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Massive multiple-input multiple-output (MIMO) is an essential technology in fifth-generation (5G) and beyond 5G (B5G) communication systems. Massive MIMO is employed to meet the increasing request for high capacity in next-generation wireless communication networks. However, signal processing in massive MIMO incurs a high complexity due to a large number of transmitting and receiving antenna elements. In this paper, we propose low complexity massive MIMO data detection techniques based on zero-forcing (ZF) and vertical bell laboratories layered space-time (V-BLAST) method in combination with approximate matrix inversion techniques; Neumann series (NS) and Newton iteration (NI). The proposed techniques reduce the complexity of the ZF V-BLAST method since they avoid the exact matrix inverse computation. Initialization based on a stair matrix is also exploited to balance the performance and the complexity. In addition, we propose a massive MIMO detector based on approximate matrix inversion with a stair matrix initialization and deep learning (DL) based detector; MM Network (MMNet) algorithm. MMNet contains a linear transformation followed by a non-linear denoising stage. As signals propagate through the MMNet, the noise distribution at the input of the denoiser stages approaches a Gaussian distribution, form precisely the conditions in which the denoisers can attenuate noise maximally. We validated the performance of the proposed massive MIMO detection schemes in Gaussian and realistic channel models, i.e., Quadriga channels models. Simulations demonstrate that the proposed detectors achieve a remarkable improvement in the performance with a notable computational complexity reduction when compared to conventional ZF V-BLAST and the MMNET in both simple and real channel scenarios.

Massive multiple-input multiple-output (MIMO) is playing a crucial role in the fifth generation (5G) and beyond 5G (B5G) communication systems. Unfortunately, the complexity of massive MIMO systems is tremendously increased when a large number of antennas and radio frequency chains (RF) are utilized. Therefore, a plethora of research efforts has been conducted to find the optimal precoding algorithm with lowest complexity. The main aim of this paper is to provide insights on such precoding algorithms to a generalist of wireless communications. The added value of this paper is that the classification of massive MIMO precoding algorithms is provided with easily distinguishable classes of precoding solutions. This paper covers linear precoding algorithms starting with precoders based on approximate matrix inversion methods such as the truncated polynomial expansion (TPE), the Neumann series approximation (NSA), the Newton iteration (NI), and the Chebyshev iteration (CI) algorithms. The paper also presents the fixed-point iteration-based linear precoding algorithms such as the Gauss-Seidel (GS) algorithm, the successive over relaxation (SOR) algorithm, the conjugate gradient (CG) algorithm, and the Jacobi iteration (JI) algorithm. In addition, the paper reviews the direct matrix decomposition based linear precoding algorithms such as the QR decomposition and Cholesky decomposition (CD). The non-linear precoders are also presented which include the dirty-paper coding (DPC), Tomlinson-Harashima (TH), vector perturbation (VP), and lattice reduction aided (LR) algorithms. Due to the necessity to deal with a high consuming power by the base station (BS) with a large number of antennas in massive MIMO systems, a special subsection is included to describe the characteristics of the peak-to-average power ratio precoding (PAPR) algorithms such as the constant envelope (CE) algorithm, approximate message passing (AMP), and quantized precoding (QP) algorithms. This paper also reviews the machine learning role in precoding techniques. Although many precoding techniques are essentially proposed for a small-scale MIMO, they have been exploited in massive MIMO networks. Therefore, this paper presents the application of small-scale MIMO precoding techniques for massive MIMO. This paper demonstrates the precoding schemes in promising multiple antenna technologies such as the cell-free massive MIMO (CF-M-MIMO), beamspace massive MIMO, and intelligent reflecting surfaces (IRSs). In-depth discussion on the pros and cons, performance-complexity profile, and implementation solidity is provided. This paper also provides a discussion on the channel estimation and energy efficiency. This paper also presents potential future directions in massive MIMO precoding algorithms.

Green wireless networking has attracted considerable research attention, especially in academics and industry from economic and ecological perspectives. Promoting wireless infrastructures by exploiting green power sources has the potential to enhance sustainability and address the adverse impact of conventional power sources. A sustainable optimal standalone solar-powered model for green cellular base stations in urban locations of South Korea is proposed in this work to extend 24-hour uninterrupted power supply support to LTE cellular base stations (BSs) and take advantage of integrated storage devices. The optimal system architecture, energy management, and economic analysis are examined using the hybrid optimization model for electric renewable optimization software based on actual prevailing conditions of regions and their technical feasibility. Results showed that the proposed solar photovoltaic system can achieve significant operational expenditure savings of up to 43% and 43.58% in on- and off-grid sites, respectively, and reduce greenhouse gas emissions in the telecommunications sector. Moreover, the results of this study can provide a stronger platform for a sustainable green wireless network paradigm that can ensure energy sustainability compared with conventional technology.

Internet of things (IoT) is one of key pillars in fifth generation (5G) and beyond 5G (B5G) networks. It is estimated to have 42 billion IoT devices by the year 2025. Currently, carbon emissions and electronic waste (e-waste) are significant challenges in the information & communication technologies (ICT) sector. The aim of this article is to provide insights on green IoT (GIoT) applications, practices, awareness, and challenges to a generalist of wireless communications. We garner various efficient enablers, architectures, environmental impacts, technologies, energy models, and strategies, so that a reader can find a wider range of GIoT knowledge. In this article, various energy efficient hardware design principles, data-centers, and software based data traffic management techniques are discussed as enablers of GIoTs. Energy models of IoT devices are presented in terms of data communication, actuation process, static power dissipation and generated power by harvesting techniques for optimal power budgeting. In addition, this article presents various effective behavioral change models and strategies to create awareness about energy conservation among users and service providers of IoTs. Fog/Edge computing offers a platform that extends cloud services at the edge of network and hence reduces latency, alleviates power consumption, offers improved mobility, bandwidth, data privacy, and security. Therefore, we present the energy consumption model of a fog-based service under various scenarios. Problems related to ever increasing data in IoT networks can be solved by integrating artificial intelligence (AI) along with machine learning (ML) models in IoT networks. Therefore, this article provides insights on role of the ML in the GIoT. We also present how legislative policies support adoption of recycling process by various stakeholders. In addition, this article is presenting future research goals towards energy efficient hardware design principles and a need of coordination between policy makers, IoT devices manufacturers along with service providers.

Massive multiple-input multiple-output (M-MIMO) is a significant pillar in fifth generation (5G) networks where a large number of antennas is deployed. It provides massive advantages to modern communication systems in data rate, spectral efficiency, number of users serviced simultaneously, energy efficiency, and quality of service (QoS). However, it requires advanced signal processing for data detection. The growing MIMO size leads to complicated scenarios, which makes the detector design a knotty problem. The problem is also becoming more complicated when high-order modulation schemes are exploited and more users are multiplexed. Therefore, it is not practical to employ the maximum likelihood (ML) detector despite the excellent performance. Linear detectors are alternative solutions and relatively simple. Unfortunately, they still need an exact matrix inversion computation, which bears to a significant high complexity. Therefore, several iterative methods are utilized to approximate or evade the matrix inversion rather than computing it. This paper studies the pros and cons of iterative matrix inversion methods where the number of computations and bit-error-rate (BER) are considered to compare between the methods. The comparison is conducted in several scenarios such as different ratio between the number of base station (BS) antennas and user terminal (UT) antennas ( $\beta $ ), the number of iterations ( ${n}$ ), and the relaxation parameter ( $\omega $ ). This paper also studies the impact of $\omega $ in the performance of Richardson (RI) and the successive over-relaxation (SOR) methods. Numerical results show that the conjugate gradient (CG) and optimized coordinate descent (OCD) methods exhibit the lowest complexity with an acceptable performance. In addition, the Gauss-Seidel (GS) method outperforms all other detectors with a trivial complexity increment. It is also noticed that the performance is not improved with every iteration. It is also shown that $\omega $ has a great impact and a significant role in achieving a satisfactory performance in both RI and SOR based detectors. From implementation point of view, detectors based on RI, OCD, and CG methods have achieved the highest hardware efficiency (HE) while Jacobi (JA) based detector has obtained the lowest HE. Recent research advances of detection methods are also presented in the open research direction with a potential impact of linear detection methods in initialization and pre-processing.

With the rapid increase in the development of a cellular communication system, remote health monitoring and smart health care are improving and getting through a swift transformation. Currently, we are utilizing the advance long term evolution (A-LTE) network to support the modern health care. Nevertheless, smart hospital/health concern is not fully evolved all around the world. The rollout of the fifth generation (5G) will improve the standard of the smart health care. However, requirements of a smart hospital will be different as compared to other applications such as education, industries, and the public. The smart hospital will be connected 24/7, with several small devices integrated with the sensors. In simple words, the future smart hospital will be based on the 5G and the internet of things (IoT), expected to augment the system coverage, effectiveness, and throughput of the system. Further, high speed, low latency, spectral efficiency, and low energy consumption are the requirements of the 5G based modern hospital. In this correspondence, we focused to improve the latency, spectrum, and throughput of the 5G network by implementing a hybrid detection technique based on the QR decomposition and the M algorithm-maximum likelihood detection (QRM-MLD) and beamforming (BF) for massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) system. In addition, a comparison between the proposed and conventional detection techniques is presented. The proposed hybrid detection technique improves the throughput of the system and reduces the computational complexity as compared to the conventional QRM-MLD algorithm, conventional BF and zero-forcing (ZF) techniques on the platform of several parameters i.e. complexity, bit error rate (BER), peak power, etc.