Time-varying Multipath Channels Research Articles

Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems.

For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.

Pipelined Neural Network Assisted Mobility Speed Estimation Over Doubly-Selective Fading Channels

The speed estimation has been widely used for tracking mobile device locations, providing essential information in location/mobility-aware communications, enhancing received signal quality/robustness, and reducing energy consumption and latency. Deep learning can be used to improve the performance constrained by signal/system model. This work focuses on the issues on machine learning (ML) based speed estimation using primary synchronous signal (PSS), which is embedded in the 5G standards, over general time-variant multipath channels. Aiming to reduce the complexity involved in the ML algorithms for the speed estimation in mobile networks, we propose a pipelined ML algorithm to decompose the original ML model into several smaller ones. The advantages of the proposed convolutional neural network (CNN) based approach have been verified by simulations.

Semantic communication for cooperative perception based on importance map

Cooperative perception, which has a broader perception field than single-vehicle perception, has played an increasingly important role in autonomous driving to conduct 3D object detection. Through vehicle-to-vehicle (V2V) communication technology, various connected automated vehicles (CAVs) can share their sensory information (LiDAR point clouds) for cooperative perception. We employ an importance map to extract significant semantic information and propose a novel cooperative perception semantic communication scheme with intermediate fusion. Meanwhile, our proposed architecture can be extended to the challenging time-varying multipath fading channel. To alleviate the distortion caused by the time-varying multipath fading, we adopt explicit orthogonal frequency-division multiplexing (OFDM) blocks combined with channel estimation and channel equalization. Simulation results demonstrate that our proposed model outperforms the traditional separate source-channel coding over various channel models. Moreover, a robustness study indicates that only part of semantic information is key to cooperative perception. Although our proposed model has only been trained over one specific channel, it has the ability to learn robust coded representations of semantic information that remain resilient to various channel models, demonstrating its generality and robustness.

Anti-Multipath Underwater Acoustic Time-Delay Detection Algorithm Based on Dual-Delta Correlators

For the underwater acoustic (UAC) positioning system based on continuous waveform signal, the time-delay estimation of the Line-of-sight (Los) is not robust under the UAC strongly time-varying multipath channel. So an anti-multipath time-delay detection algorithm based on dual-delta (DD) correlators is proposed in this paper to address the issue. The algorithm uses a differential processing code-phase discriminator model to improve the traditional delay-locked-loop (DLL) time-delay detection algorithm, and combines it with the signal tracking loop adapted to the UAC channel condition to estimate time-delay. Theoretical analysis indicates that due to the reduction of local code interval and the differential processing operation, the proposed algorithm effectively mitigates the effect of time-varying multipath signals on time-delay estimation compared with conventional methods. Simulation analysis further demonstrates that the proposed algorithm can robustly detect the Los and achieve high-precision time-delay estimation under the time-varying multipath channel. The algorithm shows great potential for practical applications.

Federated Learning With Over-the-Air Aggregation Over Time-Varying Channels

We study federated learning (FL) with over-the-air aggregation over time-varying wireless channels. Independent workers compute local gradients based on their local datasets and send them to a parameter server (PS) through a time-varying multipath fading multiple access channel via orthogonal frequency-division multiplexing (OFDM). We assume that the workers do not have channel state information, hence the PS employs multiple antennas to alleviate the fading effects. Wireless channel variations result in inter-carrier interference, which has a detrimental effect on the performance of OFDM systems, especially when the channel is rapidly varying. We examine the effects of the channel time variations on the convergence of the FL with over-the-air aggregation, and show that the resulting undesired interference terms have only limited destructive effects, which do not prevent the convergence of the learning algorithm. We also validate our results via extensive simulations, which corroborate the theoretical expectations.

A Double-Threshold Channel Estimation Method Based on Adaptive Frame Statistics

Channel estimation is an important module to enhance the performance of orthogonal frequency division multiplexing (OFDM) systems. However, the presence of a large amount of noise in time-varying multipath fading channels significantly affects the channel estimation accuracy and thus the recovery quality of the received signals. Therefore, this paper proposes a double-threshold (DT) channel estimation method based on adaptive frame statistics (AFS). The method first adaptively determines the number of statistical frames based on the temporal correlation of the received signals, and preliminarily detects the channel structure by analyzing the distribution characteristics of multipath sampling points and noise sampling points during adjacent frames. Subsequently, a multi-frame averaging technique is used to expand the distinction between multipath and noise sampling points. Finally, the DT is designed to better recover the channel based on the preliminary detection results. Simulation results show that the proposed adaptive frame statistics-double-threshold (AFS-DT) channel estimation method is effective and has better performance compared with many existing channel estimation methods.

A deep-learning based high-gain method for underwater acoustic signal detection in intensity fluctuation environments

To achieve efficient underwater acoustic (UWA) signal detection in low signal-to-noise ratio (SNR) scenarios, the transmitted signals are designed with a large time-bandwidth product to get a high detection gain. The linear correlator (LC) is considered as the maximum SNR detector, whose detection gain is proportional to the time-bandwidth product. However, the detection performance of LC degrades significantly in time-variant multipath UWA channel and non-Gaussian UWA ambient noise. In this study, we present a deep-learning based two-stage UWA signal detection method in intensity fluctuation environments. This method takes the advantages of the Conv-TasNet and Encoder-Decoder network, which utilizes an encoder module to extract signal features, a separation module to enhance the signal components and then another decoder module to reconstruct the transmitted signal. To demonstrate the performance of the proposed method, the datasets used for training and testing originated from the ASIAEX 2001 South China Sea (SCS) experiment. The experimental results show that our model outperforms the classical LC and channel estimation based LC (CE-LC) in constant false alarm rate (CFAR) detection and also surpasses the TCDAE and Conv-Tasnet models as evaluated by DSI-SNR1 and DSI-SNR2.

Underwater Acoustic Channel Estimation via an Orthogonal Matching Pursuit Algorithm Based on the Modified Phase-Transform-Weighted Function

In the context of torpedo guidance systems, the performance of active sonar in channel parameter estimation and target detection and recognition is significantly degraded by the multipath effect and the time-varying characteristics of the underwater acoustic (UWA) channel. Therefore, it is urgent to propose an algorithm that can accurately estimate the channel parameters in multipath time-varying UWA channels. To solve these problems, this study developed a modified phase transform (PHAT)-weighted function and applied it to the orthogonal matching pursuit (OMP) algorithm, named M-PHAT-OMP. The proposed algorithm is more robust, improves the resolution of the time delay and further improves the estimation accuracy of the parameters in the case of motion. Furthermore, with the aim of solving the problem of the difficulty that the traditional OMP algorithm has in determining sparsity, this study proposes a joint-threshold method, where the threshold value serves as the condition for terminating the algorithm’s iteration. The simulation results demonstrate that the M-PHAT-OMP algorithm proposed in this study exhibits a superior performance compared to other algorithms, as evidenced by its lower root mean square error (RMSE) for delay. Moreover, the experimental results also validate that the proposed algorithm has superior robustness and resolution of the time delay in practical applications.

Performance of STBC Based MIMO-OFDM Using Pilot-aided Channel Estimation

Many studies have been published to address the growing issues in wireless communication systems. Space-Time Block Coding (STBC) is an effective and practical MIMO-OFDM application that can address such issues. It is a powerful tool for increasing wireless performance by coding data symbols and transmitting diversity using several antennas. The most significant challenge is to recover the transmitted signal through a time-varying multipath fading channel and obtain a precise channel estimation to recover the transmitted information symbols. This work considers different pilot patterns for channel estimation and equalization in MIMO-OFDM systems. The pilot patterns fall under two general types: comb and block types, with a proper arrangement suitable to the multiple transmit antennas. The two main channel estimation methods, LS and MMSE, are compared by evaluating performance in terms of Bit Error Rate (BER) to analyze the performance of pilot-aided channel estimation for 2x2 and 4x4 MIMO arrangements utilizing LTE parameters and the effects of modifying different numbers of OFDM subcarriers under different channel models It has been discussed, a 4x4 system performs better than a 2x2 system in terms of BER with an acceptable amount of additional complexity.

Channel modelling and characterization for mud pulse telemetry

Mud pulse telemetry (MPT) is a low-cost borehole wireless communication scheme in the oil drilling industry. Drilling mud is used as the medium to deliver logging information via mud pressure pulses. However, the data transmission rate is severely limited by poor channel conditions. To overcome the existing limitations and to achieve the full potential of the MPT, a transmission model, which considers the effect of the boundary layer thickness on viscous friction and the effect of pipe materials on characteristic impedance, has been proposed. Then, the accuracy of the model is verified through experiments. Finally, the channel characteristics are analysed and discussed based on the model. The results demonstrate that the MPT channel is a time-varying multipath channel with significant symbol timing offsets, carrier frequency offsets, frequency selective fading, and inter-symbol interference issues. Based on the model simulation results, countermeasures are proposed.

Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems

Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep Learning (DL) based OFDM channel estimators are only applicable to Signal-to-Noise Ratios (SNRs) in a small range, and the estimation performance of the existing algorithms is greatly limited when the channel model or the mobile speed at the receiver does not match. To solve this problem, this paper proposes a novel network model NDR-Net that can be used for channel estimation under unknown noise levels. NDR-Net consists of a Noise Level Estimate subnet (NLE), a Denoising Convolutional Neural Network subnet (DnCNN), and a Residual Learning cascade. Firstly, a rough channel estimation matrix value is obtained using the conventional channel estimation algorithm. Then it is modeled as an image and input to the NLE subnet for noise level estimation to obtain the noise interval. Then it is input to the DnCNN subnet together with the initial noisy channel image for noise reduction to obtain the pure noisy image. Finally, the residual learning is added to obtain the noiseless channel image. The simulation results show that NDR-Net can obtain better estimation results than traditional channel estimation, and it can be well adapted when the SNR, channel model, and movement speed do not match, which indicates its superior engineering practicability.

ISAC system assisted by RIS with sparse active elements

In this paper, we consider an integrated sensing and communications system assisted by a reconfigurable intelligent surface (RIS). A small number of sparsely placed active sensors are applied in the RIS to perform effective channels and, thereby, enable optimized beamforming for both communications and sensing objectives, namely, establishing reliable communication links with communication users (CUs) and effectively localize targets. The time-varying multipath channels between the RIS and the CU as well as the time-varying channel between the RIS and the targets are estimated by exploiting an interpolated Hermitian and Toeplitz covariance matrix followed by direction-of-arrival estimation using the MUSIC algorithm. Based on such results, we jointly optimize the transmit beamformer at the base station and the unit-modulus RIS passive beamformer. The RIS beamformer is optimized to maximize its minimum beam-pattern gain towards the desired sensing angles subject to the minimum signal-to-noise ratio requirement at the CU. Simulation results verify the effectiveness of the proposed approach, and the performance of different sparse array configurations is compared.

Doppler Frequency Estimation Based on Time Diversity of Random Processes in Doubly-Selective Channels

This work introduces a new method to estimate Doppler frequency which carries important characteristics feature of time-variant multipath fading channels. The proposed method employs the time diversity (TD) of general random processes with rich correlation characteristics introduced by various time intervals, where a maximum-likelihood (ML) algorithm is utilized. A closed-form weighted least-square equation is derived with the help of basis expansion approximation. In addition, the Cram <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\acute{\text{e}}$</tex-math></inline-formula> r-Rao lower bound (CRLB) of the estimation is analyzed. The results show that the proposed method performs well in terms of Doppler frequency estimation accuracy.

Multipath Canceled RF Fingerprinting for Wireless OFDM Devices Based on Hammerstein System Parameter Separation

Radio frequency fingerprint (RFF) based authentication of wireless devices can be used in the fields of the access security at the physical-layer of wireless networks and radio spectrum management. However, the stability of RFF is easily damaged by the wireless multipath fading channel in mobile communications. An RFF fingerprinting method with the nonlinearity and in-phase and quadrature (IQ) imbalance of the transmitter is proposed for chunk-based wireless orthogonal frequency division multiplexing (OFDM) devices based on a Hammerstein system parameter separation technique, which cancels the adverse influence of the time-varying multipath channel. First, the parameters of the nonlinear model of the transmitter and finite impulse response (FIR) of the wireless multipath channel are estimated with the Hammerstein system parameter separation technique. Second, the best IQ imbalance parameter combination is obtained with the FIR estimation of the channel. Finally, the estimated parameters of the nonlinear model and IQ imbalance are used as RFFs to classify the transmitters. Theoretical analyses and numerical experiments demonstrate that the obtained RFFs are stable and the fusion authentication of transmitters with subtle differences from the same model and same series is feasible using the novel method.

A Method for Underwater Acoustic Key Detection Based on OFDM Pilot Sequences

The traditional physical layer key generation technology uses channel temporal variation, reciprocity, and spatial decorrelation to realize key distribution and fast update. However, due to the complex, time-varying and time-delayed features, the underwater acoustic channel environment often changes during channel detection, resulting in impaired channel reciprocity. To solve this problem, this paper proposes an orthogonal frequency division multiplexing pilot structure based on the features of underwater acoustic time-varying multipath channel. The structure can complete key detection when channel reciprocity is impaired, promotes the consistency of observation sequences, and reduces the key disagreement rate. The theoretical deduction proves the validity of the proposed method under an underwater acoustic multipath time-varying channel. Also, the simulation results verify the feasibility of the proposed method.

Iterative Interference Cancellation for Multi-Carrier Modulation in MIMO-DWT Downlink Transmission

The Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) scheme represents the dominant radio interface for broadband multicarrier communication systems. However, with insufficient Cyclic Prefixes (CP), Inter-Symbol Interference (ISI) and Inter-Carrier Interference (ICI) occur due to the time-varying multipath channel. This means that the performance of the system will be degraded. In this paper, we investigate the interference problem for a MIMO Discrete Wavelet Transform (MIMO-DWT) system under the effect of the downlink LTE channel. A Low-Density Parity-Check (LDPC) decoder is used to estimate the decoded signal. The proposed iterative algorithm uses the estimated decoded signal to compute the components required for ICI/ISI interference reduction. In this paper, Iterative Interference Cancellation (IIC) is employed to mitigate the effects of interference that contaminates the received signal due to multiple antenna transmission and a multipath channel. An equalizer with minimum mean square error is considered. We compare the performance of our proposed algorithm with the traditional MIMO-OFDM scheme in terms of bit error probability under insufficient CP. Simulation results verify that significant improvements are achieved by using IIC and MIMO-IIC for both systems.

Improved Atomic Norm Based Time-Varying Multipath Channel Estimation

In this paper, improved channel gain delay estimation strategies are investigated when practical pulse shapes with finite block length and transmission bandwidth are employed. Pilot-aided channel estimation with an augmented atomic norm based approach is proposed to promote the low rank structure of the time-varying narrowband leaked channel. All the channel parameters, i.e., delays, Doppler shifts, and channel gains are recovered. Design choices which ensure unique estimates of channel parameters for rectangular, Gaussian, and root-raised-cosine pulse shapes are examined in the noiseless case, respectively. Furthermore, a perturbation analysis is conducted to measure the impact of noise and further design choices for parameters are proposed to mitigate the effects of noise. Finally, numerical results verify the theoretical analysis and show performance improvements over the previously proposed method.

Analytical Performance Evaluation of GFDM in Underwater Acoustic Communication Systems

The rapid growth of the Internet of Things (IoT) has extended its concept to underwater environments. However, the implementation of these systems via wired communication still represents a technological challenge, mainly due to the high cost of their deployment. Therefore, wireless communications are seen as an interesting solution for the deployment of underwater communications systems. Preliminary research indicated that underwater acoustic wireless communication could be used for some Internet of Underwater Things applications, mainly due to the wide range of communications involved. However, a significant disadvantage of acoustic systems is their low transmission data rate; thus, studies and analyses to improve this disadvantage must be carried out. Considering that new waveforms have been proposed to improve the performance of terrestrial wireless communications systems, this work presents the development of general analytical expressions that allow the performance evaluation of the Generalized Frequency Division Multiplexing (GFDM) waveform in underwater environments. These analytical expressions were obtained considering a continuous-time model for the GFDM signal and modeling the underwater acoustic communication channel as a time-varying multipath channel. Numerical results were obtained for many different systems and channel parameters, allowing a quantitative evaluation of the system performance degradation.

Performance Simulation of Underwater Acoustic Spread Spectrum Communication Based on LDPC Code

As we all know, the underwater acoustic channel is a time-varying, space-changing, and frequency-varying fading channel with limited bandwidth and severe noise interference. This paper studies and analyzes the main factors restricting the transmission of underwater acoustic channels, and establishes an underwater acoustic multipath time-varying fading channel. The channel coding adopts Quasi-Cyslic Low-Density Parity-Check Codes (QC-LDPC), combined with spread spectrum technology and high-order modulation technology, and utilizes Log-likelihood Ratios Belief Propagation (LLR-BP) is decoded, the simulation results show that LDPC codes have stronger error correction capabilities in underwater acoustic spread spectrum communication systems, ensuring that the system can operate reliably under low SNR conditions.

Blind multicarrier waveform recognition based on spatial-temporal learning neural networks

Blind multicarrier waveform recognition has become a more daunting task and open problem for the current and future radio surveillance and signals interception, with the advent of new multicarrier technologies such as the state-of-the-art F-OFDM, UFMC, FBMC, OTFS, GFDM and CP-OFDM techniques. Therefore, the practical recognition scheme for multicarrier waveforms is necessary to keep up with the pace. To tackle this challenge, we propose a novel multicarrier waveform recognition framework based on Spatial Temporal-Convolutional Long Short-Term Deep Neural Network (ST-CLDNN) in the entirely blind context. The complementary information of the raw in-phase, quadrature and amplitude samples are first extracted to provide more distinguishable features. Then ST-CLDNN collects the advantages of one-dimensional convolutional and long short-term memory (LSTM) to extract high-level spatial and temporal features for the recognition task. Later, we introduce the transfer learning strategy to put the computational resource to good use and obviate the retraining from scratch for a time-varying channel. Experimental results indicate that the proposed ST-CLDNN can perform better than the traditional feature-based classifiers and existing deep learning (DL)-based neural networks, and deliver a substantial recognition performance in a time-varying multipath fading channel.