Channel-Coded Precoding for Multi-User MISO Systems

Precoding is a critical and long-standing technique in multi-user communication systems. However, the majority of existing precoding methods do not consider channel coding in their designs. In this paper, we consider the precoding problem in multi-user multiple-input single-output (MISO) systems, incorporating channel coding into the design. By leveraging the error-correcting capability of channel codes we increase the degrees of freedom in the transmit signal design, thereby enhancing the overall system performance. We first propose a novel data-dependent precoding framework for coded MISO systems, referred to as channel-coded precoding (CCP), which maximizes the probability that information bits can be correctly recovered by the channel decoder. This proposed CCP framework allows the transmit signals to produce data symbol errors at the users' receivers, as long as the overall information BER performance can be improved. We develop the CCP framework for both one-bit and multi-bit error-correcting capacity and devise a projected gradient-based approach to solve the design problem. We also develop a robust CCP framework for the case where knowledge of perfect channel state information (CSI) is unavailable at the transmitter, taking into account the effect of both noise and channel estimation errors. Finally, we conduct numerous simulations to verify the effectiveness of the proposed CCP and its superiority compared to existing precoding methods, and we identify situations where the proposed CCP yields the most significant gains.

Symbol Level Precoding for Systems with Improper Gaussian Interference

This paper focuses on precoding design in multi-antenna systems with improper Gaussian interference (IGI), characterized by correlated real and imaginary parts. We first study block level precoding (BLP) and symbol level precoding (SLP) assuming the receivers apply a pre-whitening filter to decorrelate and normalize the IGI. We then shift to the scenario where the base station (BS) incorporates the IGI statistics in the SLP design, which allows the receivers to employ a standard detection algorithm without pre-whitenting. Finally we address the case where the channel and statistics of the IGI are unknown, and we formulate robust BLP and SLP designs that minimize the worst case performance in such settings. Interestingly, we show that for BLP, the worst-case IGI is in fact proper, while for SLP the worst case occurs when the interference signal is maximally improper, with fully correlated real and imaginary parts. Numerical results reveal the superior performance of SLP in terms of symbol error rate (SER) and energy efficiency (EE), especially for the case where there is uncertainty in the non-circularity of the jammer.

A Deep Reinforcement Learning Approach for Autonomous Reconfigurable Intelligent Surfaces

A reconfigurable intelligent surface (RIS) is a prospective wireless technology that enhances wireless channel quality. An RIS is often equipped with passive array of elements and provides cost and power-efficient solutions for coverage extension of wireless communication systems. Without any radio frequency (RF) chains or computing resources, however, the RIS requires control information to be sent to it from an external unit, e.g., a base station (BS). The control information can be delivered by wired or wireless channels, and the BS must be aware of the RIS and the RIS-related channel conditions in order to effectively configure its behavior. Recent works have introduced hybrid RIS structures possessing a few active elements that can sense and digitally process received data. Here, we propose the operation of an entirely autonomous RIS that operates without a control link between the RIS and BS. Using a few sensing elements, the autonomous RIS employs a deep Q network (DQN) based on reinforcement learning in order to enhance the sum rate of the network. Our results illustrate the potential of deploying autonomous RISs in wireless networks with essentially no network overhead.

RIS-Aided Interference Cancellation for Joint Device-to-Device and Cellular Communications

Joint device-to-device (D2D) and cellular communication is a promising technology for enhancing the spectral efficiency of future wireless networks. However, the interference management problem is challenging since the operating devices and the cellular users share the same spectrum. The emerging reconfigurable intelligent surfaces (RIS) technology is a potentially ideal solution for this interference problem since RISs can shape the wireless channel in desired ways. This paper considers an RIS-aided joint D2D and cellular communication system where the RIS is exploited to cancel interference to the D2D links and maximize the minimum signal-to-interference plus noise (SINR) of the device pairs and cellular users. First, we adopt a popular alternating optimization (AO) approach to solve the minimum SINR maximization problem. Then, we propose an interference cancellation (IC)-based approach whose complexity is much lower than that of the AO algorithm. We derive a representation for the RIS phase shift vector which cancels the interference to the D2D links. Based on this representation, the RIS phase shift optimization problem is transformed into an effective D2D channel optimization. We show that the AO approach can converge faster and can even give better performance when it is initialized by the proposed IC solution. We also show that for the case of a single D2D pair, the proposed IC approach can be implemented with limited feedback from the single receive device.

Decision-Directed Hybrid RIS Channel Estimation with Minimal Pilot Overhead

To reap the benefits of reconfigurable intelligent surfaces (RIS), channel state information (CSI) is generally required. However, CSI acquisition in RIS systems is challenging and often results in very large pilot overhead, especially in unstructured channel environments. Consequently, the RIS channel estimation problem has attracted a lot of interest and also been a subject of intense study in recent years. In this paper, we propose a decision-directed RIS channel estimation framework for general unstructured channel models. The employed RIS contains some hybrid elements that can simultaneously reflect and sense the incoming signal. We show that with the help of the hybrid RIS elements, it is possible to accurately recover the CSI with a pilot overhead proportional to the number of users. Therefore, the proposed framework substantially improves the system spectral efficiency compared to systems with passive RIS arrays since the pilot overhead in passive RIS systems is proportional to the number of RIS elements times the number of users. We also perform a detailed spectral efficiency analysis for both the pilot-directed and decision-directed frameworks. Our analysis takes into account both the channel estimation and data detection errors at both the RIS and the BS. Finally, we present numerous simulation results to verify the accuracy of the analysis as well as to show the benefits of the proposed decision-directed framework.

Joint Communications and Sensing Hybrid Beamforming Design via Deep Unfolding

Joint communications and sensing (JCAS) is envisioned as a key feature in future wireless communications networks. In massive MIMO-JCAS systems, hybrid beamforming (HBF) is typically employed to achieve satisfactory beamforming gains with reasonable hardware cost and power consumption. Due to the coupling of the analog and digital precoders in HBF and the dual objective in JCAS, JCAS-HBF design problems are very challenging and usually require highly complex algorithms. In this paper, we propose a fast HBF design for JCAS based on deep unfolding to optimize a tradeoff between the communications rate and sensing accuracy. We first derive closed-form expressions for the gradients of the communications and sensing objectives with respect to the precoders and demonstrate that the magnitudes of the gradients pertaining to the analog precoder are typically smaller than those associated with the digital precoder. Based on this observation, we propose a modified projected gradient ascent (PGA) method with significantly improved convergence. We then develop a deep unfolded PGA scheme that efficiently optimizes the communications-sensing performance tradeoff with fast convergence thanks to the well-trained hyperparameters. In doing so, we preserve the interpretability and flexibility of the optimizer while leveraging data to improve performance. Finally, our simulations demonstrate the potential of the proposed deep unfolded method, which achieves up to 33.5% higher communications sum rate and 2.5 dB lower beampattern error compared with the conventional design based on successive convex approximation and Riemannian manifold optimization. Furthermore, it attains up to a 65% reduction in run time and computational complexity with respect to the PGA procedure without unfolding.

One-Bit Massive MIMO Precoding for Frequency-Selective Fading Channels

One-bit digital-to-analog converters (DACs) are a practical and promising solution for reducing cost and power consumption in massive multiple-input multiple-output (MIMO) systems. However, the one-bit precoding problem is NP-hard and even more challenging in frequency-selective fading channels compared to the flat-fading scenario. While block-wise processing (BWP) can effectively address the inter-symbol-interference (ISI) in frequency-selective fading channels, its computational complexity and processing delay can be too high for practical implementation. An alternative solution to alleviate the processing complexity and delay issues is symbol-wise processing (SWP) which sequentially designs the transmit signals. However, existing SWP work leaves unwanted interference for later signal designs. In this paper, we propose an SWP approach which can efficiently address the ISI even at the symbol rate. The idea is to design the transmit signal to not only be beneficial for its time slot, but also to provide constructive interference for subsequent symbols. We develop two active ISI processing methods that significantly outperform a conventional approach, one of which that even outperforms the BWP approach at low SNR.

Variational Bayes Inference for Data Detection in Cell-Free Massive MIMO

Cell-free massive MIMO is a promising technology for beyond-5G networks. Through the deployment of many cooperating access points (AP), the technology can significantly enhance user coverage and spectral efficiency compared to traditional cellular systems. Since the APs are distributed over a large area, the level of favorable propagation in cell-free massive MIMO is less than the one in colocated massive MIMO. As a result, the current linear processing schemes are not close to the optimal ones when the number of AP antennas is not very large. The aim of this paper is to develop nonlinear variational Bayes (VB) methods for data detection in cell-free massive MIMO systems. Contrary to existing work in the literature, which only attained point estimates of the transmit data symbols, the proposed methods aim to obtain the posterior distribution and the Bayes estimate of the data symbols. We develop the VB methods accordingly to the levels of cooperation among the APs. Simulation results show significant performance advantages of the developed VB methods over the linear processing techniques.

Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) communications using low-resolution analog-to-digital converters (ADCs) is a promising technology for providing high spectral and energy efficiency with affordable hardware cost and power consumption. However, the use of low-resolution ADCs requires special signal processing methods for channel estimation and data detection since the resulting system is severely non-linear. This paper proposes joint channel estimation and data detection methods for massive MIMO systems with low-resolution ADCs based on the variational Bayes (VB) inference framework. We first derive matched-filter quantized VB (MF-QVB) and linear minimum mean-squared error quantized VB (LMMSE-QVB) detection methods assuming the channel state information (CSI) is available. Then we extend these methods to the joint channel estimation and data detection (JED) problem and propose two methods we refer to as MF-QVB-JED and LMMSE-QVB-JED. Unlike conventional VB-based detection methods that assume knowledge of the second-order statistics of the additive noise, we propose to float the noise variance/covariance matrix as an unknown random variable that is used to account for both the noise and the residual inter-user interference. We also present practical aspects of the QVB framework to improve its implementation stability. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also significantly outperform existing methods.

Leveraging Deep Neural Networks for Massive MIMO Data Detection

Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Low-complexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine paper is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.