A Receiver Structure for Frequency-Flat Time-Varying Rayleigh Channels and Performance Analysis

This paper proposes a wavelet based receiver structure for frequency-flat time-varying Rayleigh channels, consisting of a receiver front-end followed by a Maximum A-Posteriori (MAP) detector. Discretization of the received continuous time signal using filter banks is an essential stage in the front-end part, where the Fast Haar Transform (FHT) is used to reduce complexity. Analysis of our receiver over slow-fading channels shows that it is optimal for certain modulation schemes. By comparison with literature, it is shown that over such channels our receiver can achieve optimal performance for Time-Orthogonal modulation. Computed and Monte-Carlo simulated performance results over fast time-varying Rayleigh fading channels show that with Minimum Shift Keying (MSK), our receiver using four basis functions (filters) lowers the error floor by more than one order of magnitude with respect to other techniques of comparable complexity. Orthogonal Frequency Shift Keying (FSK) can achieve the same performance as Time-Orthogonal modulation for the slow-fading case, but suffers some degradation over fast-fading channels where it exhibits an error floor. Compared to MSK, however, Orthogonal FSK provides better performance.

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System

The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-division duplex(TDD)mode,the acquired CSI depending on the channel reciprocity is inevitably outdated,leading to outdated beamforming design and then performance degradation.In this paper,a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degradation further,based on the channel prediction technique.Specifically,the statistical characteristics of historical channel prediction errors are exploited and modeled.Moreover,to deal with random error terms,deterministic equivalents are adopted to further explore potential beamforming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance.Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast,compared with the traditional beamforming design.

Antenna calibration using channel prediction for time-varying channels

Traditional antenna calibration methods for time division duplex （TDD） systems asSume that the flee-space channel remains the same during calibration, which is unreasonable under the high-speed rail and other time-varying channel scenarios, and will cause calibration error due to time variability. This paper proposes an antenna calibration method for time-varying channels. In the proposed method, the transceiver first sequentially sends a pilot signal to ob- tain equivalent do^vnlink and uplink channel responses. Then, by predicting the downlink （uplink） channel response fed back from the receiver using the channel prediction algorithm, the transmitter obtains the channel response correspond- ing to the channel response on uplink （downlink）. Finally, the transmitter calculates the transmission calibration factor through the prediction value. Compared with the traditional antenna calibration method, this method can improve the accuracy of the calibration factor. Simulation results show that the performance degradation of antenna calibration can be caused by time-varying channels and the proposed method can well compensate for the performance loss and sig- nificantly improve the antenna calibration performance for time-varying channels.

Pilot Placement for Time-Varying MIMO OFDM Channels with Virtual Subcarriers

The rapid time-variation of a fading multipath environment can impair the performance of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO OFDM). This paper proposes a pilot placement method for MIMO OFDM systems under time-varying channels with the guard band. The time-varying channel is described by complex exponential basis expansion model (BEM). We discuss the least square (LS) channel estimation to obtain the minimum mean square error (MSE) and derive the pilot allocation that can satisfy the minimum MSE with regard to guard band in time-varying channels. It is shown that optimal pilot clusters can distribute non-uniformly in frequency domain and minimize the MSE. We generalize our scheme over G OFDM symbols and compare it with comb pilots. It is demonstrated that the proposed approach is more effective than previous work. Simulation results validate our theoretical analysis.

RIS-Assisted Communications in Correlated Rayleigh Channels:Comparison of Training Overhead Reduction Schemes

Channel training in reconfigurable intelligent surface(RIS)-assisted communications is usually conducted in an on-off manner,resulting in unaffordable training time overhead when the number of RIS elements is large.In this paper,for correlated Rayleigh channels,we compare three typical training overhead reduction schemes,namely RIS element selection(Scheme 1),element grouping(Scheme 2),and statistical CSI-based phase shifts design(Scheme3).For Scheme 1 and Scheme 2,we propose two algorithms to select RIS elements(or form element groups) and determine the optimal number of activated elements(or formed groups),based on the channel correlation information only;for Scheme 3,we consider a semi-definite programming-based approach in the literature,and propose an alternative dominant eigenvector-based method for determining the RIS phase shifts vector.Via extensive simulations,we compare the achievable ergodic rates of these schemes versus the signal-to-noise ratio,the channel correlation level,and the element number-to-coherent time ratio,respectively,and discuss possible switching of the three schemes over these system parameters.At last,operation regions of the considered training overhead reduction schemes are shown in the plane characterized by the system parameters,which provides useful guidelines for practical scheme determination.

Fragmental Weight-Conservation Combining Scheme for Statistical Signal Transmissions under Fast Time-Varying Channels

Statistical Signal Transmission(SST)is a technique based on orthogonal frequency-division multiplexing(OFDM)and adopts cyclostationary features,which can transmit extra information without additional bandwidth.However,the more complicated environment in 5G communication systems,especially the fast time-varying scenarios,will dramatically degrade the performance of the SST.In this paper,we propose a fragmental weight-conservation combining(FWCC)scheme for SST,to overcome its performance degradation under fast time-varying channels.The proposed FWCC scheme consists of three phases:1、incise the received OFDM stream into pieces;2、endue different weights for fine and contaminated pieces,respectively;3、combine cyclic autocorrelation function energies of all the pieces;and 4、compute the final feature and demodulate data of SST.Through these procedures above,the detection accuracy of SST will be theoretically refined under fast time-varying channels.Such an inference is confirmed through numerical results in this paper.It is demonstrated that the BER performance of proposed scheme outperforms that of the original scheme both in ideal channel estimation conditions and in imperfect channel estimation conditions.In addition,we also find the experiential optimal weight distribution strategy for the proposed FWCC scheme,which facilitates practical applications.

Analysis of quality factors for Rayleigh channel waves

To facilitate investigation of the effect of imperfect elastic dissipation on thepropagation of Rayleigh-type channel waves and use of their quality factors in investigationsof the properties of coal seams, a simple method for calculating the quality factor QR isproposed in this paper. Introduction of complex velocities into the dispersion function allowscalculation of the dispersion function of Rayleigh-type channel waves in coal seams. By thecontrol variable method, we analyzed changes in QR with changes in coal seam thickness andP- and S-wave Q-factors within the coal seam and adjacent rock layers. The numerical resultsshow that the trend of the QR curve is consistent with the group velocity curve. The minimumQR value occurs at the Airy phase frequency; the Airy phase frequency decreases as coal seamthickness increases. The value of QR increases with increasing Qs2 （quality factor for S wavein coal seam）. We can compensate for the absorption of Rayleigh-type channel waves usingthe computed QR curve. Inversion of the QR curve can also be used to predict the thicknessesand litholoeies of coal seams.

Concatenated Trellis Coding and CPM for DS/SSMA Communications in Rayleigh Fading Channel

本文在衰落信道下将级联网格编码调制（ＴＣＭ）和连续相位调制（ＣＰＭ）应用于直接序列扩频多址（ＤＳ／ＳＳＭＡ）系统中．作者分别采用了理论分析和数值模拟两种方法研究了级联ＴＣＭ／ＣＰＭＤＳ／ＳＳＭＡ系统的性能，结果表明在相同的条件下采用级联ＴＣＭ／ＣＰＭ编码调制方式的ＤＳ／ＳＳＭＡ系统的错误概率远小于采用单一ＴＣＭ或ＣＰＭ的ＤＳ／ＳＳＭＡ系统的错误概率．

Neural Network and GBSM Based Time-Varying and Stochastic Channel Modeling for 5G Millimeter Wave Communications

In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN models are developed for modeling of path loss together with shadow fading(SF)and joint small scale channel parameters.The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell.The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment.Moreover,the sparse feature of clusters,delay and angular spread,channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.

A k-Coverage Scheduling Scheme in Wireless Sensor Network Based on Hard-Core Process in Rayleigh Fading Channel

Sensing coverage is a fundamental design issue in wireless sensor networks(WSNs),while sensor scheduling ensures coverage degree to the monitored event and extends the network lifetime.In this paper,we address k-coverage scheduling problem in dense WSNs,we maintain a connected k-coverage energy efficiently through a novel Hard-Core based Coordinated Scheduling(HCCS),in which hardcore is a thinning process in stochastic geometry that inhibits more than one active sensor covering any area redundantly in a minimum distance. As compared with existing coordinated scheduling,HCCS allows coordination between sensors with little communication overhead.Moreover,due to the traditional sensing models in k-coverage analysis is unsuitable to describe the characteristic of transmit channel in dense WSNs,we propose a novel sensing model integrating Rayleigh Fading and Distribution of Active sensors(RFDA),and derive the coverage measure and k-coverage probability for the monitored event under RFDA. In addition,we analyze the influence factors,i.e. the transmit condition and monitoring degree to the k-coverage probability. Finally,through Monte Carlo simulations,it is shown that the k-coverage probability of HCCS outperforms that of its random scheduling counterpart.

Secure Audio Transmission Over Wireless Uncorrelated Rayleigh Fading Channel

Audio communications and computer networking play essential roles in our daily lives,including many domains with different scopes.Developments in these technologies are quick.In consequence,there is a dire need to secure these technologies up to date.This paper presents an efficient model for secure audio signal transmission over the wireless noisy uncorrelated Rayleigh fading channel.Also,the performance of the utilized multiple secret keys-based audio cryptosystem is analyzed in different transformation domains.The discrete cosine transform(DCT),the discrete sine transform(DST),and the discrete wavelet transform(DWT)are investigated in the utilizedmultiple secret key-based audio cryptosystem.Simulation results show consistent results with the wireless noisy channel.The performance of the proposed multiple secret keys-based audio cryptosystem can be ranked concerning the employed domain as DWT,DCT,and DST transform techniques.The simulation experiments proved that the presented multiple secret keysbased audio cryptosystemfor audio signals transmitted over the wireless noisy uncorrelatedRayleigh fading channel achieves reliable and secure wireless link utilizing combined multi security layers.

Deep learning-based time-varying channel estimation with basis expansion model for MIMO-OFDM system

For high-speed mobile MIMO-OFDM system,a low-complexity deep learning(DL) based timevarying channel estimation scheme is proposed.To reduce the number of estimated parameters,the basis expansion model(BEM) is employed to model the time-varying channel,which converts the channel estimation into the estimation of the basis coefficient.Specifically,the initial basis coefficients are firstly used to train the neural network in an offline manner,and then the high-precision channel estimation can be obtained by small number of inputs.Moreover,the linear minimum mean square error(LMMSE) estimated channel is considered for the loss function in training phase,which makes the proposed method more practical.Simulation results show that the proposed method has a better performance and lower computational complexity compared with the available schemes,and it is robust to the fast time-varying channel in the high-speed mobile scenarios.

Multiple Vandermonde Decomposition Based Channel Estimation for Mm Wave MIMO High-Mobility Communication in 5G and Beyond

Millimeter wave(mmWave)massive massive multiple input multiple output(MIMO)technique has been regarded as the viable solution for vehicular communications in 5G and beyond.To achieve the substantial increase in date rates,it is important to take an effective channel state information(CSI).However,existing channel estimation strategies are unavailable since the users high-mobility.To solve above issues,in this paper,inspired by a specific antenna structure,we propose a novel approach for fast time-varying channel estimation.Specifically,by considering the vehicle scenario with high-mobility,a corresponding mathematical model is firstly established.Then,based on the special structural of the sparse array,the switch network is used to replace the convention phase shifter of mmWave hybrid system,which can effectively reduce the number of radio-frequency(RF)chains and antennas.Furthermore,by solving the semidefinite programming(SDP)duality problem,the Doppler frequency and path parameters are effectively estimated.Simulation results are shown that the computational complexity and estimation accuracy of the proposed algorithm is superior than that of the traditional schemes.

Network Coding for Wireless Sensor Network Cluster over Rayleigh Fading Channel: Finite State Markov Chain

Network Coding (NC) is confirmed to be power and bandwidth efficient technique, because of the less number of transmitted packets over the network. Wireless Sensor Network (WSN) is usually power limited network application, and in many scenarios it is power and bandwidth limited application. The proposed scenario in this paper applies the advantages of NC over WSN to obtain such power and bandwidth efficient WSN. To take the advantages of NC over the one of the most needed applications i.e., WSN, we come up to what this paper is discussing. We consider a WSN (or its cluster) that consists of M nodes that transmit equal-length information packets to a common destination node D over wireless Rayleigh block-fading channel where the instantaneous SNR is assumed to be constant over a single packet transmission period. Finite-State packet level Markov chain (FSMC) model is applied to give the channel more practical aspect. The simulation results showed that applying NC over the WSN cluster improved the channel bandwidth significantly by decreasing the number of the Automatic Repeat Request (ARQ), resulting in improving the power consumption significantly. The results are collected for different transmission distances to evaluate the behavior to the proposed scenario with regard to the bath losses effect.

A Fuzzy Clustering Algorithm Based on Multipath Component Trajectory for Millimeter Wave Radio Channels

Time-varying channel modeling plays an important role for many applications in time-variant scenarios,while most clustering algorithms focus on static channels and cannot accurately model the channel time-evolution properties.In this paper,a fuzzy clustering algorithm based on multipath component(MPC)trajectory is proposed.Firstly,both the distance and velocity similarities of the MPCs in different snapshots are taken into account to track the MPC trajectory,in which the fuzzy scheme and a kernel function are used to calculate the distance and velocity similarities,respectively.Secondly,a fuzzy MPC trajectory clustering algorithm is proposed to cluster the MPCs in multiple snapshots.The MPCs in a snapshot are clustered according to the membership,which is defined as the probability that a MPC belongs to different clusters.Finally,time-varying channels at 28 GHz are simulated to validate the performance of our proposed algorithm.The results show that our proposed algorithm is able to accurately identify the clusters in time-varying channels compared with the existing clustering algorithms.

Adaptive Switching between Space-Time and Space-Frequency Block Coded OFDM Systems in Rayleigh Fading Channel

This work explores the performances of Space-Time and Space Frequency Coded Orthogonal Frequency Division Multiplexing (OFDM) with simple two branches transmit diversity scheme. The combination of multiple-antenna and orthogonal frequency division multiplexing (OFDM) provides reliable communications over frequency selective fading channels. We focus on the application of space-time block codes (STBC) and space-frequency block codes (SFBC) in OFDM systems over time-varying and frequency-selective channels. SFBC transmitter shows superior performance in fast varying channels while STBC shows better performance in frequency selective channels. A switching technique is presented that selects an appropriate transmission scheme between the STBC and SFBC assuming Rayleigh fading model.

On the Capacity of CPM MIMO Systems over Band-Limited Channels

Capacity analysis is a fundamental and important issue for continuous phase modulation (CPM) signals. In the letter, we investigate the capacity formula of CPM MIMO systems. Using Finite State Machine (FSM), the CPM symbols can be modeled as Markov source by combining channel and CPM modulation. Thus the capacity of CPM signals can be derived in form of the erroneous probability and normalized CPM bandwidth. In addition, the capacity of CPM MIMO systems is derived over Gaussian channels and Rayleigh channels. Finally, numerical simulations are implemented according to various parameters such as modulation scheme, modulation index h, memory length L, and antenna configuration.

Sparse Recovery of Linear Time-Varying Channel in OFDM System

In order to improve the performance of linear time-varying（LTV）channel estimation,based on the sparsity of channel taps in time domain,a sparse recovery method of LTV channel in orthogonal frequency division multiplexing（OFDM）system is proposed.Firstly,based on the compressive sensing theory,the average of the channel taps over one symbol duration in the LTV channel model is estimated.Secondly,in order to deal with the inter-carrier interference（ICI）,the group-pilot design criterion is used based on the minimization of mutual coherence of the measurement.Finally,an efficient pilot pattern optimization algorithm is proposed by a dual layer loops iteration.The simulation results show that the new method uses less pilots,has a smaller bit error ratio（BER）,and greater ability to deal with Doppler frequency shift than the traditional method does.

Framework for Random Power Allocation of Wireless Sensor Networks in Fading Channels

In naturally deaf wireless sensor networks or generally when there is no feedback channel, the fixed-level transmit power of all nodes is the conventional and practical power allocation method. Using random power allocation for the broadcasting nodes has been recently proposed to overcome the limitations and problems of the fixed power allocation. However, the previous work discussed only the performance analysis when uniform power allocation is used for quasi-static channels. This paper gives a general framework to evaluate the performance (in terms of outage and average transmit power) of any truncated probability density function of the random allocated power. Furthermore, dynamic Rayleigh fading channel is considered during the performance analysis which gives more realistic results that the AWGN channels assumed in the previous work. The main objective of this paper is to evaluate the communication performance when general random power allocation is used. Furthermore, the truncated inverse exponential probability distribution of the random power allocation is proposed and compared with the fixed and the uniform power allocations. The performance analysis for the proposed schemes are given mathematically and evaluated via intensive simulations.