Fast Time-varying Channels Research Articles

Adaptive blind equalization of fast time‐varying channel with frequency estimation in impulsive noise environment

In this paper, a novel source signal recovery method for fast time-varying channels described by complex exponential-basis expansion model (CE-BEM) in the impulsive noise environment is proposed. This method consists of two phases. The first phase is the equalization of fast time-varying channels, in this phase, a novel algorithm FSE-FLOS-CMA is proposed. The convergence performance of this newly proposed algorithm is much better than that of existing fractionally spaced equalizer-constant modulus algorithm (FSE-CMA) in impulsive noise environment. In the second phase, a novel frequency estimation method is proposed. The estimated frequency in impulsive noise environment is obtained by calculating a specific p-order fractional low-order cyclic moment of the equalized signal. Simulation results show that the proposed FSE-FLOS-CMA and frequency estimation method can effectively estimate the source signals transmitted through the fast time-varying channels in impulsive noise environment.

A Simple Cooperative Diversity Method Based on Deep-Learning-Aided Relay Selection

Opportunistic relay selection (ORS) has been recognized as a simple but efficient method for mobile nodes to achieve cooperative diversity in slow fading channels. However, the wrong selection of the best relay arising from outdated channel state information (CSI) in fast time-varying channels substantially degrades its performance. With the proliferation of high-mobility applications and the adoption of higher frequency bands in 5G and beyond systems, the problem of outdated CSI will become more serious. Therefore, the design of a novel cooperative method that is applicable to not only slow fading but also fast fading is increasingly of importance. To this end, we develop and analyze a deep-learning-aided cooperative method coined predictive relay selection (PRS) in this article. It can remarkably improve the quality of CSI through fading channel prediction while retaining the simplicity of ORS by selecting a single opportunistic relay so as to avoid the complexity of multi-relay coordination and synchronization. Information-theoretic analysis and numerical results in terms of outage probability and channel capacity reveal that PRS achieves full diversity gain in slow fading wireless environments and substantially outperforms the existing schemes in fast fading channels.

Low Complexity MIMO Channel Prediction for Fast Time-Variant Vehicular Communications Channels Based on Discrete Prolate Spheroidal Sequences

Channel state information (CSI) is required at the transmitter for achieving the maximum potentials of multiple-input multiple-output (MIMO) systems. In fast time-variant vehicular communications channels high data rate feedback lines are required in a frequency division duplex (FDD) transceiver for updating the transmitter with the rapidly changing CSI. Even with high data rate feedback lines, the delay caused by channel estimation and feedback may lead to outdated CSI at the transmitter. To reduce both the feedback load and CSI delay, this paper presents a reduced rank autoregressive (AR) channel predictor based on low dimensional discrete prolate spheroidal (DPS) sequences. The new subframe-wise DPS basis expansion model (DPS-BEM) channel predictor properly exploits the channel's restriction to low dimensional subspaces for reducing the prediction error and the computational complexity. The proposed channel predictor can be applied for updating the precoding matrix in time-variant MIMO systems. Simulation results demonstrate that the proposed channel predictor outperforms the DPS based minimum energy (ME) predictor at different Doppler frequencies and has better performance than the conventional Wiener predictor for slower time-variant channels and almost similar performance for very fast time-variant channels with reduced amount of computational complexity.

Identification of Fast Time-varying Communication Channels Using the Preestimation Technique

Abstract Accurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state-of-the-art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately chosen basis functions. The paper shows that tracking performance of the resulting local basis function estimation algorithms can be further improved by means of regularization. The method is illustrated by an important recent application - identification of fast time-varying acoustic channels used in underwater communication.

Application of m-ldpc codes in multipath Rayleigh fading channels

The fifth generation mobile communication (5G) network has been commercialized on a large scale in the world in 2020, but the wireless communication channel as an important component is a complex and changeable environment. The signal is easy to be affected by the fast time-varying channel, serious multipath interference and Doppler frequency shift, resulting in large ISI and delay. Therefore, it is necessary to process the signal before transmission. In this paper, LDPC code is used as channel coding method to study the performance of m-ldpc code in 5G wireless channel. The simulation results show that the m-ldpc code can avoid the influence of multipath channel on the signal in 5G wireless channel.

On the Throughput of Wireless Communication With Combined CSI and H-ARQ Feedback

In this paper, we characterize the combined channel state information (CSI) and re-transmission feedback on wireless communication. Feedback, often assumed free, has traditionally been used to either learn the channel state, or to request the re-transmission of a failed reception. We propose here a generalized framework for feedback that captures the channel time-variation. We consider two-way communication model where all the resources needed for training, feedback (CSI and automatic repeat request), and data. In order to maximize the throughput and optimize resource allocations, we provide the expressions of outage probabilities for different rate/power adaptation and re-transmission protocols. The simulation results show that, besides signal-to-noise ratio (SNR) and Doppler, the optimal pilot overhead depends on whether we are using rate/power adaptation and the number of re-transmissions. We also prove that in slow-fading channels with high SNR regimes, varying the rate provides the best throughput. When dealing with fast time-varying channels and low SNR regimes, using constant power and rate gives the best throughput performance.

Deep Learning Based Predictive Power Allocation for V2X Communication

As an essential technology of the fifth generation communication (5G), Vehicle-to-Everything (V2X) has attracted wide attention lately. A well-designed power allocation scheme can decrease the interference among terminals to yield a significant performance gain for V2X communication. Accurate channel state information (CSI) feedback, which is crucial to power allocation, is hard to be tracked due to fast time-varying channel in V2X scenario. This paper investigates power allocation problem under delayed CSI feedback in V2X system. We focus on maximizing sum throughput of V2X system while meeting Quality of Service (QoS) constraint requirement of each link. First of all, a power allocation scheme utilizing projective constraint analysis (PCA) is proposed to guarantee reliability of V2X network and improve system throughput, namely PCA-PA. Subsequently, we develop a Deep Neural Network (DNN) based predictive power allocation algorithm, namely DNN-PPA. This algorithm aims to address delayed CSI feedback in V2X system utilizing the normalized data obtained from PCA-PA scheme. Furthermore, a V2X communication model compliant with the 3rd Generation Partnership Project (3GPP) standard is simulated to validate the performances of PCA-PA and DNN-PPA. Simulation results illustrate that PCA-PA has superior performances compared to existing power allocation approaches and the throughput performance of DNN-PPA is impressively close to optimal solution under delayed CSI feedback.

Joint Multiple Turbo Equalization for Harsh Time-Varying Underwater Acoustic Channels

To address the problem of the harsh time-varying multi-path interference (TMI) incurred by moving underwater acoustic communications, a joint multiple turbo equalization (JMTE) algorithm is proposed. The superimposed training scheme and the single turbo equalization have been proposed to address the issue of the TMI. To tackle the more challenging fast TMI, the symbol block has been successfully divided into the segments, and the channel correlation between (among) the segments has been established, improving the tracking performance for fast time-varying channels. When the channels sharply change, the channel correlation between (among) the segments is broken, leading to decoding failure. Therefore, we develop the JMTE algorithm with the calculational complexity of the logarithmic order per symbol, where the channel of each segment is separately estimated, so that the establishment of the channel correlation is avoided, and the corresponding separate equalization results of the segments are combined to construct a joint equalization result. Unlike the Gaussian approximate process of the input symbols of the traditional linear minimum mean square error equalization, the discrete independent random variable form of the input symbols of the JMTE algorithm is retained to avoid input information loss. The ‘separate’ channel estimates, the JMTE and decoding are iteratively operated, where based on the low-complexity diagonal-matrix message passing, the information exchange between the JMTE equalizer and the decoder is carried out to dramatically enhance the equalization performance by utilizing the encoding redundant information. Moving underwater communication experiments were done in 2021 (the communication distance was about 5.5 km, and the relative speed was about 0.5 m/s), and the experimental results verify the effectiveness of the proposed algorithm.

Angular-Domain Selective Channel Tracking and Doppler Compensation for High-Mobility mmWave Massive MIMO

In this paper, we consider a mmWave massive multiple-input multiple-output (MIMO) communication system with one static base station (BS) serving a fast-moving user, both equipped with a very large array. The transmitted signal arrives at the user through multiple paths, each with a different angle-of-arrival (AoA) and hence Doppler frequency offset (DFO), thus resulting in a fast time-varying multipath fading MIMO channel. In order to mitigate the Doppler-induced channel aging for reduced pilot overhead, we propose a new angular-domain selective channel tracking and Doppler compensation scheme at the user side. Specifically, we formulate the joint estimation of partial angular–domain channel and DFO parameters as a dynamic compressive sensing (CS) problem. Then we propose a Doppler-aware-dynamic variational Bayesian inference (DD-VBI) algorithm to solve this problem efficiently. Finally, we propose a practical DFO compensation scheme which selects the dominant paths of the fast time-varying channel for DFO compensation and thereby converts it into a slow time-varying effective channel. Compared with the existing methods, the proposed scheme can enjoy the huge array gain provided by the massive MIMO and also balance the tradeoff between the CSI signaling overhead and spatial multiplexing gain. Simulation results verify the advantages of the proposed scheme over various baseline schemes.

Backscatter Aided Wireless Communications on High-Speed Rails: Capacity Analysis and Transceiver Design

Fast time-varying channel parameters and large penetration losses for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper, we introduce, for the first time, backscatter technology into HSR wireless communications, which can address these two challenges and yet have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relaying or beamforming. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and demonstrate that it outperforms the existing direct wireless transmission (DWT) scheme. We derive the upper and lower bounds of channel capacity for BAWT and prove that it exceeds that of DWT on certain conditions. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset and channel estimator, and signal detector. We show that BAWT, rather than DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally, simulation results are provided to corroborate the proposed solutions.

Sparsity-Aware Direct Equalization of Time-Varying Channels for V2V Communications

In this letter, we propose a new adaptive anti-interference equalization technique for fast time-varying channels. The equalization is implemented by basis expansion model (BEM) based time-varying (TV) finite impulse response (FIR) filters. By exploiting the sparsity of angle of arrival, we propose the idea of adaptive basis expansion for direct equalization. The optimization problem of the basis functions is formulated and solved by block orthogonal matching pursuit (BOMP) algorithm. We further propose a decision directed solution to improve the equalization performance. Simulation results show that the proposed methods exhibit a significant gain over conventional schemes.

Frequency-Domain Decision Feedback Equalization for Single-Carrier Transmissions in Fast Time-Varying Underwater Acoustic Channels

Frequency-domain equalization (FDE) is a computationally efficient method to recover underwater acoustic single-carrier transmissions in a multipath environment. The FDE receivers often operate in a block-by-block manner, assuming that the channel is time invariant during a block duration. This assumption implies that traditional block-based FDE receivers experience performance degradation in fast time-varying channel conditions, since the interfrequency interference (IFI) is often not considered. Here, we focus on the IFI cancelation (IFIC) in single-carrier transmissions. The IFI formulations are developed for a blockwise FDE receiver, where the time reversal (TR) processing is incorporated to alleviate the interblock interference. Based on the formulations, the IFI is estimated by the time-varying impulse responses, which are obtained from an adaptive basis expansion model. A soft two-step IFIC strategy is then employed to address the IFI. In the first step, the main IFI is removed by the time-varying TR processing and explicit IFI cancelation. The residual IFI is suppressed by the frequency-domain decision feedback equalization (FD-DFE) in the second step. To further enhance the communication performance, we develop a soft-symbol-based iterative receiver, referred to as IFIC-FD-DFE. The impulse responses, explicit IFI, and residual IFI are updated in an iterative fashion, based on the soft symbol estimates. The proposed receiver has been tested using the at-sea measurements collected at the Gulf of Mexico in August 2017. Communication packets at four ranges at the carrier frequency of 160 kHz with a symbol rate of 32 kHz have been decoded. The effectiveness of the IFIC-FD-DFE receiver has been demonstrated via comparisons with several FDE schemes in the current literature.

Dynamic Computation Offloading in IoT Fog Systems With Imperfect Channel-State Information: A POMDP Approach

Driven by the growing popularity of mobile applications, such as the Internet of Things (IoT), fog computing has been envisioned as a promising approach to enhance the computation capability of mobile devices and reduce the energy consumption. In this article, we aim to investigate the dynamic computation offloading problem in the IoT fog system under the fast time-varying wireless channel conditions. Our work differs from the existing work, which is based on the assumption that the channel-state information can be perfectly obtained by the offloading agent (e.g., the IoT device). In reality, due to hardware limitation, short sensing time, and network connectivity issues in IoT fog systems, it is difficult for the IoT device to have the perfect knowledge of a dynamic channel environment. Therefore, in this article, we propose a partially observable offloading scheme to enable the IoT device to make the optimal offloading decision with imperfect channel-state information. The optimization problem is formulated as a partially observable Markov decision process (POMDP) formulation, with the objective of minimizing the IoT device's energy consumption while meeting its requirement on task processing delay. To find the optimal offloading solution, an offline algorithm based on the deep recurrent $Q$ -network (DRQN) is developed. Finally, extensive simulation experiments are performed to evaluate the effectiveness of the proposed offloading scheme.

Channel prediction based temporal multiple sparse bayesian learning for channel estimation in fast time-varying underwater acoustic OFDM communications

In recent years, the multi-blocks joint channel estimation methods for underwater acoustic (UWA) OFDM systems have been proposed to improve the performance by using time correlation across consecutive OFDM blocks. However, the assumption of common sparse support (multi-path delays are invariant across consecutive blocks) in these methods is difficult to be met in fast time-varying channels. Therefore, we propose a channel prediction based joint channel estimation method for UWA fast time-varying channels, where multi-path delays and gains change significantly across consecutive OFDM blocks. Firstly we define a channel offset parameters model by the clustering property of UWA channels, and adopt Orthogonal Matching Pursuit (OMP) algorithm to estimate the channel offset parameters. Then we reconstruct a virtual current received signal based on the prediction method. Finally we combine the virtual received signal and the actual one into a joint estimation model, and utilize temporal multiple sparse Bayesian learning (TMSBL) method to jointly estimate the channel. Results of simulations and sea trial demonstrate the effectiveness of the proposed method in fast time-varying UWA channels, which achieves better performance than both the TMSBL method which is based on the joint processing of multi-blocks, and the OMP method which is based on block-by-block processing.

Learning-Based Multi-Channel Access in 5G and Beyond Networks With Fast Time-Varying Channels

We propose a learning-based scheme to investigate the dynamic multi-channel access (DMCA) problem in the fifth generation (5G) and beyond networks with fast time-varying channels wherein the channel parameters are unknown. The proposed learning-based scheme can maintain near-optimal performance for a long time, even in the sharp changing channels. This scheme greatly reduces processing delay, and effectively alleviates the error due to decision lag, which is cased by the non-immediacy of the information acquisition and processing. We first propose a psychology-based personalized quality of service model after introducing the network model with unknown channel parameters and the streaming model. Then, two access criteria are presented for the living streaming model and the buffered streaming model. Their corresponding optimization problems are also formulated. The optimization problems are solved by learning-based DMCA scheme, which combines the recurrent neural network with deep reinforcement learning. In the learning-based DMCA scheme, the agent mainly invokes the proposed prediction-based deep deterministic policy gradient algorithm as the learning algorithm. As a novel technical paradigm, our scheme has strong universality, since it can be easily extended to solve other problems in wireless communications. The real channel data-based simulation results validate that the performance of the learning-based scheme approaches that derived from the exhaustive search when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision at every few time-slots.

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.

Deep Learning Based Channel Estimation Algorithm for Fast Time-Varying MIMO-OFDM Systems

Channel estimation is very challenging for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in high mobility environments with non-stationarity channel characteristics. In order to handle this problem, we propose a deep learning (DL)-based MIMO-OFDM channel estimation algorithm. By performing offline training to the learning network, the channel state information (CSI) generated by the training samples can be effectively utilized to adapt the characteristics of fast time-varying channels in the high mobility scenarios. The simulation results show that the proposed DL-based algorithm is more robust for the scenarios of high mobility in MIMO-OFDM systems, compared to the conventional algorithms.

Decision Directed Channel Estimation Based on Deep Neural Network $k$ -Step Predictor for MIMO Communications in 5G

We consider the use of deep neural network (DNN) to develop a decision-directed (DD)-channel estimation (CE) algorithm for multiple-input multiple-output (MIMO)-space-time block coded systems in highly dynamic vehicular environments. We propose the use of DNN for k -step channel prediction for space-time block code (STBC), and show that deep learning (DL)-based DD-CE can remove the need for Doppler rate estimation in fast time-varying quasi stationary channels, where the Doppler rate varies from one packet to another. Doppler rate estimation in this kind of vehicular channels is remarkably challenging and requires a large number of pilots and preambles, leading to lower power and spectral efficiency. We train two DNNs which learn the real and imaginary parts of the MIMO fading channels over a wide range of Doppler rates. We demonstrate that by these DNNs, DD-CE can be realized with only priori knowledge about Doppler rate range and not the exact value. For the proposed DD-CE algorithm, we also analytically derive the maximum likelihood (ML) decoding algorithm for STBC transmission. The proposed DL-based DD-CE is a promising solution for reliable communication over vehicular MIMO fading channels without accurate mathematical models. This is because DNNs can intelligently learn the statistics of the fading channels. Our simulation results show that the proposed DL-based DD-CE algorithm exhibits lower error propagation compared to existing DD-CE algorithms which require perfect knowledge of the Doppler rate.

Beamforming Network Optimization for Reducing Channel Time Variation in High-Mobility Massive MIMO

Communications in high-mobility environments have caught a lot of attentions recently. In this paper, fast time-varying channels for massive multiple-input multiple-output (MIMO) systems are addressed. We derive the exact channel power spectrum density (PSD) for the uplink from a high-speed railway (HSR) to a base station (BS) and propose to further reduce the channel time variation via beamforming network optimization. A large-scale uniform linear array (ULA) is equipped at the HSR to separate multiple Doppler shifts in angle domain through high-resolution transmit beamforming. Each branch comprises a dominant Doppler shift, which can be compensated to suppress the channel time variation, and we derive the channel PSD and the Doppler spread to assess the residual channel time variation. Interestingly, the channel PSD can be exactly expressed as the product of a pattern function and a beam-distortion function. The former reflects the impact of array aperture and is the converted radiation pattern of ULA, while the latter depends on the configuration of beamforming directions. Inspired by the PSD analysis, we introduce a common configurable amplitudes and phases (CCAP) parameter to optimize the beamforming network, by partly removing the constant modulus quantized phase constraints of matched filter (MF) beamformers. In this way, the residual Doppler shifts can be ulteriorly suppressed, further reducing the residual channel time variation. The optimal CCAP parameter minimizing the Doppler spread is derived in a closed form. Numerical results are provided to corroborate both the channel PSD analysis and the superiority of beamforming network optimization technique.

On Channel Sounding With Switched Arrays in Fast Time-Varying Channels

Time-division multiplexed (TDM) channel sounders, in which a single RF chain is connected sequentially via an electronic switch to different elements of an array, are widely used for the measurement of double-directional/MIMO propagation channels. This paper investigates the impact of array switching patterns on the accuracy of parameter estimation of multipath components (MPC) for a time-division multiplexed (TDM) channel sounder. The commonly-used sequential (uniform) switching pattern poses a fundamental limit on the number of antennas that a TDM channel sounder can employ in fast time-varying channels. We thus aim to design improved patterns that relax these constraints. To characterize the performance, we introduce a novel spatio-temporal ambiguity function, which can handle the non-idealities of real-word arrays. We formulate the sequence design problem as an optimization problem and propose an algorithm based on simulated annealing to obtain the optimal sequence. As a result we can extend the estimation range of Doppler shifts by eliminating ambiguities in parameter estimation. We show through Monte Carlo simulations that the root mean square errors of both direction of departure and Doppler are reduced significantly with the new switching sequence. Results are also verified with actual vehicle-to-vehicle (V2V) channel measurements.