Abstract Multi‐user (MU) massive multiple input multiple output (mMIMO) is considered a potential technology for fifth generation (5G) and sixth‐generation (6G) wireless systems. The presence of the antenna arrays at the base station level to communicate with the users or to serve tens of single antenna users leads to excessively high system costs and power consumption. The deployment 1‐bit digital‐to‐analogue converters (DACs) in the base station can solve these problems. This paper starts by presenting an analytical study centered on the effects of 1‐bit DACs on the system envisaged for a Rayleigh‐type fading channel. Compact‐form expressions are derived for the symbol error rate. Afterwards, an efficient end‐to‐end deep learning technique to compensate for the joint effect of 1‐bit DAC and imperfect channel state information in downlink mMIMO systems. Moreover, to improve the performance of the considered system, a DAC mixed architecture is proposed, where a number of antennas use 1 bit DACs while the others do not. The simulations results showed the improvement in transmission quality of the downlink of the MU‐mMIMO system in the presence of hardware imperfections using the considered end‐to‐end compensation technique.

This paper investigates a novel high-performance autoencoder based deep learning approach for Multi-User massive MIMO uplink systems assisted by a Reconfigurable Intelligent Surface (RIS) in which the users are equipped by Power Amplifiers (PA) and aim to communicate with the base station. Indeed, the communication process is formulated in the form of a Deep Neuronal Network (DNN). To handle these scenarios, we have designed a DNN network that includes two components. the first is an encoder intended to process the nonlinear distortions of the PA. The second is a decoder consisting of two fundamental steps. 1) A classic Minimum Mean Square Error linear decoder to decode the information transmitted by the users. 2) The neural network decoder to minimize interference. The results of the numerical simulation illustrate that the proposed method offers a significant improvement in error performance in comparison with the different basic schemes.

This paper proposes an autoencoder-based deep learning approach for multiuser massive multiple-input multiple-output (mMIMO) downlink systems assisted by a reconfigurable intelligent surface (RIS) whose base station is equipped with an antenna array with 1-bit digital-to-analog converters (DACs) to serve multiple user terminals. RIS has introduced today one of the most revolutionary techniques to improve spectrum and energy efficiency for the 6G of wireless networks. First, we present an analytical study on the effects of 1bit DAC on the system under consideration for a Rician fading channel. Then, the transmission system assisted by the proposed RIS design is presented, which allows network operators to control the signal propagation environment. To further improve our system, we propose the deep learning technique to compensate for the signal degradation caused by 1-bit DACs. Numerical simulations demonstrate that the compensation technique considered with the RIS presence achieves competitive performance compared to the existing literature.

Abstract In this paper, the authors study the compensation of high‐power amplifier (HPA) non‐linear distortion in the multi‐user (MU) massive multiple‐input multiple‐output (MIMO) systems and focus on uplink transmission, where the base station (BS) uses a large antenna array. First, the authors present a non‐linear distortion iterative cancellation (NDIC) algorithm‐based MMSE and approximate message passing (AMP) at the receiver level, in order to estimate and mitigate jointly a non‐linear distortion and the channel noise. Second, the authors propose a novel distortion cancellation technique based on deep learning. At this level, the authors first introduce a multilayer neural network, trained in the Levenberg–Marquardt algorithm by eliminating HPA non‐linearities on the ‘Pre distortion’ transmitter and ‘Post distortion’ receiver side. Next, the authors developed a novel end‐to‐end (E2E) learning approach for the joint transmitter and non‐coherent receiver in the Rayleigh fading channel. The basic idea lies in the use of deep neural networks (DNNs), auto encoder (AE) for unknown channels, where DNNs are applied to perform several functions and modules existing in the transmission chain. The simulation results demonstrate the strong potential of the proposed approach E2E in terms of improving the link quality and symbol error rate (SER) compared to other compensation techniques presented in this work.

Multi-User (MU) Massive Multiple Input Multiple Output (MIMO) is considered a potential and disruptive technology for fifth-generation (5G) and sixth-generation (6G) wireless systems. The presence of antennas networks at the base station level to communicate with single antenna users leads to in excessively high system costs and power consumption. The deployment of the analog-digital converters (ADC) 1 bit in the BS can solve these problems. In this work, we study the compensation of the impact of 1 bit ADC in MU massive MIMO systems on the uplink transmission accompanied with Minimum Mean Square Error (MMSE) detector. First, we propose a new end-to-end (E2E) learning approach based on Deep neural network (DNN). The objective is to design the communication system as a set of layers for the transmitter and the receiver in unknown channels. Then we present a mixed ADC architecture, where some antennas use 1-bit ADCs while the rest does not exploit ADCs using the end-to-end approach. The simulation results demonstrate the high potential of the proposed E2E approach in terms of uplink quality improvement. It is also demonstrated that the performance of massive MIMO systems with a mixed ADC architecture approaches those of ideal systems.

As Sparse code multiple access (SCMA) has proved to be a fascinating research in order to meet the requirements of future wireless communication systems. To reach high power efficiency, wireless communication systems are equipped with high power amplifiers (HPAs). In this paper, we investigate the effects of distortions due to high power amplifiers (HPA) nonlinearities. We study the performance of amplified SCMA systems, in terms of bit error rate (BER). Message passing algorithm (MPA) is considered for SCMA detectors. BER performance is derived and evaluated for Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. Numerical results and comparisons are provided for several system parameters, such as the input back-off (IBO). Indeed, we propose a new distortion cancellation technique based on feed-forwarded neural networks (FNNs) to restore the system performance via eliminating the HPA nonlinearities at transmitter and receiver sides. It is confirmed that the proposed pre-distorter and post-distorter with neural network exhibit a good performance improvement of quality of the transmission. Specifically, post-distortion based on NNs shows a better BER performance, which is almost close to the one of the linear system.