scispace - formally typeset
Search or ask a question
Author

Mahmoud A. M. Albreem

Bio: Mahmoud A. M. Albreem is an academic researcher from University of Sharjah. The author has contributed to research in topics: MIMO & Detector. The author has an hindex of 10, co-authored 70 publications receiving 544 citations. Previous affiliations of Mahmoud A. M. Albreem include Universiti Malaysia Perlis & Universiti Sains Malaysia.


Papers
More filters
Journal ArticleDOI
TL;DR: This paper discusses 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, and presents recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms.
Abstract: 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.

262 citations

Journal ArticleDOI
24 Apr 2020-Symmetry
TL;DR: This study highlights the most promising lines of research from the recent literature in common directions for the 6G project, exploring the critical issues and key potential features of 6G communications and contributing significantly to opening new horizons for future research directions.
Abstract: The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the stratification of the communication needs of the 2030s. In support of this vision, this study highlights the most promising lines of research from the recent literature in common directions for the 6G project. Its core contribution involves exploring the critical issues and key potential features of 6G communications, including: (i) vision and key features; (ii) challenges and potential solutions; and (iii) research activities. These controversial research topics were profoundly examined in relation to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion. Thus, this article will contribute significantly to opening new horizons for future research directions.

207 citations

Proceedings ArticleDOI
21 Apr 2015
TL;DR: Why there is a need for 5G, advantages, and challenges, and a comprehensive study related to 5G has been presented.
Abstract: The fourth generation wireless communication (4G) systems have been deployed or are soon to be deployed in many countries. However, with an explosion of wireless mobile devices and services, there are still some challenges that cannot be accommodated even by 4G, such as the spectrum crises and high energy consumption. Wireless system designers have been facing the continuously increasing demand for high data rates and mobility required by new wireless applications and therefore have started research on fifth generation (5G) wireless systems that are expected to be deployed beyond 2020. The main purpose of 5G is planned to design the best wireless world that is free from limitations and hindrance of the previous generations. 5G is going to change the way most high bandwidth users access their mobile radio communication. Therefore, this paper presents why there is a need for 5G, advantages, and challenges. Moreover, a comprehensive study related to 5G has been presented.

85 citations

Proceedings ArticleDOI
01 Nov 2017
TL;DR: An overview regarding green IoT, which discusses the life cycle of green IoT which contains green design, green production, green utilization, and green recycling, and studies of IoT in 5G and IoT for smart cities are presented.
Abstract: Internet of Things (IoT) connects everything in the smart world, and thus, energy consumption of IoT technology is a challenge and attractive research area. Motivated by achieving a low power consumption IoT, a green IoT is proposed. This paper provides an overview regarding green IoT. It also discusses the life cycle of green IoT which contains green design, green production, green utilization, and green recycling. Furthermore, green IoT technologies such as green tags, green sensing networks and green internet technologies are discussed. In addition, studies of IoT in 5G and IoT for smart cities are presented. Finally, future research directions and open challenges about green IoT are presented.

65 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide insights on linear precoding algorithms for massive MIMO systems and discuss the performance and energy efficiency of the precoders. And they also present potential future directions of linear precoder algorithms.
Abstract: 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.

64 citations


Cited by
More filters
Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal Article
TL;DR: In this article, the optimal number of scheduled users in a massive MIMO system with arbitrary pilot reuse and random user locations is analyzed in a closed form, while simulations are used to show what happens at finite $M$, in different interference scenarios, with different pilot reuse factors, and for different processing schemes.
Abstract: Massive MIMO is a promising technique for increasing the spectral efficiency (SE) of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent transceiver processing. A common rule-of-thumb is that these systems should have an order of magnitude more antennas $M$ than scheduled users $K$ because the users’ channels are likely to be near-orthogonal when $M/K > 10$ . However, it has not been proved that this rule-of-thumb actually maximizes the SE. In this paper, we analyze how the optimal number of scheduled users $K^\star$ depends on $M$ and other system parameters. To this end, new SE expressions are derived to enable efficient system-level analysis with power control, arbitrary pilot reuse, and random user locations. The value of $K^\star$ in the large- $M$ regime is derived in closed form, while simulations are used to show what happens at finite $M$ , in different interference scenarios, with different pilot reuse factors, and for different processing schemes. Up to half the coherence block should be dedicated to pilots and the optimal $M/K$ is less than 10 in many cases of practical relevance. Interestingly, $K^\star$ depends strongly on the processing scheme and hence it is unfair to compare different schemes using the same $K$ .

363 citations

Journal Article
TL;DR: Cooperative spectrum sensing in cognitive radio(CR) networks is analyzed and detection probability to primary users can be improved by multi-cooperative users in multi-user networks.
Abstract: Cognitive users need to detect primary users continuously and rapidly.Cooperative spectrum sensing in cognitive radio(CR) networks is analyzed.Cooperative spectrum sensing in the ideal network with two cognitive users can reduce the mean detection time.Detection probability to primary users can be improved by multi-cooperative users in multi-user networks.Finally,the realization of multi-user networks is considered.

267 citations

Journal ArticleDOI
TL;DR: This paper discusses 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, and presents recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms.
Abstract: 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.

262 citations

Journal Article
TL;DR: The model of inductive power system is set up, and how to choose the compensation topology and resonant frequency is analyzed, and the impact of the power transfer due to the variety of load is analyzed.
Abstract: The model of inductive power system is set up,and how to choose the compensation topology and resonant frequency is analyzed.According to the analysis result,the model of inductive power system is set up,and the impact of the power transfer due to the variety of load is analyzed.

213 citations