LS channel estimator and a low-complexity frequency-domain Gaussian elimination GE equalizer are proposed to eliminate IQ distortion.The former estimator can significantly suppress channel noise by a factor N/L＋1 over the existing frequency-domain FD LS where N and L＋1 are the total number of subcarriers and the length of cyclic prefix and the proposed GE requires only 2N complex multiplications per OFDM symbol.Simulation results show that by exploiting the TD property of the channel the proposed TD-LS channel estimator obtains a significant signal-to-noise ratio gain over the existing FD-LS one whereas the proposed low-complexity GE compensation achieves the same bit error rate BER performance as the existing LS one.
文摘For orthogonal frequency division multiplexing (OFDM) wireless communication, the system throughput and data rate are usually limited by pilots, especially in a high mobility environment. In this paper, an enhanced iterative joint channel estimation and symbol detection algorithm is proposed to enhance the system throughput and data rate. With lower pilot power, the proposed scheme increases system throughput firstly, and then the channel estimation and symbol detection proceed iteratively within one OFDM symbol to improve the BER performance. In the proposed algorithm, the original channel estimate of each OFDM symbol is based on the channel estimate of the previous OFDM symbol, thus the variation of the mobile channel is traced efficiently, so the number of pilots in the time domain can be reduced greatly. Besides reducing the system overhead, the proposed algorithm is also shown by simulation to give much better BER performance than the conventional iterative algorithm does.
基金The National Natural Science Foundation of China (No.60872075)the National High Technology Research and Development Program of China (863 Program) (No.2008AA01Z227)
文摘In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the European Telecommunication Standards Institute（ETSI） digital radio mondiale （DRM） standard, the subspace pursuit （SP） algorithm is employed for delay spread and attenuation estimation of each path in the case where the channel profile is identified and the multipath number is known. The stop condition for SP is that the sparsity of the estimation equals the multipath number. For the case where the multipath number is unknown, the orthogonal matching pursuit （OMP） algorithm is employed for channel estimation, while the stop condition is that the estimation achieves the noise variance. Simulation results show that with the same number of pilots, CS algorithms outperform the traditional cubic-spline-interpolation-based least squares （LS） channel estimation. SP is also demonstrated to be better than OMP when the multipath number is known as a priori.
文摘Under analyzing several characteristics of frequency-selective fast fading channels, such as large Doppler spread and multi-path interference, a low-dimensional Kalman filter method based on pilot signals is presented for the channel estimation of orthogonal frequency division multiplexing （OFDM） systems. For simplicity, a one-dimensional autoregressive （AR） process is used to model the time-varying channel, and the least square （LS） algorithm based on pilot signals is adopted to track the time-varying channel fading factor a. The low-dimensional Kalman filter estimator greatly reduces the complexity of the high-dimensional Kalman filter. To utilize the relationship of fading channel in frequency domain, a minimum mean-square-error （MMSE） combiner is used to refine the estimation results. The simulation results in the frequency band of 5.5 GHz show that the proposed method achieves a good symbol error rate （SER） performance close to the theoretical bound of ideal channel estimation.
文摘Based on the transform-domain characteristics of pilot signals,a band suppression filter is used as a transform-domain filter to restrain the interference of noise in channel estimation.The performance effect on channel estimation for an orthogonal frequency division multiplex （OFDM） system by different energy coefficients in the transform domain and the energy coefficient under the different signal-to-noise ratios （SNR） are also analyzed.A new energy coefficient expression is deduced.It is theoretically proven that dynamically selecting an energy coefficient can significantly improve the performance of channel estimation.Simulation results show that the proposed algorithm can achieve better performance close to the theoretic bounds of perfect channel estimation. The algorithm is adapted to single-input single-output （SISO） OFDM and multi-input multi-output （MIMO） OFDM systems.
文摘In order to increase the transmission efficiency,a subspace-based algorithm for blind channel estimation using second-order statistics is proposed in orthogonal frequency division multiplexing (OFDM) systems.Because the transmission equation of OFDM systems does not exactly have the desired structure to directly derive a subspace algorithm,the algorithm first divides the OFDM signals into three parts,then,by exploiting the redundancy introduced by the cyclic prefix (CP) in OFDM signals,a new equation with Toeplitz channel matrix is derived.Based on the equation,a new blind subspace algorithm is developed.Toeplitz structure eases the derivation of the subspace algorithm and practical computation.Moreover the algorithm does not change the existing OFDM system,is robust to channel order overdetermination,and the channel zero locations.The performances are demonstrated by simulation results.
文摘Channel estimation techniques applied to cognitive radio networks (CRN) are analyzed for simultaneously primary and secondary channel estimations operating in underlay cognitive radio networks (uCRN). A complete base-band transmission including pilot sequence transmission, channel matrix estimation and optimal precoder matrix generation based on imperfect channel estimation are described. Also, the effect of imperfect channel estimation has been studied to provide means of developing techniques to overcome problems while enhancing the MIMO communication performance.
基金supported by the Natural Science Foundation of Chongqing(No.cstc2019jcyj-msxmX0017)。
文摘Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.
基金supported in part by National Natural Science Foundation of China(Grant Nos.61921003,61925101,61831002 and 61901315)in part by the Beijing Natural Science Foundation under(Grant No.JQ18016)in part by the Fundamental Research Funds for the Central Universities(Grant No.2020RC08).
文摘Reconfigurable intelligent surface(RIS)can manipulate the wireless propagation environment by smartly adjusting the amplitude/phase in a programmable panel,enjoying the improved performance.The accurate acquisition of the instantaneous channel state information(CSI)in the cascaded RIS chain makes an indispensable contribution to the performance gains.However,it is quite challenging to estimate the CSI in a time-variant scenario due to the limited signal processing capability of the passive elements embedded in a RIS pannel.In this work,a channel estimation scheme for the RIS-assisted wireless communication system is proposed,which is demonstrated to perform well in a time-variant scenario.The cascaded RIS channel is modeled as a state-space model based upon the mobility situations.In addition,to fully exploit the time correlation of channel,Kalman filter is employed by taking the prior information of channels into account.Further,the optimal reflection coefficients are derived according to the minimum mean square error(MMSE)criterion.Numerical results show that the proposed methods exhibit superior performance if compared with a conventional channel estimation scheme.
基金supported in part by the National Key Research and Development Program of China(Grant No.2020YFB1805005)the National Natural Science Foundation of China(Grant No.62031019)the European Commission through the H2020-MSCA-ITN META WIRELESS Research Project under Grant 956256.
文摘Terahertz(THz)communication is considered to be a promising technology for future 6G network.To overcome the severe attenuation and relieve the high power consumption,massive multipleinput multiple-output(MIMO)with hybrid precoding has been widely considered for THz communication.However,accurate wideband channel estimation,which is essential for hybrid precoding,is challenging in THz massive MIMO systems.The existing wideband channel estimation schemes based on the ideal assumption of common sparse channel support will suffer from a severe performance loss due to the beam split effect.In this paper,we propose a beam split pattern detection based channel estimation scheme to realize reliable wideband channel estimation in THz massive MIMO systems.Specifically,a comprehensive analysis on the angle-domain sparse structure of the wideband channel is provided by considering the beam split effect.Based on the analysis,we define a series of index sets called as beam split patterns,which are proved to have a one-to-one match to different physical channel directions.Inspired by this one-to-one match,we propose to estimate the physical channel direction by exploiting beam split patterns at first.Then,the sparse channel supports at different subcarriers can be obtained by utilizing a support detection window.This support detection window is generated by expanding the beam split pattern which is determined by the obtained physical channel direction.The above estimation procedure will be repeated path by path until all path components are estimated.Finally,the wideband channel can be recovered by calculating the elements on the total sparse channel support at all subcarriers.The proposed scheme exploits the wideband channel property implied by the beam split effect,i.e.,beam split pattern,which can significantly improve the channel estimation accuracy.Simulation results show that the proposed scheme is able to achieve higher accuracy than existing schemes.
基金This work is supported in part by the National Natural Science Foundation of China under grants 61901403,61971366 and 61971365in part by the Youth Innovation Fund of Xiamen under grant 3502Z20206039in part by the Natural Science Foundation of Fujian Province of China under grant 2019J05001.
文摘The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training.Then accurate channel inference can be efficiently implemented using the trained network.The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas.It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.
