Time-varying Channels Research Articles

On the tensor-based approach to adaptive joint channel estimation/data detection in flexible multicarrier MIMO systems

Multiple-input multiple-output (MIMO) systems employing multicarrier modulation (MCM), including flexible MCM that unifies several MCM schemes, have been well studied recently also in their tensor-based formulation. The latter naturally allows for (semi-)blind joint channel estimation/data detection (JCD), of particular interest in future generation massive MIMO systems, given the uniqueness properties of the tensor decomposition (TD) models. Of course, in the presence of time-varying channels, adaptive JCD needs to be implemented. However, the rich literature on recursive TD has only very little been considered in this context and only for orthogonal frequency division multiplexing (OFDM). In this paper, online TD is adapted to realize adaptive JCD for time-varying flexible MIMO-MCM systems. The common assumptions underlying online TD schemes are revisited on this occasion and shown to be inadequate to cope with fast-fading channels. This is demonstrated via simulation results on two of the most popular MCM systems. The results of this work can serve to motivate further study of online TD to extend its applicability in this and other demanding application contexts.

Impacts of Time-Varying Channel Spreading Function on the OTFS Modulation System

Impacts of Time-Varying Channel Spreading Function on the OTFS Modulation System

Manifold Optimization-Based Data Detection Algorithm for Multiple-Input–Multiple-Output Orthogonal Frequency-Division Multiplexing Systems under Time-Varying Channels

Recently, multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems have gained significant attention in the field of wireless communications. The utilization of the Riemannian manifold has become prevalent in MIMO-OFDM systems. However, the existing data detection algorithms for MIMO-OFDM systems are mostly designed for block fading channels. Additionally, these algorithms often suffer from high computational complexity. In this paper, we propose a data detection algorithm on the basis of Riemannian manifold optimization for MIMO-OFDM systems under time-varying channels. The core concept of this algorithm is to optimize the transmitted signals by solving the manifold optimization problem in the case of time-varying channels. In order to reduce the computational complexity of the algorithm, we improve the proposed algorithm by dividing the transmitted signals into multiple subframes for solving the optimization problem separately and using the pilots to maintain the performance of the algorithm. In the simulation, the performance of multiple proposed algorithms and the forced-zero detection algorithm under different parameter settings are compared. The simulation results show that the proposed algorithm demonstrates good bit error rate and computational complexity performances.

A novel variable tap-length adaptive decision feedback equalization in underwater wireless communication

Fix-parameter decision feedback equalization (DFE) is prone to the challenge posed by the time-varying underwater acoustic (UWA) channels. This paper proposes a new adaptively adjusting the DFE’s tap length (TL) algorithm to solve this problem. The number of feedforward filter (FFF) taps of DFE is changed by comparing the fourth-order cumulants and second-order moments related to receiving signals with a threshold value. The feasibility of this approach is verified via theoretical analysis and numerical simulations. Further, the new system has processed the massive experimental data from two lakes. In light of the outcomes, for achieving the same bit error ratio (BER), the signal noise ratio (SNR) that the new algorithm requires is approximately 4 dB less than the variable tap-length method based on the mean square error (MSE). Additionally, this method can improve the system’s flexibility in addressing time-varying channels in underwater wireless communication.

Energy saving computation offloading for dynamic CR-MEC systems with NOMA via decomposition based soft actor–critic

This paper investigates resource allocation in cognitive radio (CR) based mobile edge computing (MEC) systems under time-varying channels while considering non-orthogonal multiple access (NOMA) technique. In contrast to prior research on NOMA-MEC, we introduce a generalized user grouping scheme, which makes the secondary users (SUs) have the flexibility to participate in multiple NOMA groups simultaneously, enabling the partial offloading of their task data to multiple MEC servers. To tackle the non-convex long-term energy minimization problem, we propose a decomposition based soft actor–critic (DB-SAC) approach combining conventional convex optimization with deep reinforcement learning (DRL) algorithms, which divides the problem into a joint communication and computation resource allocation subproblem and an offloading ratios optimization subproblem. The former one is further divided into a series of local computation resource and offloading energy minimization problems, addressed through theoretical analysis and convex optimization techniques. For the latter subproblem, we adopt the SAC algorithm to dynamically allocate offloading ratios for SUs. Simulation results showcase that when compared to the conventional non-generalized user grouping scheme, the generalized user grouping scheme can achieve remarkable energy savings of up to 87.5%.

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation.

Widely linear RLS equalizer with variable forgetting factor and UD factorization for mud pulse telemetry

To address the problems of time variations and high interferences in continuous-wave mud pulse telemetry channels, a widely linear recursive least squares decision feedback equalizer with a variable forgetting factor was proposed, a smaller steady-state error was achieved, and the tracking performance of the equalizer in time-varying mud channels was enhanced. Furthermore, employing UD factorization in the recursive algorithm improves the computational stability and ensures robust performance. The simulation results indicate that the proposed equalizer enhances the convergence speed by 46 %. The experimental findings demonstrate that within a 3000 m mud pulse telemetry channel, the proposed equalizer reduces the steady-state mean squared error by 3 to 4 dB and increases the error-free transmission rate from 3 bps to 6 bps. These advancements significantly enhance the performance of continuous wave mud pulse telemetry systems.

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.

Joint equalization and decoding with per-survivor processing based on super-trellis in time-varying underwater acoustic channels.

This Letter proposes a low-complexity joint equalization and decoding reception scheme based on super-trellis per-survivor processing, making it possible to apply maximum likelihood sequence estimation in high-order underwater acoustic communications under fast time-varying channels. The technique combines trellis-coded modulation states and intersymbol interference states and uses per-survivor processing to track channel parameters. Furthermore, a general trellis configuration for arbitrary order quadrature amplitude modulation signal is provided when truncate the channel is used to describe the intersymbol interference state to 1. Sea trials results show that the performance of proposed method can be more than 1.4 dB superiority than conventional schemes.

Time-Varying Channel and Intrablock Carrier Frequency Offset Estimation for OFDM Underwater Acoustic Communication

Time-Varying Channel and Intrablock Carrier Frequency Offset Estimation for OFDM Underwater Acoustic Communication

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.

Doppler spread analysis for suppressing channel time variation in high‐mobility massive MIMO V2V communications

AbstractHere, fast time‐varying channels of high‐mobility vehicle‐to‐vehicle communications for massive multiple‐input multiple‐output orthogonal frequency division multiplexing systems are considered. Large‐scale uniform linear arrays are configured at the transmitter and receiver to separate multiple angle domain Doppler frequency offsets based on transmit and receive beamforming with high spatial resolution. Then, each beamforming branch comprises only one dominant Doppler frequency offset. Next, the conventional channel estimation method is performed for each beamforming branch, and carry out maximum‐ratio‐combining for data detection. Power spectrum density and Doppler spread of the equivalent link between the transmitter and receiver are derived and regarded as the criterion for assessing the residual channel time variation caused by limited antennas in practice. Interestingly, a scaling law between the asymptotic Doppler spread and the number of transceiver antennas shows that asymptotic Doppler spread is proportional to the maximum Doppler frequency offset and decreases at the rate of , where and are the number of transmit and receive antennas, respectively. Simulation results confirm the validity of the proposed Doppler suppression framework in high‐mobility vehicle‐to‐vehicle communications.

Speech emotion recognition in real static and dynamic human-robot interaction scenarios

The use of speech-based solutions is an appealing alternative to communicate in human-robot interaction (HRI). An important challenge in this area is processing distant speech which is often noisy, and affected by reverberation and time-varying acoustic channels. It is important to investigate effective speech solutions, especially in dynamic environments where the robots and the users move, changing the distance and orientation between a speaker and the microphone. This paper addresses this problem in the context of speech emotion recognition (SER), which is an important task to understand the intention of the message and the underlying mental state of the user. We propose a novel setup with a PR2 robot that moves as target speech and ambient noise are simultaneously recorded. Our study not only analyzes the detrimental effect of distance speech in this dynamic robot-user setting for speech emotion recognition but also provides solutions to attenuate its effect. We evaluate the use of two beamforming schemes to spatially filter the speech signal using either delay-and-sum (D&S) or minimum variance distortionless response (MVDR). We consider the original training speech recorded in controlled situations, and simulated conditions where the training utterances are processed to simulate the target acoustic environment. We consider the case where the robot is moving (dynamic case) and not moving (static case). For speech emotion recognition, we explore two state-of-the-art classifiers using hand-crafted features implemented with the ladder network strategy and learned features implemented with the wav2vec 2.0 feature representation. MVDR led to a signal-to-noise ratio higher than the basic D&S method. However, both approaches provided very similar average concordance correlation coefficient (CCC) improvements equal to 116 % with the HRI subsets using the ladder network trained with the original MSP-Podcast training utterances. For the wav2vec 2.0-based model, only D&S led to improvements. Surprisingly, the static and dynamic HRI testing subsets resulted in a similar average concordance correlation coefficient. Finally, simulating the acoustic environment in the training dataset provided the highest average concordance correlation coefficient scores with the HRI subsets that are just 29 % and 22 % lower than those obtained with the original training/testing utterances, with ladder network and wav2vec 2.0, respectively.

Iterative interference cancel in a channel and estimation of its parameters for the OFDM-systems

In mobile reception, the reliability of orthogonal frequency division multiplexing (OFDM) is limited due to time-varying channel characteristics. This causes inter-channel interference (ICI) and increases the errors in tracking data transmitted over the channel. In this work, ICIs are simulated based on the study of the amplitudes’ derivatives of the signals in the channel. This made it possible to design a relatively simple receiver circuit that iteratively canceles the ICI. The structure of the proposed device noise reduction is aimed at ensuring maximum signal/(noise+ICI) at the input of the detector. A new method for estimating channel parameters is also proposed and it is shown that it provides reliable mobile reception in important practical situations that occur during the organization of terrestrial digital broadcasting DVB-T2. A large number of model experiments for a receiver with one or two antennas show that a small number of successive iterations of ICI suppression and proposed channel parameter estimation provide reliable reception at vehicle speeds above 100 km/h.

Non pilot data-aided carrier and sampling frequency offsets estimation in fast time-varying channel

This paper considers the non pilot data-aided estimation of the carrier frequency offset (CFO) and sample frequency offset (SFO) of orthogonal frequency division multiplexing (OFDM) signals in fast time-varying channel. The main obstacle is the time-variant channel response, which deteriorates the estimation validity. A practical approach to mitigate this impact is to reduce the time consumption of one-shot estimation. In this way, we propose a method to reduce the time consumption to within one OFDM symbol duration. The maximum likelihood (ML) estimator is derived based on the observations of frequency domain constellations output of two FFTs on one symbol; its closed-form approximation is then derived to reduce the calculation burden. Remarkably, our method does not require any training symbol or pilot tone embedded in the signal spectrum, therefore achieves the highest spectral efficiency. Theoretical analysis and simulation results are employed to assess the performance of proposed method in comparison with existing alternatives.

A CSI Entropy-Based Estimation Algorithm and Modified Recurrent Neural Networks for Time-Varying Non-Stationary Channels

A CSI Entropy-Based Estimation Algorithm and Modified Recurrent Neural Networks for Time-Varying Non-Stationary Channels

Deep Learning-Assisted Online Task Offloading for Latency Minimization in Heterogeneous Mobile Edge

With the proliferation of smart devices in recent years, many applications requiring high computing capability and low latency have emerged. Edge computing is one of the promising paradigms to support such applications. Due to the high volatility of edge environments, e.g., frequent movements of mobile devices, varying task sizes, and time-variant channel conditions, we have to make the offloading and resource management decisions on the fly. This paper formulates and studies the problem of online task offloading and resource management in heterogeneous mobile edge environments. The goal of the problem is to minimize the overall system latency. We prove that the problem is NP-hard. Moreover, traditional algorithms needing long decision-making times are insufficient to support applications with high volatility. This paper proposes a deep learning-assisted online algorithm that can make fast decisions. In particular, we design an offline solver for the proposed problem and use a deep neural network to emulate the solver. We conduct extensive simulations to evaluate the proposed approach. Results show that the proposed approach is around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$50,000\times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$500\times$</tex-math></inline-formula> faster than the commercial Gurobi solver for the optimal solution and the proposed offline approximation solver, respectively. Moreover, the overall latency under the proposed approach is near-optimal.

SEAttention-residual based channel estimation for mmWave massive MIMO systems in IoV scenarios

To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) systems in Internet of Vehicles (IoV) scenarios, the paper proposes a deep learning (DL) algorithm, Squeeze-and-Excitation Attention Residual Network (SEARNet), which integrates Squeeze-and-Excitation Attention (SEAttention) mechanism and residual module. Specifically, SEARNet considers the channel information as an image matrix, and embeds a SEAttention module in residual module to construct the SEAttention-Residual block. Through a data-driven approach, SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism, thereby reducing noise interference and estimating the channel in an accurate and efficient manner. The simulation results show that compared to two traditional and two DL channel estimation algorithms, the proposed SEARNet can achieve a maximum reduction in normalized mean square error (NMSE) of 97.66% and 84.49% at SNR of -10 dB, 78.18% at SNR of 5 dB, and 43.51% at SNR of 10 dB, respectively.

Iterative Time-Varying Channel Prediction Based on the Vector Prony Method

Iterative Time-Varying Channel Prediction Based on the Vector Prony Method

A metasurface-based full-color circular auto-focusing Airy beam transmitter for stable high-speed underwater wireless optical communications

Due to its unique intensity distribution, self-acceleration, and beam self-healing properties, Airy beam holds great potential for optical wireless communications in challenging channels, such as underwater environments. As a vital part of 6G wireless network, the Internet of Underwater Things requires high-stability, low-latency, and high-capacity underwater wireless optical communication (UWOC). Currently, the primary challenge of UWOC lies in the prevalent time-varying and complex channel characteristics. Conventional blue Gaussian beam-based systems face difficulties in underwater randomly perturbed links. In this work, we report a full-color circular auto-focusing Airy beams metasurface transmitter for reliable, large-capacity and long-distance UWOC links. The metasurface is designed to exhibits high polarization conversion efficiency over a wide band (440-640 nm), enabling an increased data transmission rate of 91% and reliable 4 K video transmission in wavelength division multiplexing (WDM) based UWOC data link. The successful application of this metasurface in challenging UWOC links establishes a foundation for underwater interconnection scenarios in 6G communication.