channel estimation

摘要： A reconfigurable intelligent surface(RIS)aided massive multiple-input multiple-output(MIMO)system is considered,where the base station employs a large antenna array with low-cost and low-power 1-bit analog-to-digital converters(ADCs).To compensate for the per-formance loss caused by the coarse quantization,oversampling is applied at the receiver.The main challenge for the acquisition of cascaded channel state information in such a system is to handle the distortion caused by the 1-bit quantization and the sample correlation caused by oversampling.In this work,Bussgang decomposition is applied to deal with the coarse quantization,and a Markov chain is developed to char-acterize the banded structure of the oversampling filter.An approximate message-passing based algorithm is proposed for the estimation of the cascaded channels.Simulation results demonstrate that our proposed 1-bit systems with oversampling can approach the 2-bit systems in terms of the mean square error performance while the former consumes much less power at the receiver.

摘要： As a critical candidate technology for 5G-advanced and 6G,reconfigurable intelligent surfaces(RIS)have received extensive atten-tion from academia and industry.RIS has the promising features of passiveness,reconfigurable ability,and low cost.RIS channel estimation faces the challenges of high matrix dimension,passive estimation,and spatial-wideband effect.In this article,we analyze the impact of the spatial-wideband effect on the RIS channel to account for the propagation delay across RIS elements and estimate sparse channel parameters such as angle and gain through a super-resolution compressive sensing(CS)algorithm.The simulation results explore the influence of the spatial-wideband effect on the RIS channel and verify the effectiveness of the proposed algorithm.

摘要： Intelligent reflecting surface(IRS),with its unique capability of smartly reconfiguring wireless channels,provides a new solution to improving spectrum efficiency,reducing energy consumption and saving deployment/hardware cost for future wireless networks.In this paper,IRS-enabled spectrum sharing is investigated,from the perspectives of interference modeling,efficient channel estimation and robust passive beamforming design.Specifically,we first characterize the interference in a spectrum sharing system consisting of a single primary user(PU)pair and a single secondary user(SU)pair,and extend it to the large-scale network by leveraging the Poisson point process(PPP).Then,we propose an efficient channel estimation framework based on decoupling the cascaded IRS channels.Moreover,the tradeoff between spectrum efficiency and energy efficiency is derived from the view of channel estimation accuracy.Finally,we discuss the robust passive beamforming design in presence of imperfect channel estimation and nonideal/discrete phase shifts.It is hoped that this paper provides useful guidance for unlocking the full potential of IRS for achieving efficient spectrum sharing for future wireless networks.

摘要： A joint Doppler shift and channel estimation method for the millimeter-wave communication system of an unmanned aerial vehicle(UAV) equipped with a large-scale uniform linear antenna(ULA) array has been proposed. Since Doppler shift induces intercarrier interference, the parameters of the channel paths have been decomposed into the Doppler shift and the channel information. In order to obtain the Doppler shift, a new estimation algorithm based on a combination of discrete Fourier transform and phase rotation has been proposed, which can determine the appropriate number of antennas. In addition to estimating the channel information, a low-complexity joint Doppler shift and channel estimation method has been designed that can quickly obtain accurate estimates. Furthermore, the achievable sum rate, the theoretical bounds of the mean squared errors, and the Cram?er-Rao lower bounds of the estimation method have been derived. The analysis and simulation results prove that the performance of the proposed approach is close to the theoretical inference.

摘要： In this paper,we survey state-of-the-art research outcomes in the burgeoning field of Reconfigurable Intelligent Surface(RIS),given its potential for significant performance enhancement of next-generation wireless communication networks by means of adapting a propagation environment.Emphasis has been placed on several aspects gating the commercial viability of future network deployment.Comprehensive summaries are provided for practical hardware design considerations and broad implications of artificial intelligence techniques,as are in-depth outlooks on the salient aspects of system models,use cases,and physical layer optimization techniques.

摘要： This paper proposes a novel joint channel estimation and beamforming scheme for the massive multiple-input-multiple-output(MIMO)frequency-division duplexing(FDD) wireless legitimate surveillance system. With the proposed scheme,the monitor with the full duplex capability realizes the proactive eavesdropping of the suspicious link by leveraging the pilot attack approach. Specifically, exploiting the effective eavesdropping rate and the mean square error as performance metrics and setting a total power budget at the training and transmission phases,while guaranteeing the information from suspicious source can be successfully decode, joint pilot design,power allocation and beamforming strategy are formulated as optimization problems for the two objective functions: MSE and effective eavesdropping rate. A closed-form expression of the optimal pilot with the limited length can be obtained via the channel correlation. The optimal power problem at the training phase can be solved by a simple bisection method. Then,based on the obtained imperfect estimated channel,the jamming beamforming at monitor optimization algorithm is proposed by utilizing the convex Semidefinite Programming approach to maximize the effective eavesdropping rate. Numerical results show that the proposed joint pilot design, power allocation and beamforming optimization scheme can improve the surveillance performance of the legitimate monitor as compared to the existing passive eavesdropping and jamming-assisted eavesdropping.

摘要： In communication channel estimation,the Least Square(LS)technique has long been a widely accepted and commonly used principle.This is because the simple calculation method is compared with other channel estimation methods.The Minimum Mean Squares Error(MMSE),which is developed later,is devised as the next step because the goal is to reduce the error rate in the communication system from the conventional LS technique which still has a higher error rate.These channel estimations are very important to modern communication systems,especially massive MIMO.Evaluating the massive MIMO channel is one of the most researched and debated topics today.This is essential in technology to overcome traditional performance barriers.The better the channel estimation,the more accurate it is.This paper investigated machine learning(ML)for channel estimation.ML channel estimations based on the Extreme Learning Machine(ELMx)group are also implemented.These estimations,known as the ELMx group,include Regularized Extreme Learning Machine(RELM)and Outlier Robust Extreme Learning Machine(ORELM).Then,it was compared with LS and MMSE.The simulation results reveal that the ELMx group outperforms LS and MMSE in channel capacity and bit error rate.Additionally,this paper has proven complexity for verified computational times.The RELM method is less time consuming and has low complexity which is suitable for future use in large MIMO systems.

摘要： 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.

摘要： Relying on direct and converse piezoelectric effects,piezo-acoustic backscatter(PAB)technology reflects ambient acoustic signals to enable underwater backscatter communications at near-zero power,which was first realized through a prototype.In this paper,we propose a mathematical model of the PAB assisted underwater acoustic(UWA)communication,and address the sparse channel estimation problem.First,we present a five-stage backscatter process to derive the backscatter coefficient,and propose the channel model for the shallow-water communications.Then,we formulate the shallow-water acoustic channel estimation problem as a sparse vector recovery one according to the compressed sensing theory,and leverage the orthogonal matching pursuit(OMP)algorithm to obtain the channel estimator.Finally,simulation results are provided to corroborate our proposed studies.

摘要： Channel estimation has been considered as a key issue in the millimeter-wave(mmWave)massive multi-input multioutput(MIMO)communication systems,which becomes more challenging with a large number of antennas.In this paper,we propose a deep learning(DL)-based fast channel estimation method for mmWave massive MIMO systems.The proposed method can directly and effectively estimate channel state information(CSI)from received data without performing pilot signals estimate in advance,which simplifies the estimation process.Specifically,we develop a convolutional neural network(CNN)-based channel estimation network for the case of dimensional mismatch of input and output data,subsequently denoted as channel(H)neural network(HNN).It can quickly estimate the channel information by learning the inherent characteristics of the received data and the relationship between the received data and the channel,while the dimension of the received data is much smaller than the channel matrix.Simulation results show that the proposed HNN can gain better channel estimation accuracy compared with existing schemes.

摘要： In pilot-symbol-aided channel estimation algorithms of OFDM system,transformation domain based channel estimators have excellent performance.This paper proposes a truncation DCT channeI estimation algorithm which iS bases on mirror weighted DCT channel estimation and utilizes the characteristic which compress the power to the low frequency reign by DCT.It does not only overcome aliasing effect of DFT-based channel estimation algorithms,but also reduces rudimental noise in DCT-based channel estimation algorithms.By the result of simulation,the proposed algorithms’performance in this paper is closed to MMSE estimator and much betcer to excels Other conventional transformation domain method.

摘要： In pilot-symbol-aided channel estimation algorithms of OFDM system,transformation domain based channel estimators have excellent performance.This paper proposes a truncation DCT channeI estimation algorithm which iS bases on mirror weighted DCT channel estimation and utilizes the characteristic which compress the power to the low frequency reign by DCT.It does not only overcome aliasing effect of DFT-based channel estimation algorithms,but also reduces rudimental noise in DCT-based channel estimation algorithms.By the result of simulation,the proposed algorithms’performance in this paper is closed to MMSE estimator and much betcer to excels Other conventional transformation domain method.

摘要： In pilot-symbol-aided channel estimation algorithms of OFDM system,transformation domain based channel estimators have excellent performance.This paper proposes a truncation DCT channeI estimation algorithm which iS bases on mirror weighted DCT channel estimation and utilizes the characteristic which compress the power to the low frequency reign by DCT.It does not only overcome aliasing effect of DFT-based channel estimation algorithms,but also reduces rudimental noise in DCT-based channel estimation algorithms.By the result of simulation,the proposed algorithms’performance in this paper is closed to MMSE estimator and much betcer to excels Other conventional transformation domain method.

摘要： In pilot-symbol-aided channel estimation algorithms of OFDM system,transformation domain based channel estimators have excellent performance.This paper proposes a truncation DCT channeI estimation algorithm which iS bases on mirror weighted DCT channel estimation and utilizes the characteristic which compress the power to the low frequency reign by DCT.It does not only overcome aliasing effect of DFT-based channel estimation algorithms,but also reduces rudimental noise in DCT-based channel estimation algorithms.By the result of simulation,the proposed algorithms’performance in this paper is closed to MMSE estimator and much betcer to excels Other conventional transformation domain method.