Unfolding for Joint Channel Estimation and Symbol Detection in MIMO Communication Systems

This paper proposes a Joint Channel Estimation and Symbol Detection (JED) scheme for Multiple-Input Multiple-Output (MIMO) wireless communication systems. Our proposed method for JED using Alternating Direction Method of Multipliers (JED-ADMM) and its model-based neural network version JED using Unfolded ADMM (JED-U-ADMM) markedly improve the symbol detection performance over JED using Alternating Minimization (JED-AM) for a range of MIMO antenna configurations. Both proposed algorithms exploit the non-smooth constraint, that occurs as a result of the Quadrature Amplitude Modulation (QAM) data symbols, to effectively improve the performance using the ADMM iterations. The proposed unfolded network JED-U-ADMM consists of a few trainable parameters and requires a small training set. We show the efficacy of the proposed methods for both uncorrelated and correlated MIMO channels. For certain configurations, the gain in SNR for a desired BER of $10^{-2}$ for the proposed JED-ADMM and JED-U-ADMM is upto $4$ dB and is also accompanied by a significant reduction in computational complexity of upto $75\%$, depending on the MIMO configuration, as compared to the complexity of JED-AM.Comment: 14 pages, 19 figures, submitted to IEEE Transactions on Signal Processin

Robust MIMO Detection With Imperfect CSI: A Neural Network Solution

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.Comment: 15 pages, 8 figures, 2 tables; Accepted by IEEE TCO

On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection

This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment

Offset Learning based Channel Estimation for Intelligent Reflecting Surface-Assisted Indoor Communication

The emerging intelligent reflecting surface (IRS) can significantly improve the system capacity, and it has been regarded as a promising technology for the beyond fifth-generation (B5G) communications. For IRS-assisted multiple input multiple output (MIMO) systems, accurate channel estimation is a critical challenge. This severely restricts practical applications, particularly for resource-limited indoor scenario as it contains numerous scatterers and parameters to be estimated, while the number of pilots is limited. Prior art tackles these issues and associated optimization using mathematical-based statistical approaches, but are difficult to solve as the number of scatterers increase. To estimate the indoor channels with an affordable piloting overhead, we propose an offset learning (OL)-based neural network for channel estimation. The proposed OL-based estimator can dynamically trace the channel state information (CSI) without any prior knowledge of the IRS-assisted channel structure as well as indoor statistics. In addition, inspired by the powerful learning capability of convolutional neural network (CNN), CNN-based inversion blocks are developed in the offset estimation module to build the offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor CSI with a lower complexity as compared to the benchmark schemes

ADMM-SLPNet: A Model-Driven Deep Learning Framework for Symbol-Level Precoding

Constructive interference (CI)-based symbol-level precoding (SLP) is an emerging downlink transmission technique for multi-antenna communications systems, and its low-complexity implementations are of practical importance. In this paper, we propose an interpretable model-driven deep learning framework to accelerate the processing of SLP. Specifically, the network topology is carefully designed by unrolling a parallelizable algorithm based on the proximal Jacobian alternating direction method of multipliers (PJ-ADMM), attaining parallel and distributed architecture. Moreover, the parameters of the iterative PJ-ADMM algorithm are untied to parameterize the network. By incorporating the problem-domain knowledge into the loss function, an unsupervised learning strategy is further proposed to discriminatively train the learnable parameters using unlabeled training data. Simulation results demonstrate significant efficiency improvement of the proposed ADMM-SLPNet over benchmark schemes