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Abstract Reconfigurable intelligent surface (RIS) can provide unprecedented spectral efficiency gains and excellent ability to manipulate electromagnetic waves. This article considered a RIS‐assisted multiuser multiple‐input multiple‐output (MIMO) downlink system, where the beamforming at the base station and RIS are jointly designed to maximize the sum‐rate. For the large dimension scenario and high‐rank beamforming matrix, the accurate deterministic approximations from random matrix theory are then utilized to simplify the RIS‐assisted MIMO systems. The asymptotical signal‐to‐interference‐plus‐noise ratio values obtained through random matrix theory is infinitely close to the theoretical limits calculated by accurately iteration. And the performance of the proposed algorithm computed via the sharing second‐order channel statistics matches that of the RIS algorithm with sharing full channel state information asymptotically. The deterministic approximations are instrumental to get improvement into the structure of the optimal beamforming and to reduce the implementation complexity in large‐scale MIMO system. Numerical simulations results are provided to evaluate and verify the accuracy of the asymptotic results obtained from the proposed algorithm in the finite system regime. With the complex operation process of large dimension matrix reducing to the deterministic approximations, a lower computational complexity can be obtained compared with other methods.

【Objective】Power Line Carrier(PLC)communication adopts the frame burst transmission mode. Due to the carrier frequency offset between transceivers, various noise and time-varying characteristics of PLC channel and the system has no dedicated reference signal. The traditional channel estimation has no tracking and prediction ability for the channel, which leads to the deterioration of the PLC system performance.【Methods】Aiming at the existing problems, this paper proposes a Denoising Long Short Term Memory (DnLSTM) neural network based on Long Short Term Memory (LSTM) neural network and Denoising Convolutional Neural Network (DnCNN), which is used for PLC channel estimation. First, offline training is performed on DnLSTM and the parameters are saved after training. Then the trained parameters are deployed in PLC system. After loading parameters, online prediction is performed to obtain the predicted PLC system channel estimation. In the simulation of PLC system, this paper uses Least Squares (LS) algorithm, Minimum Mean Square Error (MMSE) algorithm and DnLSTM to estimate the channel response, and gives the simulation results under the conditions of Additive White Gaussian Noise (AWGN), combined noise, impulsive noise and colored noise. Meanwhile, simulations for different number of preamble symbols for channel estimation are performed.【Results】The results show that there is a relationship between the accuracy of DnLSTM channel estimation and the number of preamble symbols. Using four preamble symbols for channel estimation, its estimation accuracy is better than LS and close to MMSE algorithm. DnLSTM has a good ability to resist carrier frequency offset and channel time-varying. When the number of preamble symbols for channel estimation increases, PLC system performance with low Singnal to Noise Ratio (SNR) gets better and PLC system performance is similar with high SNR.【Conclusion】According to simulation results above, it can be concluded that DnLSTM, which is based on DnCNN and LSTM, can predict PLC system channel response with frequency offset very well and it can track the varying PLC system channel response in real time.

A novel channel estimation method based on deep learning algorithm is proposed for large-scale IoT networks. We consider asymmetric backscatter communication system to maintain low-power at sensor nodes. In order to obtain channel data, we design denoising autoencoder which consists of encoder with Feedforward Neural Network (FNN) and decoder with Convolutional Neural Network (CNN). Finally, the channel estimation error is minimized, while the pilots are optimized. Especially, we adopt beamforming technique that relies only on cascaded channel data to reduce complexity in multi-sensor system. It is shown that the accuracy is slightly degraded while the complexity is greatly reduced.

Abstract In the paper, we investigate a massive machine-type communication (mMTC), where numerous single-antenna users communicate with a single-antenna base station while being active. However, the status of user can undergoes multiple transitions between active and inactive states across whole consecutive intervals. Then, we formulate the problem of joint user activity detection and channel estimation within the dynamic compressed sensing (DCS) framework, considering the temporally-correlated user activity across the entire consecutive intervals. To be specific, we introduce a new hybrid vector approximate message passing algorithm for DCS (HyVAMP-DCS). The proposed algorithm comprises a VAMP block for estimating channel and a loopy belief propagation (LBP) block for detecting user activity. Moreover, these two blocks can exchange messages, enhancing the performance of both channel estimation and user activity detection. Importantly, compared to the fragile GAMP algorithm, VAMP is robust and applicable to a much broader class of large random matrices. Furthermore, the fixed points of VAMP’s state evolution align with the replica prediction of the minimum mean-squared error. The simulation results illustrate the superiority of HyVAMP-DCS, demonstrating its significant outperformance over HyGAMP-DCS.

In order to examine the potential and synergetic aspects of intelligent reflecting surface (IRS) techniques for Internet-of-Things (IoT), we study an IRS-aided Long Range (LoRa) system in this paper. Specifically, to facilitate the acquisition of accurate channel state information (CSI) for effective reflection of LoRa signals, we first propose an optimal training design for the least squares channel estimation with LoRa modulation, and then, by utilizing the acquired CSI, we develop a high-performing passive beamforming scheme based on a signal-to-ratio (SNR) criterion. Numerical results show that the proposed training design considerably outperforms the baseline schemes, and the proposed passive beamforming design results in a significant improvement in performance over that of the conventional LoRa system, thereby demonstrating the feasibility of extending coverage areas of LoRa systems with the aid of IRS.

Summary: In the context of Reconfigurable Intelligent Surface (RIS) assisted millimeter wave communications within the Multiple Input Multiple Output (MIMO) system, Channel Estimation (CE) poses a significant challenge due to signal sparsity and blockages between transmitters and receivers. Consequently, communication faces difficulties without a direct line‐of‐sight path, leading to increased pilot overhead. To address these obstacles, this research introduces a channel estimation approach for RIS‐aided millimeter wave communication employing the Multidimensional Runge Kutta Orthogonal Matching Pursuit (MRKOMP) algorithm in MIMO systems. The proposed method aims to enhance network coverage and channel capacity for efficient data transmission. By utilizing the multiscale cumulative residual distribution entropy function, the method establishes an indirect path when the line‐of‐sight is obstructed. Furthermore, the multidimensional orthogonal matching pursuit methodology calculates sparsity levels and facilitates sparse recovery. Optimization of the weight parameter through the Runge Kutta optimizer effectively eliminates sparsity, resulting in improved spectral efficiency and RIS gain. Simulation results demonstrate a significant 99.9% enhancement in network capacity and spectral efficiency compared to existing methods. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) can provide unprecedented spectral efficiency gains and excellent ability to manipulate electromagnetic waves. This article considered a RIS‐assisted multiuser multiple‐input multiple‐output (MIMO) downlink system, where the beamforming at the base station and RIS are jointly designed to maximize the sum‐rate. For the large dimension scenario and high‐rank beamforming matrix, the accurate deterministic approximations from random matrix theory are then utilized to simplify the RIS‐assisted MIMO systems. The asymptotical signal‐to‐interference‐plus‐noise ratio values obtained through random matrix theory is infinitely close to the theoretical limits calculated by accurately iteration. And the performance of the proposed algorithm computed via the sharing second‐order channel statistics matches that of the RIS algorithm with sharing full channel state information asymptotically. The deterministic approximations are instrumental to get improvement into the structure of the optimal beamforming and to reduce the implementation complexity in large‐scale MIMO system. Numerical simulations results are provided to evaluate and verify the accuracy of the asymptotic results obtained from the proposed algorithm in the finite system regime. With the complex operation process of large dimension matrix reducing to the deterministic approximations, a lower computational complexity can be obtained compared with other methods. [ABSTRACT FROM AUTHOR]

Summary The channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM (DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM) is proposed in this manuscript. In OFDM, there is a non‐stationary channel physical appearance during channel estimation (CE). Therefore in this work, Hyb AOA‐COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%; lesser bit error rate 98.33%, 83.12%, and 88.96%; and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO‐OFDM system using deep neural network and MN‐based improved AMO model (DNN‐IAMO‐MIMO‐OFDM), MIMO‐OFDM systems using the deep learning and optimization (RBFNN‐PSO‐MIMO‐OFDM), and MIMO‐OFDM systems using hybrid neural network (HNN‐CSI‐MIMO‐OFDM) respectively. [ABSTRACT FROM AUTHOR]

Objectives: This pilot study primarily aimed to detect the adherence as well as the effect size required to estimate the actual sample size needed for a larger scale study to compare and evaluate the effectiveness of two extracorporeal shock wave therapy (ESWT) protocols along, with a physical therapy program in reducing pain and improving function among patients suffering from plantar fasciitis. The study also aimed to report the effects of the ESWT protocols used on pain and function. Methods: A total of 26 participants took part in the study, including 17 females and 9 males. The average age of the participants was 34 years with a body mass index (BMI) of 23 kg/m2. Participants were divided into three equal groups; Group A received ESWT at a frequency of 15 Hz and intensity of 3, Group B received ESWT at a frequency of 10 Hz and intensity of 4, while Group C underwent the selected physical therapy program along with sham shock wave therapy as a control. Pain levels were assessed using the Visual Analog Scale (VAS) while functional improvements were evaluated using the Foot Function Index (FFI). Data was collected prior to treatment, after three sessions and at the end of six weeks (after six sessions). Results: The three groups were well matched, and the results revealed high adherence rates (90%, 90% and 80% respectively). Results also indicated reductions in pain levels and improvements in function for both intervention groups when compared to the control group. Group A demonstrated better outcomes compared to Group B while Group C showed relatively less improvement. Conclusion: The study concluded a high adherence rate for the three groups as well as a small effect size detected of 0.282 that would suggest a total of 123 participants to be required to replicate the study on a larger scale. With regards to the findings of this pilot, the combination of ESWT and a targeted physical therapy program revealed a possible effective therapeutic approach for plantar fasciitis, with a higher frequency potentially yielding more favourable results. [ABSTRACT FROM AUTHOR]

In this article, we propose a joint waveform based on universal filtered multicarrier (UFMC) chirp for radar jamming and communication joint systems. Modulation of radar jamming chirp signals and communication signals on different subcarrier groups in the UFMC sub-band is used to achieve the waveform design. The jamming signal in the waveform contains a frequency shift coefficient that depends on the delay time, which can effectively improve the anti-frequency hopping ability and enhance the overall jamming efficiency. Simultaneously jamming signals can provide assistance in channel estimation and equalization of communication, improving the information transmission quality of communication subsystems. We concluded through reasonable trade-off analysis that the combined weight of radar jamming and communication is closely related to the overall performance of the waveform. The simulation results show that the proposed UFMC chirp synthesized waveform has good jamming and communication performance. Software defined radio (SDR) simulation experiments demonstrated the effectiveness of this method in practical environments. [ABSTRACT FROM AUTHOR]

Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l 1 − l 1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Simultaneous transmit and receive wireless communications have been highlighted for their potential to double the spectral efficiency. However, it is necessary to mitigate self-interference (SI). Considering both the SI channel and remote transmission (RT) channel need to be estimated before equalizing the received signal, we propose two adaptive algorithms for linear and nonlinear self-interference cancellation (SIC), based on a multi-layered joint channel estimator structure. The proposed algorithms estimate the RT channel while performing SIC, and the multi-layered structure ensures improved performance across various interference-to-signal ratios. The M-estimate function enhances the robustness of the algorithm, allowing it to converge even when affected by impulsive noise. For nonlinear SIC, this paper introduces an adaptive algorithm based on generalized Hammerstein polynomial basis functions. The simulation results indicate that this approach achieves a better convergence speed and normalized mean squared difference compared to existing SIC methods, leading to a lower system bit error rate. [ABSTRACT FROM AUTHOR]

Several state-of-the-art convolutional neural networks (CNNs)-based methods are available for image denoising tasks. CNNs are typically trained using the backpropagation algorithm, which requires all operations in the network to be differentiable. Most CNN operations satisfy this requirement and can be applied to backpropagation-based training algorithms. However, some transforms, including wavelet transform, which is useful for speeding up CNN computations as well as performing multi-resolution analysis, are not strictly differentiable. This paper addresses this challenge by proposing a wavelet-like transform that is differentiable. This new design is, in fact, a new CNN architecture named semi-wavelet, specific edge convolutional neural network (SW/SE-CNN), consisting of three newly designed layers. The first layer is a Semi-Wavelet (SW)-based layer which is a differential down-sampling operator for wavelet approximation. That is, the SW layer converts the input image into four channels. Three of these channels are estimations of the vertical, horizontal, and diagonal edges of the original image; and the fourth channel is a down-sampled version of it. The second proposed layer, called Semi-Wavelet Inverse (SWI), is to restore the original image by using the four SW output channels. Additionally, a specific edge extractor (SE), as another new layer, is designed on the basis of the well-known Sobel operator to extract specific edges of the image. The reason behind proposing the SE layer is to provide more edge information for the network; and the motive for including the SW layer is to speed the network up as well as multi-resolution analysis. Then, the new SW/SE-CNN architecture is implemented for Gaussian image denoising. The experimental results show that the new SW/SE-CNN outperformed the state-of-the-art methods for Gaussian image denoising based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) measurements for grayscale as well as color images. [ABSTRACT FROM AUTHOR]

Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method utilizing superimposed training sequences. Compared with conventional schemes, this method offers higher spectral efficiency and better adaptability to time-varying channels owing to its temporal traversal. To ensure success in this scheme, it is crucial to obtain time-varying channel estimation and data detection at low SNRs given that superimposed training sequences consume power resources. To achieve this goal, we initially employ coarse channel estimation utilizing superimposed training sequences. Subsequently, we employ approximate message passing algorithms based on the estimated channels for data detection, followed by iterative channel estimation and equalization based on estimated symbols. We devise an approximate message passing channel estimation method grounded on a Gaussian mixture model and refine its hyperparameters through the expectation maximization algorithm. Then, we refine the channel information based on time correlation by employing an autoregressive hidden Markov model. Lastly, we perform numerical simulations of communication systems by utilizing a time-varying channel toolbox to simulate time-varying channels, and we validate the feasibility of the proposed communication system using experimental field data. [ABSTRACT FROM AUTHOR]

In this letter, an optimal calibration factor for mutual information effective signal‐to‐interference‐plus‐noise ratio (SINR) mapping (MIESM) scheme in fifth‐generation new radio (5G NR) is provided. By applying the optimal calibration factor to the MIESM scheme, the estimation accuracy of the effective SINR can be improved. The optimal calibration factor minimizes the mean squared error (MSE) of the difference between the estimated effective SINR and the required SNR under additive white Gaussian noise (AWGN) at a block error rate (BLER) of 10%. Simulation results show that the optimized MIESM scheme outperforms the non‐optimized MIESM scheme. [ABSTRACT FROM AUTHOR]

For the channel estimation of broadband OFDM system assisted by IRS, most of the current researches are based on the full pilot setting on a single symbol, and excessive cascaded channel coefficients will lead to large pilot overhead. This paper proposed a two-stage channel estimation scheme based on block sparse in time-angle domain. Firstly, by analyzing the common sparse characteristics of the channel in the time domain and the angular domain, this paper transformed the cascaded channel matrix into a block sparse representation in the time domain-angular domain, and converted the channel estimation problem into a block sparse matrix recovery problem. Secondly, this paper proposed a two-stage sparse channel estimation scheme to sparsely recover the cascaded channel considering the transmission pilot limitation and the time-angle domain block sparse characteristics. Simulation results show that compared to the other three benchmark schemes, the proposed scheme can achieve better channel estimation performance with fewer pilots, effectively reducing the pilot overhead and achieving higher estimation accuracy. [ABSTRACT FROM AUTHOR]

High-accuracy positioning plays an important role in future 5G-Advanced cellular networks. However, it is quite challenging to utilize 5G signals to estimate positioning parameters accurately under multipath environments. To establish a high-precision real-time location system (RTLS), which is suitable for indoor scenarios, advanced parameter estimation techniques with low computational complexity should be developed. This paper proposes a modified subspace-based time-of-arrival (TOA) estimation algorithm for 5G signals with ultra-high accuracy and reduced complexity under multipath environments. A novel smoothing scheme termed as grouped subcarrier smoothing (GSS) is proposed to reduce the dimension of the covariance matrix involved in typical subspace-based methods. Then, the computational complexity of the corresponding eigenspace calculation is reduced. The TOA estimation performance is enhanced by constructing multiple snapshots of the estimated channel frequency response vector in the frequency domain. Finally, the super-resolution TOA estimate can be obtained using only one orthogonal frequency division multiplexing (OFDM) symbol. Simulation results show that the proposed method achieves centimeter-level TOA estimation accuracy under multipath channels when the signal-to-noise ratio (SNR) is high. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-channel speech separation method based on spatial estimation via sparse Bayesian inference (SBI) and nonnegative matrix factorization (NMF). The spatial information estimated by traditional compressed sensing (CS) models is insufficient when two microphones receive limited columns of mixed signals. Considering the sparsity of peak values in the cross-correlation spectrum between two received signals, the proposed method builds a new CS model based on cross-correlation spectrum and applies SBI algorithm to solve this model to improve the estimation accuracy of spatial information. Combined the spatial information with the spectral features decomposed by NMF, NMF coefficient matrix masks belonging to individual source are generated for pre-separation. To mitigate retained potential interference components, a post-separation processing stage is designed using an expectation maximization (EM) algorithm based on a Gaussian mixture model (GMM). The estimated spatial information and binary time–frequency masks are used for parameter initialization of the EM algorithm. The experimental results using real-world speech data show that the proposed method can achieve better separation performance compared to various existing methods. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, multiple input multiple outputs with orthogonal frequency division multiplexing (MIMO‐OFDM) provide better communication performance that can be applied to the fast‐growing Internet of Things (IoTs). In underwater IoT, information fades away rapidly due to varying water conditions. Therefore, the MIMO model can be applied to the OFDM acoustic system, enabling high‐speed data transmission without affecting the channel effectively. However, detecting the underwater signal and estimating the channel is highly necessary for enhancing underwater acoustic communication (UWAC). Recently, many techniques have been introduced for effectively performing signal detection and channel estimation. However, those techniques face high time complexity due to increased channel interferences and noises during data transmission. Hence, this article brings a novel technique for SD and CE for the UWAC‐IoT‐enabled MIMO‐OFDM system. An adaptive recursive least square (ARLS) technique is proposed in this study for CE that aids in evaluating the acoustic channel parameters effectively. For performing SD, a bi‐directional deep pelican convolutional neural network (BDPCNN) technique is introduced to ensure the presence and absence of signals at the receiver end. The proposed method is analyzed via the MATLAB platform, and the performances are analyzed under different water types like turbid water, coastal water, clear ocean water, and pure seawater. Different performance metrics like bit error rate (BER), mean square error (MSE), energy efficiency (EE), and time complexity are analyzed with different existing techniques. The experimental section obtains the BER of 0.0086, 0.013, 0.017, and 0.021 for turbid, coastal, clear, and pure seawater, respectively. [ABSTRACT FROM AUTHOR]

Summary: The future generation of wireless communications systems uses the generalized frequency division multiplexing (GFDM) due to its viable candidate waveform to perform multiple user scheduling. It is a non‐orthogonal waveform susceptible to intercarrier and intersymbol interference (ISI); still, it offers flexible pulse shaping that enhances the efficiency of user scheduling. Here, to accomplish spatial diversity, the multiple‐input multiple‐output (MIMO) is incorporated with GFDM to enhance the performance; there is also an extra inter‐antenna interference problem that limits the model's performance. The issue concerning inter‐antenna interference can be resolved by adopting pilot‐based information transfer. The detection of a signal at the receiver based on the minimum mean square error has the issue of computational complexity while enhancing the estimation quality of a detector. Hence, a low‐complexity channel estimation technique is proposed in this research using the V‐degree polynomial‐based channel estimation technique, wherein the cubic order computation is reduced to square order. Also, the proposed adaptive pulse shaping technique, wherein the filter coefficient is optimized using the gazelle optimization algorithm (GOA) to provide optimal pulse shaping filter parameters by employing bit error rate (BER) as the objective function. The performance of a proposed V‐degree polynomial‐based channel estimation is analyzed based on various assessment measures like BER and MSE and acquired the minimal values of 5.75E‐05 and 2.07E‐05, respectively. [ABSTRACT FROM AUTHOR]

A digital coding metasurface is a platform connecting the digital space and electromagnetic wave space, and has therefore gained much attention due to its intriguing value in reshaping wireless channels and realizing new communication architectures. Correspondingly, there is an urgent need for electromagnetic information theory that reveals the upper limit of communication capacity and supports the accurate design of metasurface-based communication systems. To this end, we propose a macroscopic model and a statistical model of the digital coding metasurface. The macroscopic model uniformly accommodates both digital and electromagnetic aspects of the meta-atoms and predicts all possible scattered fields of the digital coding metasurface based on a small number of simulations or measurements. Full-wave simulations and experimental results show that the macroscopic model is feasible and accurate. A statistical model is further proposed to correlate the mutual coupling between meta-atoms with covariance and to calculate the entropy of the equivalent currents of digital coding metasurface. These two models can help reconfigurable intelligent surfaces achieve more accurate beamforming and channel estimation, and thus improve signal power and coverage. Moreover, the models will encourage the creation of a precoding codebook in metasurface-based direct digital modulation systems, with the aim of approaching the upper limit of channel capacity. With these two models, the concepts of current space and current entropy, as well as the analysis of information loss from the coding space to wave space, is established for the first time, helping to bridge the gap between the digital world and the physical world, and advancing developments of electromagnetic information theory and new-architecture wireless systems. [ABSTRACT FROM AUTHOR]

A matched filtering (SMF) channel estimation method based on sequence pilots can solve the problem of difficulty and high complexity in channel estimation for existing orthogonal time-frequency-space (OTFS) modulation systems in terms of peak to average ratio and fractional Doppler channels. This algorithm first combines the sequence pilot and data into a frame, and obtains the number of paths, delay, and integer Doppler based on the correlation of the sequence. Then, it estimates the fractional Doppler tap and channel gain through matched filtering to obtain channel state information. Finally, the channel gain and initial phase are updated through channel features. The simulation results show that this method improves the peak to average ratio and channel estimation performance compared to channel estimation based on embedded pulse pilots. Compared to traditional sequence pilots, this method can estimate fractional Doppler taps and provide more accurate channel state information. This channel estimation method is more universal. [ABSTRACT FROM AUTHOR]

To tackle massive data, subsampling is a practical approach to select the more informative data points. However, when responses are expensive to measure, developing efficient subsampling schemes is challenging, and an optimal sampling approach under measurement constraints was developed to meet this challenge. This method uses the inverses of optimal sampling probabilities to reweight the objective function, which assigns smaller weights to the more important data points. Thus, the estimation efficiency of the resulting estimator can be improved. In this paper, we propose an unweighted estimating procedure based on optimal subsamples to obtain a more efficient estimator. We obtain the unconditional asymptotic distribution of the estimator via martingale techniques without conditioning on the pilot estimate, which has been less investigated in the existing subsampling literature. Both asymptotic results and numerical results show that the unweighted estimator is more efficient in parameter estimation. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of velocity distribution is the fundamental information hydraulic engineers need to estimate an alluvial channel's discharge and stage characteristics. This study examines the Tsallis entropy approach for estimating streamwise velocity patterns in open channels. Entropy, which quantifies system uncertainty, has been applied in hydraulic research to account for variables such as shear strength, silt content, and flow velocities. However, its applicability to non-uniform channel sections remains unexplored. In the current work, the velocities estimation under various experimental conditions was calculated using the Tsallis entropy approach, wherein the random variable employed for constructing the velocity estimations was the time-averaged normalised velocity. This study considered two experimental conditions: (1) channels with attached spurs under seepage and non-seepage conditions and (2) channels with a 31-degree bank slope with and without an upstream pit. The velocity pattern obtained closely corresponds to the experimental data, exhibiting significant accuracy. However, it should be noted that the accuracy of the velocity pattern is slightly diminished in the region near the spur field when the y / D value is below 0.3. The difference can be caused by factors such as the area's non-uniform cross-section, sediment interaction along the bed, and secondary currents, which ultimately affect the velocity profile. [ABSTRACT FROM AUTHOR]

Recently, cell-free (CF) architectures, in which every user can potentially communicate with every base station, have received a lot of attention. This paper considers the uplink of fully and partially centralized CF networks where unmanned aerial vehicles serve as flying base stations (FBSs). A subset of FBSs participates in the reception of each user and a subset of users is received by each FBS. Deterministic equivalent expressions, exact asymptotically in the subset sizes and approximate for finite dimensions thereof, are derived for the spectral efficiency under Rician fading. Capitalizing on these expressions, the FBS deployment problem is investigated for different receiver architectures. The nonconvex deployment problem, tackled through a combination of gradient-based and Gibbs sampling algorithms, results in a superior performance with respect to a square grid deployment; this superiority extends to the minimum and aggregate spectral efficiency for both fully and partially centralized cell-free networks.

Aiming at the problem of low accuracy of traditional channel estimation algorithms in the harsh environment of underground coal mines, this paper proposes an improved Super Resolution Convdutional Network (SRCNN) for channel estimation. In the improved SRCNN model, the estimated value at the pilot frequency is used as input, and the improved SRCNN model replaces the interpolation process in the traditional channel estimation algorithm to reduce the complexity, and the attention mechanism ECA module is added to improve the learning of channel features to achieve more accurate channel estimation for the underground coal mine environment. Simulation results show that the channel estimation algorithm of the improved SRCNN model outperforms the traditional channel estimation algorithm and improves the estimation accuracy by one order of magnitude compared with the channel estimation of the SRCNN model.

The great potentials of massive Multiple-Input Multiple-Output (MIMO) in Frequency Division Duplex (FDD) mode can be fully exploited when the downlink Channel State Information (CSI) is available at base stations. However, the accurate CSI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas. In this paper, we propose a deep learning based joint channel estimation and feedback framework, which comprehensively realizes the estimation, compression, and reconstruction of downlink channels in FDD massive MIMO systems. Two networks are constructed to perform estimation and feedback explicitly and implicitly. The explicit network adopts a multi-Signal-to-Noise-Ratios (SNRs) technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels, while the implicit network directly compresses pilots and sends them back to reduce network parameters. Quantization module is also designed to generate data-bearing bitstreams. Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

Abstract Hybrid combiner and precoder architectures, radio frequency (RF) chain, analog phase shifters, digital-to-analog converter (DAC), and analog-to-digital converter (ADC) are components of a millimeter wave cellular system. Prior works in the area of millimeter wave cellular system design employ receiver with infinite bit and large amount of RF chain that scales linearly with the quantity of transmitting and receiving antennas. This mode of design no doubt increases power demand or requirement of a typical millimeter wave system. In this work, hybrid architecture with few RF chains and small number of ADC bits are proposed and are used as candidate for millimeter wave channel estimation and cellular communication. In that connection, least square (LS), orthogonal matching pursuit (OMP), compressed sampling matching pursuit (CoSAMP), and deep learning (DL) techniques are utilized for analytical investigation. Indeed, computational results reveal that, when ADC consisting of uniform mid- rise quantizer is employed, the performance of 4 and 6 bits at signal-to-noise ratio (SNR) values of − 10 dB and 20 dB is at par with infinite bit (unquantized case). As a validation, DL compares favorably well with adaptive compressed sensing (ACS) technique previously used in the literature for channel estimation, while OMP and CoSAMP show better performance than ACS.

This paper provides an adaptive 5G-NR based solution for non-terrestrial networks (NTN) to support the high mobility of the user equipment (UE). A front-loaded demodulation reference signal (DM-RS) symbol pattern with additional orthogonal symbols is proposed to support UE mobility 500 km/h. We develop a 5G-NR link-level simulator (LLS) to analyze the performance of physical downlink shared channel (PDSCH) under the proposed front-loaded DM-RS symbol pattern, tapped delay line (TDL) channel model, and precoding-based beamforming/MIMO schemes. The simulation results show that the proposed channel estimation method based on front-loaded DM-RS helps to improve the link reliability and achieves the maximum spectral efficiency up to 5.05 bps/Hz under 64-QAM, which can also contribute to the evolution of NTN in 6G networks to support the transonic speed mobility of UE.

In fifth-generation and beyond (5GB) wireless communication systems, Cloud Radio Access Network (C-RAN) is recognized as a vital technology. In this endeavor, User-Centric C-RAN (UC-RAN) promises a significant reduction in the channel training overhead because only the intra-cluster channel state information (CSI) is necessary for successful high data rate transmission. However, the network performance may be degraded by inter-cluster interference. Furthermore, such networks are susceptible to the pilot contamination effect, which leads to a major restriction on overall system performance. To tackle this issue, we introduce a channel estimation (CE) approach based on iterative variational Bayesian inference (IVBI)- called the least square IVBI (L-IVBI) scheme. This method consists of two stages: initialization and iteration, and is designed for the UC-RAN system. The initialization stage includes a coarse channel estimate, further refined in the iteration stage. We follow the alternative minimization method to estimate the desired channel in the iteration stage. Extensive simulation results for the UC-RAN system validate the proposed algorithms. We also provide the derivation of the Bayesian Cramer-Rao bound (BCRB) for the proposed estimator. The novel approach significantly outperforms the state-of-the-art in terms of normalized mean square error (NMSE), spectral efficiency (SE), and bit error rate (BER).

Wireless networks are increasingly relying on machine learning (ML) paradigms to provide various services at the user level. Yet, it remains impractical for users to offload their collected data set to a cloud server for centrally training their local ML model. Federated learning (FL), which aims to collaboratively train a global ML model by leveraging the distributed wireless computation resources across users without exchanging their local information, is therefore deemed as a promising solution for enabling intelligent wireless networks in the data-driven society of the future. Recently, reconfigurable intelligent metasurfaces (RIMs) have emerged as a revolutionary technology, offering a controllable means for increasing signal diversity and reshaping transmission channels, without implementation constraints traditionally associated with multi-antenna systems. In this paper, we present a comprehensive survey of recent works on the applications of FL to RIM-aided communications. We first review the fundamental basis of FL with an emphasis on distributed learning mechanisms, as well as the operating principles of RIMs, including tuning mechanisms, operation modes, and deployment options. We then proceed with an in-depth survey of literature on FL-based approaches recently proposed for the solution of three key interrelated problems in RIM-aided wireless networks, namely: channel estimation (CE), passive beamforming (PBF) and resource allocation (RA). In each case, we illustrate the discussion by introducing an expanded FL (EFL) framework in which only a subset of active users partake in the distributed training process, thereby allowing to reduce transmission overhead. Lastly, we discuss some current challenges and promising research avenues for leveraging the full potential of FL in future RIM-aided extremely large-scale multiple-input-multiple-output (XL-MIMO) networks.

Extreme large-scale multiple-input multiple-output (XL-MIMO) is one of the key technologies for future 6G communications. Channel estimation plays a crucial role in XL-MIMO systems, as accurate Channel State Information (CSI) is essential for effective signal transmission. The existing channel estimation methods mainly distinguish between far-field channel estimation and near-field channel estimation. In the case where the sparsity of the channel is known, the traditional Orthogonal Matching Pursuit (OMP) algorithm is relied upon to estimate the hybrid-field channel in XL-MIMO systems.To overcome these limitations, in this paper, we propose a joint hybrid-field channel estimation scheme and adopt the Simultaneous Weighted Orthogonal Matching Pursuit (SWOMP) algorithm to effectively address these issues. Specifically, to more effectively estimate the hybrid-field channel, we propose a joint channel estimation approach that no longer distinguishes between far-field and near-field channel estimation methods. In the case where the sparsity of the hybrid-field channel is unknown, we employ the SWOMP algorithm to accurately estimate the channel state information. Furthermore, we substitute the Sherman-Morrison-Woodbury transform for the matrix inversion operation in the SWOMP algorithm, which does not reduce computational complexity but provides a novel approach to matrix inversion. Based on this, finally, we further propose a low-complexity SWOMP algorithm based on the Gauss-Seidel method transformation. Simulation results demonstrate that the proposed approach can obtain more accurate channel state information compared to traditional methods in XL-MIMO systems.

While reconfigurable intelligent surface (RIS) technology has been shown to provide numerous benefits to wireless systems, in the hands of an adversary such technology can also be used to disrupt communication links. This paper describes and analyzes an RIS-based attack on multi-antenna wireless systems that operate in time-division duplex mode under the assumption of channel reciprocity. In particular, we show how an RIS with a non-diagonal (ND) phase shift matrix (referred to here as an ND-RIS) can be deployed to maliciously break the channel reciprocity and hence degrade the downlink network performance. Such an attack is entirely passive and difficult to detect and counteract. We provide a theoretical analysis of the degradation in the ergodic sum rate that results when an arbitrary malicious ND-RIS is deployed and design an approach based on the genetic algorithm for optimizing the ND structure under partial knowledge of the available channel state information. Our simulation results validate the analysis and demonstrate that an ND-RIS channel reciprocity attack can dramatically reduce the downlink throughput.

The directions and gains of the paths with significant power can reconstruct mm-wave channel estimation, attributed to spatial sparsity resulting from severe propagation loss. Leveraging this feature, the two-dimensional beamspace method of direction estimation (2-D BMODE) is developed to estimate the path directions, including angles of departure and arrival. The least-squares(LS) method is then employed to estimate the path gains. Different from the beamspace two-dimensional multiple signal classification (MUSIC) method, the method proposed in this paper significantly ruduces computational complexity without spectral search. Moreover, the DODs and DOAs can be paired automatically by reduced-dimension(RD) MUSIC. Meanwhile, the performance of this method deteriorates when the covariance matrix of gains isn’t of full column rank. This paper also enhances the performance using spatial smoothing technique and oblique projecting to overcome the influence of rank deficiency. The 2-D BMODE method, as illustrated in this paper, outperforms in terms of both unity in computational complexity and estimation accuracy. Numerical simulations validate the advantages and demonstrate the method can serve as a superior alternative.

In this study, a space-time transmission scheme is proposed to tackle the limitations of channel estimation with orthogonal pilot information in colocated multiple-input multiple-output systems with several transmitting and receiving antennas. Channel information is obtained using orthogonal pilots. Channel estimation introduces pilot heads required to estimate a channel. This leads to bandwidth insufficiency. As a result, trade-offs exist between the number of pilots required to estimate a channel versus spectral efficiency. The detection of data symbols is performed using the maximum likelihood decoding method as it provides a consistent approach to parameter estimation problems. The moment-generating function of the instantaneous signal-to-noise ratio is used to drive an approximate expression of the symbol error rate for the proposed scheme. Furthermore, the order of diversity is less by one than the number of receiver antennas used in the proposed scheme. The effect of the length of a pilot sequence on the proposed scheme's performance is also investigated.

In this paper estimation of uplink channels using tensor modeling is addressed for multiple users in a reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) communication. The coherence interval is divided into structured frames of pilot symbols transmitted by the users and pattern of phase shifts applied by the RIS in order to estimate the base station (BS)-RIS channels and the RIS-user’s channels. Estimation methods that use tensor modeling including Khatri-Rao Factorization (KRF) and bilinear alternating least squares (BALS) are applied to the signal model. Numerical results show that both KRF and BALS are superior to the LS estimator by 10 dB SNR for the correlated Rayleigh fading channel model.