Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next generation communication systems. Due to a large number of antennas and radio frequency (RF) chains, complexity of the symbol detectors increased rapidly in a massive MIMO uplink receiver. Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. A plethora of massive MIMO detection algorithms has been proposed in the literature. The aim of this paper is to provide insights on such algorithms to a generalist of wireless communications. We garner the massive MIMO detection algorithms and classify them so that a reader can find a distinction between different algorithms from a wider range of solutions. We present optimal and near-optimal detection principles specifically designed for the massive MIMO system such as detectors based on a local search, belief propagation and box detection. In addition, we cover detectors based on approximate inversion, which has gained popularity among the VLSI signal processing community due to their deterministic dataflow and low complexity. We also briefly explore several nonlinear small-scale MIMO (2-4 antenna receivers) detectors and their applicability in the massive MIMO context. In addition, we present recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms. In each section, we also mention the related implementations of the detectors. A discussion of the pros and cons of each detector is provided.

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Massive MIMO Detection Techniques: A Survey

The demanding objectives for the future sixth generation (6G) of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity, as well as revolutionary communication and computing paradigms. Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities. Incorporating RISs in wireless networks has been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one, intended for various networking objectives, such as coverage extension and capacity boosting, spatiotemporal focusing with benefits in energy efficiency and secrecy, and low electromagnetic field exposure. Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments, in this paper, we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation. We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems, which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks. Finally, we discuss the relevance of the RIS technology in the latest wireless communication standards, and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

The demanding objectives for the future sixth generation (6G) of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity, as well as revolutionary communication and computing paradigms. Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities. Incorporating RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one, intended for various networking objectives, such as coverage extension and capacity boosting, spatiotemporal focusing with benefits in energy efficiency and secrecy, and low electromagnetic field exposure. Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments, in this paper, we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation. We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems, which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks. Finally, we discuss the relevance of the RIS technology in the latest wireless communication standards, and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches. 

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Reconfigurable intelligent surfaces for wireless communications: Overview of hardware designs, channel models, and estimation techniques

Generalized space modulation techniques: Hardware design and considerations

While fifth-generation (5G) communications are being rolled out around the world, sixth-generation (6G) communications have attracted much attention from both the industry and academia. Compared with 5G, 6G will have a wider frequency band, higher transmission rate, spectrum efficiency, greater connection capacity, shorter delay, wider coverage and stronger anti-interference capability, so as to meet the various network requirements for industries. In this paper, we present a survey of potential essential technologies in 6G. In particular, we will introduce artificial intelligence, intelligent surfaces, terahertz communications technologies in detail, while giving a brief introduction to other potential technologies, including visible light communications, Blockchain-enabled wireless network, advanced duplex and holographic radio.

A Survey of 6G Wireless Communications: Emerging Technologies

This letter presents the computational complexity reduction of the maximum likelihood-quadrature spatial modulation (QSM-ML) decoder as compared with the conventional SM-ML. Furthermore, a novel reduced-complexity (RC) sphere decoder algorithm, especially designed for QSM decoders, is proposed. It is shown that the QSM-RC algorithm achieves the optimum QSM-ML bit error ratio performance. Using Monte Carlo simulations and mathematical analysis, at the same spectral efficiency and with notable superior performance, it is shown that the QSM-ML and QSM-RC decoders provide at least 50% and up to 96% reduction in the number of visited nodes, respectively, compared to the SM-ML decoder.

Quadrature Spatial Modulation Decoding Complexity: Study and Reduction

Reconfigurable intelligent surface-empowered communication (RIS) and sparse code multiple access (SCMA) are promising candidates for future generations of wireless networks The former enhances the transmission environments, whereas the latter provides a high spectral efficiency transmission This letter proposes, for the first time, a low-cost design for RIS-assisted uplink SCMA (SCMA-RIS) scheme to improve the conventional SCMA spectrum efficiency The message passing algorithm (MPA) is utilized and modified to decode the SCMA-RIS transmitted signals Moreover, a low-complexity decoder for the SCMA-RIS scheme is proposed to significantly reduce the MPA decoding complexity and improve the bit error rate performance of the conventional SCMA Monte Carlo simulations and complexity analysis are presented, which support the findings

Reconfigurable Intelligent Surface-Assisted Uplink Sparse Code Multiple Access

Spatial modulation (SM) sparse code multiple access (SCMA) systems provide high spectral efficiency (SE) at the expense of using a high number of transmit antennas. To overcome this drawback, this paper proposes a novel SM-SCMA system operating in uplink transmission, referred to as rotational generalized SM-SCMA (RGSM-SCMA). For the proposed system, the following are introduced, first, transmitter design and its formulation, second, maximum likelihood and maximum a posteriori probability decoders, and finally, practical low-complexity message passing algorithm and its complexity analysis. Simulation results and complexity analysis show that the proposed RGSM-SCMA system delivers the same SE with significant savings in the number of transmit antennas, at the expense of close bit error rate and a negligible increase in the decoding complexity, when compared with SM-SCMA.

Low-Cost Uplink Sparse Code Multiple Access for Spatial Modulation

The Gaussian function (GF) is widely used to explain the behavior or statistical distribution of many natural phenomena as well as industrial processes in different disciplines of engineering and applied science. For example, the GF can be used to model an approximation of the Airy disk in image processing, a laser heat source in laser transmission welding [1], practical microscopic applications [2], and fluorescence dispersion in flow cytometric deoxyribonucleic acid histograms [3]. In applied sciences, the noise that corrupts the signal can be modeled by the Gaussian distribution according to the central limit theorem. Thus, by fitting the GF, researchers can develop a sound interpretation of the corresponding process or phenomenon behavior.

A Fast, Accurate, and Separable Method for Fitting a Gaussian Function [Tips & Tricks]

This letter investigates the reconfigurable intelligent surface (RIS)-assisted multiple-input single-output (MISO) wireless system, where both half-duplex (HD) and full-duplex (FD) operating modes are considered together, for the first time in the literature. The goal is to maximize the rate by optimizing the RIS phase shifts. A novel deep reinforcement learning (DRL) algorithm is proposed to solve the formulated non-convex optimization problem. The complexity analysis and Monte Carlo simulations illustrate that the proposed DRL algorithm significantly improves the rate compared to the non-optimized scenario in both HD and FD operating modes using a single parameter setting. Besides, it significantly reduces the computational complexity of the downlink HD MISO system and improves the achievable rate with a reduced number of steps per episode compared to the conventional DRL algorithm.

Ibrahim Al-Nahhal

Papers

Quadrature Spatial Modulation Decoding Complexity: Study and Reduction

Reconfigurable Intelligent Surface-Assisted Uplink Sparse Code Multiple Access

Low-Cost Uplink Sparse Code Multiple Access for Spatial Modulation

A Fast, Accurate, and Separable Method for Fitting a Gaussian Function [Tips & Tricks]

Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems