Hybrid Evolutionary-based Sparse Channel Estimation for IRS-assisted mmWave MIMO Systems

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods

Channel Estimation for Massive MIMO Systems

Massive multiple input multiple output (MIMO) systems can significantly improve the channel capacity by deploying multiple antennas at the transmitter and receiver. Massive MIMO is considered as one of key technologies of the next generation of wireless communication systems. However, with the increase of the number of antennas at the base station, a large number of unknown channel parameters need to be dealt with, which makes the channel estimation a challenging problem. Hence, the research on the channel estimation for massive MIMO is of great importance to the development of the next generation of communication systems. The wireless multipath channel exhibits sparse characteristics, but the traditional channel estimation techniques do not make use of the sparsity. The channel estimation based on compressive sensing (CS) can make full use of the channel sparsity, while use fewer pilot symbols. In this work, CS channel estimation methods are proposed for massive MIMO systems in complex environments operating in multipath channels with static and time-varying parameters. Firstly, a CS channel estimation algorithm for massive MIMO systems with Orthogonal Frequency Division Multiplexing (OFDM) is proposed. By exploiting the spatially common sparsity in the virtual angular domain of the massive MIMO channels, a dichotomous-coordinate-decent-joint-sparse-recovery (DCD-JSR) algorithm is proposed. More specifically, by considering the channel is static over several OFDM symbols and exhibits common sparsity in the virtual angular domain, the DCD-JSR algorithm can jointly estimate multiple sparse channels with low computational complexity. The simulation results have shown that, compared to existing channel estimation algorithms such as the distributed-sparsity-adaptive-matching-pursuit (DSAMP) algorithm, the proposed DCD-JSR algorithm has significantly lower computational complexity and better performance. Secondly, these results have been extended to the case of multipath channels with time-varying parameters. This has been achieved by employing the basis expansion model to approximate the time variation of the channel, thus the modified DCD-JSR algorithm can estimate the channel in a massive MIMO OFDM system operating over frequency selective and highly mobile wireless channels. Simulation results have shown that, compared to the DCD-JSR algorithm designed for time-invariant channels, the modified DCD-JSR algorithm provides significantly better estimation performance in fast time-varying channels

Denoising enabled channel estimation for underwater acoustic communications: A sparsity-aware model-driven learning approach

It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions, including frequency-selective property, high relative mobility, long propagation latency, and intensive ambient noise, etc. To this end, a deep unfolding neural network based approach is proposed, in which multiple layers of the network mimic the iterations of the classical iterative sparse approximation algorithm to extract the inherent sparse features of the channel by exploiting deep learning, and a scheme based on the Sparsity-Aware DNN (SA-DNN) for UAC estimation is proposed to improve the estimation accuracy. Moreover, we propose a Denoising Sparsity-Aware DNN (DeSA-DNN) based enhanced method that integrates a denoising CNN module in the sparsity-aware deep network, so that the degradation brought by intensive ambient noise could be eliminated and the estimation accuracy can be further improved. Simulation results demonstrate that the performance of the proposed schemes is superior to the state-of-the-art compressed sensing based and iterative sparse recovery schems in the aspects of channel recovery precision, pilot overhead, and robustness, particularly under unideal circumstances of intensive ambient noise or inadequate measurement pilots

A New Adaptive OMP-MAP Algorithm-based Iterative Sparse Channel Estimation for OFDM Underwater Communication

A sparse channel estimation approach based on doubly spread underwater acoustic (UWA) channels is widely used todetect coherent acoustic orthogonal frequency division multiplexing (OFDM) signals. A new time-domain channelestimation (CE) technique for OFDM based UWA communication with Rician fading is used to exploit the channel sparsity.First, to improve the estimation accuracy in high noise conditions, we have exploited the channel sparsity to generate aclosed-form equation for the termination condition. Then, in low-level noise instances, the additional criterion to balanceestimation accuracy and computing costs has been established. By incorporating these two requirements within theorthogonal-matching-pursuit (OMP) structure, an adaptive-OMP (AOMP) algorithm has been proposed. The AOMP andmaximum a posteriori probability (MAP) techniques are combined to provide a computationally efficient, and a newAOMP-MAP scheme for estimating the sparse complex channel path gain has been proposed. Further, The minimumvariance unbiased estimator is used to improve the proposed CE technique. Exploiting the experimental channel data,computer simulations reveal that the proposed CE technique obtains the outstanding outcomes

Compressive Sensing for Multi-channel and Large-scale MIMO Networks

Compressive sensing (CS) is a revolutionary theory that has important applications in many engineering areas. Using CS, sparse or compressible signals can be recovered from incoherent measurements with far fewer samples than the conventional Nyquist rate. In wireless communication problems where the sparsity structure of the signals and the channels can be explored and utilized, CS helps to significantly reduce the number of transmissions required to have an efficient and reliable data communication. The objective of this thesis is to study new methods of CS, both from theoretical and application perspectives, in various complex, multi-channel and large-scale wireless networks. Specifically, we explore new sparse signal and channel structures, and develop low-complexity CS-based algorithms to transmit and recover data over these networks more efficiently. Starting from the theory of sparse vector approximation based on CS, a compressive multiple-channel estimation (CMCE) method is developed to estimate multiple sparse channels simultaneously. CMCE provides a reduction in the required overhead for the estimation of multiple channels, and can be applied to estimate the composite channels of two-way relay channels (TWRCs) with sparse intersymbol interference (ISI). To improve end-to-end error performance of the networks, various iterative estimation and decoding schemes based on CS for ISI-TWRC are proposed, for both modes of cooperative relaying: Amplify-and-Forward (AF) and Decode-and-Forward (DF). Theoretical results including the Restricted Isometry Property (RIP) and low-coherent condition of the discrete pilot signaling matrix, the performance guarantees, and the convergence of the schemes are presented in this thesis. Numerical results suggest that the error performances of the system is significantly improved by the proposed CS-based methods, thanks to the awareness of the sparsity feature of the channels. Low-rank matrix approximation, an extension of CS-based sparse vector recovery theory, is then studied in this research to address the channel estimation problem of large-scale (or massive) multiuser (MU) multiple-input multiple-output (MIMO) systems. A low-rank channel matrix estimation method based on nuclear-norm regularization is formulated and solved via a dual quadratic semi-definite programming (SDP) problem. An explicit choice of the regularization parameter and useful upper bounds of the error are presented to show the efficacy of the CS method in this case. After that, both the uplink channel estimation and a downlink data recoding of massive MIMO in the interference-limited multicell scenarios are considered, where a CS-based rank-q channel approximation and multicell precoding method are proposed. The results in this work suggest that the proposed method can mitigate the effects of the pilot contamination and intercell interference, hence improves the achievable rates of the users in multicell massive MIMO systems. Finally, various low-complexity greedy techniques are then presented to confirm the efficacy and feasibility of the proposed approaches in practical applications