Channel Tracking Algorithm Research Articles

Uncoordinated Channel Tracking Algorithms and Their Stability in Practical Scenario for High-Rate IoT Devices With Massive MIMO

Uncoordinated Channel Tracking Algorithms and Their Stability in Practical Scenario for High-Rate IoT Devices With Massive MIMO

Low complexity channel tracking algorithms for coordinated and uncoordinated pilot access over high-rate internet of things

Given that high-rate internet-of-things (IoT) applications are supported by ubiquitous massive MIMO services, this paper introduces novel algorithms with significantly reduced complexity to estimate and track the channels of active and inactive devices, which can resolve intra-cell pilot collisions and utilize their information. Then, we propose an optimum coordinated access (O-CA) tracker whose complexity grows squarely with the number of devices instead of cubically. In addition, the error-boundedness and steady-state characteristics of O-CA are demonstrated. Although uncoordinated pilot access eliminates control signaling associated with active IoT devices, it needs the base station (BS) to identify the devices’ pilot transmission patterns (DPTPs). A low-complexity optimal uncoordinated access (O-UA) tracker is intended to contain DPTPs that expand exponentially, which is impractical. To this end, we derive an analytical closed-form solution for a near-optimal uncoordinated access (NO-UA) tracker that minimizes an upper bound on the error between O-UA and the simplified DPTPs. Consequently, the NO-UA results in significantly lower computational costs and superior estimation performance compared to existing trackers. Moreover, we derive the optimal linear uncoordinated access (OL-UA) tracker and its stability. Specifically, two heuristic trackers are introduced to detect DPTPs and track the devices’ channels. The simulation results and complexity analysis demonstrate that the designed trackers significantly reduce complexity compared with conventional trackers.

Channel Tracking and AoA Acquisition in Quantized Millimeter Wave MIMO Systems

This paper considers the channel tracking and angle of arrival (AoA) acquisition for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, in which each antenna at the base station is equipped with low-resolution analog-to-digital converters (ADCs) to quantize the received signals. We utilize the time-varying model to capture the sparsity and temporal correlation of the mmWave channel, and an offgrid model is incorporated for an accurate AoA acquisition. Essentially, the beamspace channel tracking is a quantized sparse Bayesian learning problem, which is solved under the expectation maximization (EM) framework. We first employ the Bussgang decomposition to linearize quantized signals, based on which, the Kalman filtering (KF) is adapted for the statistics' update in the expectation step. In this way, we propose a Bussgang joint channel tracking and data detection (BJ-CTDD) algorithm, in which the detected data symbols are reused to enhance the tracking without extra pilot overhead. To further reduce the computational burden caused by the KF, a variational inference joint channel tracking and data detection (VIJ-CTDD) algorithm is proposed. Finally, extensive simulations validate the superiority of the proposed BJ-CTDD and VIJ-CTDD algorithms over several existing works.

Channel Tracking and Prediction for IRS-Aided Wireless Communications

For intelligent reflecting surface (IRS)-aided wireless communications, channel estimation is essential and usually requires excessive channel training overhead when the number of IRS reflecting elements is large. The acquisition of accurate channel state information (CSI) becomes more challenging when the channel is not quasi-static due to the mobility of the transmitter and/or receiver. In this work, we study an IRS-aided wireless communication system with a practical channel model that characterizes the time-varying propagation property and propose an innovative two-stage transmission protocol. In the first stage, we send pilot symbols and track the direct/reflected channels based on the received signal, and then data signals are transmitted. In the second stage, instead of sending pilot symbols first, we directly predict the direct/reflected channels and all the time slots are used for data transmission. Based on the proposed transmission protocol, we propose a two-stage channel tracking and prediction (2SCTP) scheme to obtain the direct and reflected channels with low channel training overhead, which is achieved by exploiting the temporal correlation of the time-varying channels. Specifically, we first consider a special case where the IRS-access point (AP) channel is assumed to be static, for which a Kalman filter (KF)-based algorithm and a long short-term memory (LSTM)-based neural network are proposed for channel tracking and prediction, respectively. Then, for the more general case where the IRS-AP, user-IRS and user-AP channels are all assumed to be time-varying, we present a generalized KF (GKF)-based channel tracking algorithm, where proper approximations are employed to handle the underlying non-Gaussian random variables. Numerical simulations are provided to verify the effectiveness of our proposed transmission protocol and channel tracking/prediction algorithms as compared to existing ones.

Deep Learning for Channel Tracking in IRS-Assisted UAV Communication Systems

To boost the performance of wireless communication networks, unmanned aerial vehicles (UAVs) aided communications have drawn dramatically attention due to their flexibility in establishing the line of sight (LoS) communications. However, with the blockage in the complex urban environment, and due to the movement of UAVs and mobile users, the directional paths can be occasionally blocked by trees and high-rise buildings. Intelligent reflection surfaces (IRSs) that can reflect signals to generate virtual LoS paths are capable of providing stable communications and serving wider coverage. This is the first paper that exploits a three-dimensional geometry dynamic channel model in IRS- assisted UAV-enabled communication system. Moreover, we develop a novel deep learning based channel tracking algorithm consisting of two modules: channel pre-estimation and channel tracking. A deep neural network with off-line training is designed for denoising in the pre-estimation module. Moreover, for channel tracking, a stacked bi-directional long short term memory (Stacked Bi-LSTM) is developed based on a framework that can trace back historical time sequence together with bidirectional structure over multiple stacked layers. Simulations have shown that the proposed channel tracking algorithm requires fewer epochs to convergence compared to benchmark algorithms. It also demonstrates that the proposed algorithm is superior to different benchmarks with small pilot overheads and comparable computation complexity.

Efficient Multiband Channel Reconstruction and Tracking for Hybrid mmWave MIMO Systems

Multiband operation in millimeter wave (mmWave) will obtain lots of performance gain by offering larger bandwidth. However, the prerequisite is the acquisition of accurate channel state information (CSI), which is a knotty task with hybrid analog/digital architecture. This study devotes to efficiently reconstruct and track the time drifting multiband channel to keep CSI precise in time division duplex (TDD) mmWave multiple-input-multiple-output system with hybrid analog/digital architecture. Utilizing spatial reciprocity, an efficient multiband channel reconstruction scheme is introduced, which elaborately estimates the central sub-band channel and then reconstructs side sub-band channels from the central one. To this end, a beam training-based Newtonized orthogonal matching pursuit (BT-NOMP) algorithm is proposed to estimate the central sub-band channel. With the help of frequency-independent parameters extracted from BT-NOMP, side sub-bands channel can be well reconstructed with additional low-complexity path gains estimation process. Furthermore, to avoid frequent channel reconstruction in a slightly drifting channel meanwhile keep the CSI accurate, a rotated beam-based channel tracking algorithm is developed using historical observations of channel parameters. Numerical results prove the efficiency of the proposed multiband channel reconstruction scheme and the accuracy of the channel tracking algorithm.

Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks

The emerging space-air-ground integrated network has attracted intensive research and necessitates reliable and efficient aeronautical communications. This paper investigates terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the unique triple delay-beam-Doppler squint effects of aeronautical terahertz UM-MIMO channels. Specifically, based on the rough angle estimates acquired from navigation information, an initial aeronautical link is established, where the delay-beam squint at transceiver can be significantly mitigated by employing a Grouping True-Time Delay Unit (GTTDU) module (e.g., the designed Rotman lens -based GTTDU module). According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint. Doppler shifts can be subsequently estimated using the proposed prior-aided iterative Doppler shift estimation algorithm. On this basis, path delays and channel gains can be estimated accurately, where the Doppler squint can be effectively attenuated via compensation process. For data transmission, a data-aided decision-directed based channel tracking algorithm is developed to track the beam-aligned effective channels. When the data-aided channel tracking is invalid, angles will be re-estimated at the pilot-aided channel tracking stage with an equivalent sparse digital array, where angle ambiguity can be resolved based on the previously estimated angles. The simulation results and the derived Cramer-Rao lower bounds verify the effectiveness of our solution.

A Hybrid Pilot Beamforming and Channel Tracking Scheme for Massive MIMO Systems

In massive MIMO systems, the effective channel state information (CSI) is an essential prerequisite for the beamforming (BF) design. While there is a handful of previously proposed compressive sensing (CS)-based channel estimation algorithms in the literature, the large BF gain provided by the massive MIMO array has not been fully exploited in the channel estimation stage. In order to obtain higher BF gain and better estimation performance with less pilot overhead, we study the joint design of the transmitting and receiving hybrid pilot BF as well as the associated channel tracking scheme. Specifically, a Markov prior is used to model the temporal correlation in massive MIMO channels over different time slots. Then, the hybrid pilot BF is optimized by maximizing the mutual information between the channel measurements and the corresponding downlink sparse channels with the Markov prior. Following, we derive an efficient channel tracking algorithm called Turbo Bayesian Inference (Turbo-BI) to solve the resulting CS problem and generate the channel prior information required to calculate the mutual information for the optimization of hybrid pilot BF in the next time slot. The proposed Turbo-BI can exploit both the sparsity and the temporal correlation of massive MIMO channels to enhance the estimation performance. Finally, simulations show that our proposed algorithm can achieve significant gain over the existing state-of-the-art baselines.

Data Aided MSE-Optimal Time Varying Channel Tracking in Massive MIMO Systems

The temporal variations in the wireless propagation channel, referred to as channel aging, cause a mismatch between the estimated channel and the channel state at the time of data detection. This mismatch has been shown to severely impair the performance of massive MIMO systems. In this paper, we present data aided MSE-optimal channel tracking algorithms to decode the received symbols and update the channel estimates at the base station (BS) and the UEs. These algorithms combine ideas from Kalman filtering for channel tracking and deterministic equivalent analysis for symbol estimation. In the uplink case, we first develop a minimum mean squared error (MMSE) data estimator and the MSE-optimal channel predictor based on the Kalman filtering algorithm. We analytically show that the updated channel estimate obtained from the estimates of the data symbols leads to significantly larger signal to interference-plus-noise ratio (SINR), and hence achievable rate, compared to that obtained from the channel estimate based on pilot symbols. Following this, in the downlink case, we develop an algorithm to track the effective channel at the UEs and analyze its MSE, SINR and achievable rate performance. We show that tracking the effective downlink channel mitigates the effects of channel aging and leads to improved performance. However, since the beamforming matrices at the BS are not updated, downlink channel tracking is not as effective as uplink channel tracking. Finally, via Monte Carlo simulations, we validate our derived results, and demonstrate the gains achievable by tracking the time-varying channels in massive MIMO systems.

Wideband Channel Tracking and Hybrid Precoding for mmWave MIMO Systems

A major source of difficulty when operating with large arrays at millimeter wave (mmWave) frequencies is to estimate the wideband channel, since the use of hybrid architectures acts as a compression stage for the received signal. Moreover, the channel has to be tracked and the antenna arrays regularly reconfigured to obtain appropriate beamforming gains when a mobile setting is considered. In this paper, we focus on the problem of channel tracking for frequency-selective mmWave channels, and propose two novel channel tracking algorithms. One of them exploits the sparsity of the mmWave channel, while the other one leverages prior statistical information about the channel parameters. We also propose a hybrid precoder and combiner design method to increase the received signal-to-noise ratio (SNR) during channel tracking, such that near-optimum data rates can be obtained with low-overhead. In our numerical results, we analyze the performance of our proposed algorithms for different system parameters. Simulation results show that our proposed channel tracking algorithms are able to achieve near-optimum data rates outperforming state-of-the-art methods.

V2I 통신에서 기계학습 기반 밀리미터파 채널 추정 및 추적 알고리즘

V2I 통신에서 기계학습 기반 밀리미터파 채널 추정 및 추적 알고리즘

EM‐based poor slot update method for MIMO IDD receiver in fast time‐varying channels

In this study, the authors propose an expectation-maximisation (EM)-based poor slot update method for multiple-input multiple-output (MIMO) iterative detection and decoding (IDD) receivers to increase their channel tracking capability in fast time-varying channels. The poor slots that trigger error propagation in a frame are selected, the channel state information is updated, and the updated recursive least squares channel tracking algorithm is executed. The accurate channel tracking capability of this new channel updating technique contributes to a 3 dB improvement in the bit error rate performance of an IDD receiver in a fast mobile environment ( > 180 km / h ). The authors also demonstrate that an ∼ 20 − 25 % complexity reduction can be achieved under various conditions.

Multi-channel Tracking with Different Fading Rates in an OFDM-PLNC Network

In this paper, we address the problem of tracking the time varying/frequency selective fading channels with different fading rates in a decode-and-forward physical layer network coding (PLNC) structure. Since, in a PLNC system multi users send their information to the relay, simultaneously, the relay needs to estimate the uplink multi-channels, simultaneously, to be able to extract the network coded signal from superimposed signals. Different fading rates of channels due to different velocities of mobile users in a PLNC system make the channel tracking problem more complex. Providing an algorithm to simultaneous track multi-channels in such a system is the main object of this paper. To this end, a time domain channel tracking algorithm based on sum–product rules is derived, where, both frequency and time selectivity characteristics of the channels are taken into account. In order to stabilize against the frequency selectivity property of the channels, orthogonal frequency division multiplexing is also considered in this system. High accuracy, no phase ambiguity, and stability in fast fading conditions are some advantages of the proposed approach. The performance of the algorithm in the existence of channel coding has been also considered and compared with the non-coded channel system.

Low overhead channel tracking strategy for millimetre wave multiple‐input multiple‐output system

In this, study, a new channel tracking algorithm for time-varying millimetre wave (mmWave) system is proposed. The transmitter employs hybrid beamforming architecture which is generally adopted in mmWave system and the receiver is assumed to have perfect channel knowledge. Due to the short feedback interval in mmWave system, the authors propose a low overhead channel tracking algorithm in which the receivers feedback the channel state information every several channel indices. Obviously, the proposed channel tracking algorithm can reduce feedback times and improve feedback efficiency. Based on the mutual information analysis and 3 dB criterion on signal-to-noise ratio, the upper bound of the loss on system capacity and the maximum gap of discrete channel tracking, together with the desired number of data steam are given. Simulation results are presented to verify the analysis above and demonstrate the performance in discrete channel tracking.

Investigation of Sphere Decoder and Channel Tracking Algorithms for Media-Based Modulation over Time-Selective Channels

The performance of media-based modulation (MBM) systems, where additional information can be conveyed by the indices of the channel states created by RF mirrors, over time-selective channels is investigated. By transforming the MBM system model into a traditional MIMO system model, we first propose a reduced complexity sphere decoder algorithm. Then two channel tracking algorithms, which are based on least mean square adaptive filter and recursive least-squares adaptive filter, are employed in order to combat the performance loss caused by the time-varying channels. Numerical results show that the proposed sphere decoder and these two channel tracking algorithms perform well in MBM systems.

An ICVBPNN Algorithm for Time-varying Channel Tracking and Prediction

An ICVBPNN Algorithm for Time-varying Channel Tracking and Prediction

Model-Based Signal Subspace Channel Tracking for Correlated Underwater Acoustic Communication Channels

Many underwater acoustic channels exhibit correlated (semideterministic) multipath arrivals. Such channels are often time-varying with extensive delay spread and yet have a limited number of degrees of freedom due to cross-path (cross-tap) correlation. Traditional least square algorithms used for channel estimation do not exploit this correlation structure. In this paper, a model-based channel tracking algorithm is proposed for correlated underwater acoustic communication channels. To exploit the cross-tap correlation, the channel impulse response (CIR) is projected into a lower dimensional signal subspace consisting of a set of uncorrelated channel components. Assuming constant bases, the channel variations are then represented via an autoregressive (AR) model of these uncorrelated components. This leads to a dynamic model of reduced dimension which can be effectively processed using a Kalman filter, yielding improved channel tracking performance. By tracking only a small number of degrees of freedom in the signal subspace, significant savings in computations can be achieved compared with the conventional recursive least square (RLS) algorithm. Performance is demonstrated with at-sea data.

Performance analysis of decision directed maximum likelihood MIMO channel tracking algorithm

SUMMARYIn this paper, the performance of decision directed (DD) maximum likelihood (ML) channel tracking algorithm is analyzed. The ML channel tracking algorithm presents efficient performance especially in the decision directed mode of the operation. In this paper, after introducing the method for analysis of DD algorithms, the performance of ML MIMO channel tracking algorithm in the DD mode of operation is analyzed. In this method channel tracking error is evaluated for a given decision error rate. Then, the decision error rate is approximated for a given channel tracking error. By solving these two derived equations jointly, both the decision error rate and the channel tracking error are computed. The presented analysis is compared with simulation results for different channel ranks, Doppler frequency shifts, and signal‐to‐noise ratios, and it is shown that the analysis is a good match for simulation results especially in high rank MIMO channels and high Doppler shifts. Copyright © 2012 John Wiley & Sons, Ltd.

Markov Chain Monte Carlo Detection for Frequency-Selective Channels Using List Channel Estimates

In this paper, we develop a statistical approach based on Markov chain Monte Carlo (MCMC) techniques for joint data detection and channel estimation over time-varying frequency-selective channels. The proposed detector, that we call MCMC with list channel estimates (MCMC-LCE), adopts the Gibbs sampler to find a list of mostly likely transmitted sequences and matching channel estimates/impulse responses (CIR), to compute the log-likelihood ratio (LLR) of transmitted bits. The MCMC-LCE provides a low-complexity means to approximate the optimal maximum a posterior (MAP) detection in a statistical fashion and is applicable to channels with long memory. Promising behavior of the MCMC-LCE is presented using both synthetic channels and real data collected from underwater acoustic (UWA) channels whose large delay spread and time variation have been the main motivation for the developed system. We also adopt an adaptive variable step-size least mean-square (VSLMS) algorithm for channel tracking. We find that this choice, which does not require prior knowledge on the CIR statistics, is a good fit for UWA channels. Superior performance of the MCMC-LCE over turbo minimum mean-square-error (MMSE) equalizers is demonstrated for a variety of channels examined in this work.

Underwater acoustic channel estimation based on sparse recovery algorithms

The authors consider underwater acoustic (UWA) channel estimation based on sparse recovery using the recently developed homotopy algorithm. The UWA communication system under consideration employs orthogonal frequency-division multiplexing (OFDM) and receiver preprocessing to compensate for the Doppler effect before channel estimation. The authors first extend the original homotopy algorithm which is for real-valued signals to the complex field. The authors then propose two enhancements to the sparse recovery-based UWA channel estimator by exploiting the UWA channel temporal correlations, including the use of a first-order Gauss–Markov model and the recursive least-squares algorithm for channel tracking. Moreover the authors propose a scheme to optimise the pilot placement over the OFDM subcarriers based on the discrete stochastic approximation. Simulation results show that the homotopy algorithm offers faster and more accurate UWA channel estimation performance than other sparse recovery methods, and the proposed enhancements and pilot placement optimisation offer further performance improvement.