scholarly journals A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 388 ◽  
Author(s):  
Mahmoud A. Albreem ◽  
Mohammed H. Alsharif ◽  
Sunghwan Kim
Keyword(s):  
Communication Systems ◽  
Radio Channel ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Base Station ◽  
Matrix Inversion ◽  
Linear Detector ◽  
Radio Channels ◽  
Inversion Methods ◽  
Input Multiple Output

Massive multiple-input multiple-output (M-MIMO) is a substantial pillar in fifth generation (5G) mobile communication systems. Although the maximum likelihood (ML) detector attains the optimum performance, it has an exponential complexity. Linear detectors are one of the substitutions and they are comparatively simple to implement. Unfortunately, they sustain a considerable performance loss in high loaded systems. They also include a matrix inversion which is not hardware-friendly. In addition, if the channel matrix is singular or nearly singular, the system will be classified as an ill-conditioned and hence, the signal cannot be equalized. To defeat the inherent noise enhancement, iterative matrix inversion methods are used in the detectors’ design where approximate matrix inversion is replacing the exact computation. In this paper, we study a linear detector based on iterative matrix inversion methods in realistic radio channels called QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) package. Numerical results illustrate that the conjugate-gradient (CG) method is numerically robust and obtains the best performance with lowest number of multiplications. In the QuaDRiGA environment, iterative methods crave large n to obtain a pleasurable performance. This paper also shows that when the ratio between the user antennas and base station (BS) antennas ( β ) is close to 1, iterative matrix inversion methods are not attaining a good detector’s performance.

scholarly journals Study of 5G-NR-MIMO Links in the Presence of an Interferer

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 732
Author(s):  
Avner Elgam ◽  
Yael Balal ◽  
Yosef Pinhasi
Keyword(s):  
Mobile Networks ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Local Area ◽  
Maximum Energy ◽  
Base Station ◽  
Small Cells ◽  
Diversity Coding ◽  
Shift Keying ◽  
Input Multiple Output

Many communication systems are based on the Multiple Input, Multiple Output (MIMO) scheme, and Orthogonal Space–time Block Transmit diversity Coding (OSTBC), combined with Maximal Ratio Receive Combining (MRRC), to create an optimal diversity system. A system with optimal diversity fixes and optimizes the channel’s effects under multi-path and Rayleigh fading with maximum energy efficiency; however, the challenge does not end with dealing with the channel destruction of the multi-path impacts. Susceptibility to interference is a significant vulnerability in future wireless mobile networks. The 5th Generation New Radio (5G-NR) technologies bring hundreds of small cells and pieces of User Equipment (UE) per indoor or outdoor local area scenario under a specific Long Term Evolution (LTE)-based station (e-NodeB), or under 5G-NR base-station (g-NodeB). It is necessary to study issues that deal with many interference signals, and smart jammers from advanced communication equipment cause deterioration in the links between the UE, the small cells, and the NodeB. In this paper, we study and present the significant impact and performances of 2×2 Alamouti Phase-Shift Keying (PSK) modulation techniques in the presence of an interferer and a smart jammer. The destructive effects affecting the MIMO array and the advanced diversity technique without closed-loop MIMO are analyzed. The performance is evaluated in terms of Bit Error Rate (BER) vs. Signal to Interference Ratio (SIR). In addition, we proved the impairment of the orthogonal spectrum assumption mathematically.

scholarly journals Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1844
Author(s):  
Minhoe Kim ◽  
Woongsup Lee ◽  
Dong-Ho Cho
Keyword(s):  
Communication Systems ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Q Learning ◽  
Multiple Input ◽  
Beam Selection ◽  
Input Multiple Output ◽  
Optimal Beam ◽  
Resource Allocation Scheme

In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

scholarly journals Controlling Initial and Final Radii to Achieve a Low-Complexity Sphere Decoding Technique in MIMO Channels

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Fatemeh Eshagh Hosseini ◽  
Shahriar Shirvani Moghaddam
Keyword(s):  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Sphere Decoding ◽  
Mimo Channels ◽  
Threshold Factor ◽  
Multiple Input ◽  
Input Multiple Output ◽  
The Cost ◽  
Practical Constraints

In order to apply sphere decoding algorithm in multiple-input multiple-output communication systems and to make it feasible for real-time applications, its computational complexity should be decreased. To achieve this goal, this paper provides some useful insights into the effect of initial and the final sphere radii and estimating them effortlessly. It also discusses practical ways of initiating the algorithm properly and terminating it before the normal end of the process as well as the cost of these methods. Besides, a novel algorithm is introduced which utilizes the presented techniques according to a threshold factor which is defined in terms of the number of transmit antennas and the noise variance. Simulation results show that the proposed algorithm offers a desirable performance and reasonable complexity satisfying practical constraints.

scholarly journals Low complexity full duplex MIMO systems: Analog canceler architectures, beamforming design, and future directions

2021 ◽  
Vol 2 (2) ◽  
pp. 109-127
Author(s):  
George C. Alexandropoulos
Keyword(s):  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Full Duplex ◽  
Hardware Complexity ◽  
Algorithmic Solution ◽  
Input Multiple Output ◽  
The One ◽  
Rate Optimization

The hardware complexity of the analog Self-Interference (SI) canceler in conventional full duplex Multiple Input Multiple Output (MIMO) designs mostly scales with the number of transmit and receive antennas, thus exploiting the benefits of analog cancellation becomes impractical for full duplex MIMO transceivers, even for a moderate number of antennas. In this paper, we provide an overview of two recent hardware architectures for the analog canceler comprising of reduced number of cancellation elements, compared to the state of the art, and simple multiplexers for efficient signal routing among the transceiver radio-frequency chains. The one architecture is based on analog taps and the other on AUXiliary (AUX) Transmitters (TXs). In contrast to the available analog cancellation architectures, the values for each tap or each AUX TX and the configuration of the multiplexers are jointly designed with the digital transceiver beamforming filters according to desired performance objectives. We present a general optimization framework for the joint design of analog SI cancellation and digital beamforming, and detail an example algorithmic solution for the sum-rate optimization objective. Our representative computer simulation results demonstrate the superiority, both in terms of hardware complexity and achievable performance, of the presented low complexity full duplex MIMO schemes over the relative available ones in the literature. We conclude the paper with a discussion on recent simultaneous transmit and receive operations capitalizing on the presented architectures, and provide a list of open challenges and research directions for future FD MIMO communication systems, as well as their promising applications.

Efficient Iterative Massive MIMO Detectors Based on Iterative Matrix Inversion Methods

2021 ◽  
pp. 175-195
Author(s):  
Mahmoud Albreem
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Matrix Inversion ◽  
Modulation Scheme ◽  
Fifth Generation ◽  
Wide Range ◽  
Input Multiple Output ◽  
Successive Over Relaxation

Massive multiple-input multiple-output (MIMO) is a key technology in fifth generation (5G) communication systems. Although the maximum likelihood (ML) obtains an optimal performance, it is prohibited in realization because of its high computational complexity. Linear detectors are an alternative solution, but they contain a matrix inversion which is not hardware friendly. Several methods have been proposed to approximate or to avoid the computation of exact matrix inversion. This chapter garners those methods and study their applicability in massive MIMO system so that a generalist in communication systems can differentiate between different algorithms from a wide range of solutions. This chapter presents the performance-complexity profile of a detector based on the Neuamnn-series (NS), Newton iteration (NI), successive over relaxation (SOR), Gauss-Seidel (GS), Jacobi (JA), Richardson (RI), optimized coordinate descent (OCD), and conjugate-gradient (CG) methods in 8×64, 16×64, and 32×64 MIMO sizes, and modulation scheme is 64QAM.

scholarly journals Low-Complexity Joint Channel Estimation for Multi-User mmWave Massive MIMO Systems

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 301
Author(s):  
Jianhe Du ◽  
Jiaqi Li ◽  
Jing He ◽  
Yalin Guan ◽  
Heyun Lin
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Step Length ◽  
Low Complexity ◽  
Base Station ◽  
Estimation Accuracy ◽  
Input Multiple Output

For multi-user millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the precise acquisition of channel state information (CSI) is a huge challenge. With the increase of the number of antennas at the base station (BS), the traditional channel estimation techniques encounter the problems of pilot training overhead and computational complexity increasing dramatically. In this paper, we develop a step-length optimization-based joint iterative scheme for multi-user mmWave massive MIMO systems to improve channel estimation performance. The proposed estimation algorithm provides the BS with full knowledge of all channel parameters involved in up- and down-links. Compared with existing algorithms, the proposed algorithm has higher channel estimation accuracy with low complexity. Moreover, the proposed scheme performs well even with a small number of training sequences and a large number of users. Simulation results are shown to demonstrate the performance of the proposed channel estimation algorithm.

scholarly journals Impact of Stair and Diagonal Matrices in Iterative Linear Massive MIMO Uplink Detectors for 5G Wireless Networks

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 71 ◽  
Author(s):  
Mahmoud A. Albreem ◽  
Mohammed H. Alsharif ◽  
Sunghwan Kim
Keyword(s):  
Computational Complexity ◽  
Iterative Methods ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Matrix Inversion ◽  
Multiple Input ◽  
The Matrix ◽  
Input Multiple Output ◽  
The Impact

In massive multiple-input multiple-output (M-MIMO) systems, a detector based on maximum likelihood (ML) algorithm attains optimum performance, but it exhaustively searches all possible solutions, hence, it has a very high complexity and realization is denied. Linear detectors are an alternative solution because of low complexity and simplicity in implementation. Unfortunately, they culminate in a matrix inversion that increases the computational complexity in high loaded systems. Therefore, several iterative methods have been proposed to approximate or avoid the matrix inversion, such as the Neuamnn series (NS), Newton iterations (NI), successive overrelaxation (SOR), Gauss–Siedel (GS), Jacobi (JA), and Richardson (RI) methods. However, a detector based on iterative methods requires a pre-processing and initialization where good initialization impresses the convergence, the performance, and the complexity. Most of the existing iterative linear detectors are using a diagonal matrix ( D ) in initialization because the equalization matrix is almost diagonal. This paper studies the impact of utilizing a stair matrix ( S ) instead of D in initializing the linear M-MIMO uplink (UL) detector. A comparison between iterative linear M-MIMO UL detectors with D and S is presented in performance and computational complexity. Numerical Results show that utilization of S achieves the target performance within few iterations, and, hence, the computational complexity is reduced. A detector based on the GS and S achieved a satisfactory bit-error-rate (BER) with the lowest complexity.

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