5G Massive MIMO Research Articles

Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review.

Massive multiple-input multiple-output (mMIMO) is a wireless access technique that has been studied and investigated in response to the worldwide bandwidth demand in the wireless communication sector (MIMO). Massive MIMO, which brings together antennas at the transmitter and receiver to deliver excellent spectral and energy efficiency with comparatively simple processing, is one of the main enabling technologies for the upcoming generation of networks. To actualize diverse applications of the intelligent sensing system, it is essential for the successful deployment of 5G-and beyond-networks to gain a better understanding of the massive MIMO system and address its underlying problems. The recent huge MIMO systems are highlighted in this paper's thorough analysis of the essential enabling technologies needed for sub-6 GHz 5G networks. This article covers most of the critical issues with mMIMO antenna systems including pilot realized gain, isolation, ECC, efficiency, and bandwidth. In this study, two types of massive 5G MIMO antennas are presented. These types are used depending on the applications at sub-6 GHz bands. The first type of massive MIMO antennas is designed for base station applications, whereas the most recent structures of 5G base station antennas that support massive MIMO are introduced. The second type is constructed for smartphone applications, where several compact antennas designed in literature that can support massive MIMO technology are studied and summarized. As a result, mMIMO antennas are considered as good candidates for 5G systems.

Enhanced AI-Based CSI Prediction Solutions for Massive MIMO in 5G and 6G Systems

Enhanced AI-Based CSI Prediction Solutions for Massive MIMO in 5G and 6G Systems

Near-Field MIMO Communications for 6G: Fundamentals, Challenges, Potentials, and Future Directions

Extremely large-scale antenna array (ELAA) is a common feature of several key candidate technologies for sixth generation (6G) mobile networks, such as ultra-massive multiple-input-multiple-output (UM-MIMO), cell-free massive MIMO, reconfigurable intelligent surface (RIS), and terahertz communications. Since the number of antennas is very large for ELAA, the electromagnetic radiation field needs to be modeled by near-field spherical waves, which differs from the conventional planar-wave-based radiation model of 5G massive MIMO. As a result, near-field MIMO communications will become essential in 6G wireless networks. In this article, we systematically investigate the emerging near-field communication techniques. First, we present the fundamentals of nearfield communications and the metric to determine the near-field ranges in typical communication scenarios. Then we investigate recent studies specific to near-field communications by classifying them into two categories: techniques addressing the challenges and those exploiting the potentials in near-field regions. Their principles, recent progress, pros, and cons are discussed. More importantly, several open problems and future research directions for near-field communications are pointed out. We believe that this article will inspire more innovations in this important research topic of near-field MIMO communications for 6G.

Retracted: 5G Massive MIMO Signal Detection Algorithm Based on Deep Learning.

[This retracts the article DOI: 10.1155/2022/9999951.].

An Analysis of Miniaturization of 5G Massive MIMO Base Station Antenna

This paper reviews current works and efforts on the miniaturization of the 5G Massive MIMO base station antenna design. This paper first introduces the development of the Massive MIMO, with a proper mathematical illustration. Then, some conventional parameters are introduced to measure the performance of the antenna, with a connection to the miniaturization of the antenna. Specifically, two coefficients: the gain-to-volume ratio and isolation-to-volume ratio are constructed to measure the efforts in miniaturization. Tables that collect works in the literature are presented, with detailed comparisons among working situations. Then a general conclusion and future path of miniaturization are indicated. At last, the paper introduced the challenge and future of the miniaturization of the 5G Massive MIMO base station antenna.

A Novel Approximate Message Passing Detection for Massive MIMO 5G System

Massive-Multiple Inputs and Multiple Outputs (M-MIMO) is considered as one of the standard techniques in improving the performance of Fifth Generation (5G) radio. 5G signal detection with low propagation delay and high throughput with minimum computational intricacy are some of the serious concerns in the deployment of 5G. The evaluation of 5G promises a high quality of service (QoS), a high data rate, low latency, and spectral efficiency, ensuring several applications that will improve the services in every sector. The existing detection techniques cannot be utilised in 5G and beyond 5G due to the high complexity issues in their implementation. In the proposed article, the Approximation Message Passing (AMP) is implemented and compared with the existing Minimum Mean Square Error (MMSE) and Message Passing Detector (MPD) algorithms. The outcomes of the work show that the performance of Bit Error Rate (BER) is improved with minimal complexity.

Analysis of Optimal Spectral-energy Efficiency Tradeoff Linear Precoding Massive MIMO 5G and Beyond

In this article, we consider cellular wireless communication with a large number base station antennas deployement, called Massive-MIMO systems, are inspected. Massive MU (multi-user), (MIMO) multiple-input-multiple-output technique gives a significant SE (spectral efficiency) using easy techniques precoding while high EE (energy efficiency) is expected to be offered. Our paper works on two famous schemes, linear precoding Zero-Forcing and Maximum-RatioTransmission taking into account of system circuit power consumption, where that important aspect was ignored in the literature. Both EE (energy efficiency) and SE (spectral efficiency) are considered to be the important key indicators in 5G and beyond communications, which need to be taken into account at the same moment with respect to nowadays environmental consideration. To realize an optimal tradeoff between SE-EE, that precoding schemes are been formulated under CSI-imperfection (channel state information) in a realistic way. Simulation results illustrate that our proposed method achieves better optimal balance between Energy Efficiency and Spectral Efficiency. Under Matlab experimentation, simulation result shows an optimal performance, while tradeoff aspect is observed clairly between SE and EE using such two precoders algorithms. Although such tradeoff SE-EE coincide at first but later on their interests become conflicting and divergent then leading EE to so gradually decrease while SE continues increasing logarithmically. ZF pre-coding method is the appropriate scenario to achieve higher pre-coder performance.

An Optimized Approach for Spectrum Utilization in mmWave Massive MIMO 5G Wireless Networks

An Optimized Approach for Spectrum Utilization in mmWave Massive MIMO 5G Wireless Networks

Energy Efficient Operation of Adaptive Massive MIMO 5G HetNets

Energy Efficient Operation of Adaptive Massive MIMO 5G HetNets

Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemes

Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemes

Analysis and optimization of uplink spectral efficiency in massive multiple-input and multiple-output

Fifth Generation (5G) specifications aims for data rate of 1 Gbps in high mobility and 10 Gbps in low mobility conditions, 15-30 bps/Hz of spectral efficiency with less than 1 milli second (ms) latency reduction. Massive multiple-input and multiple-output (Massive MIMO) is one of the promising technologies in 5G standard which offers a high spectral efficiency improvement. This work focus on the uplink scenario spectral efficiency in a Massive MIMO simulation network based on third generation partnership project (3GPP) and long term evolution (LTE) document of 5G. This work analyzes the spectral efficiency metric by simulating the 5G Massive MIMO network. Then, the research identified major constraint parameters; number of user antennas, K, number of base station antennas, M, transmission power, P, channel bandwidth, B, and coherence time, Tau_C and pilot time Tau_P which plays a significant role in varying this metric. The authors focus on improving the spectral efficiency by passing these constraint parameters through different meta-heurestic optimization algorithms, such as, convex optimization solver, White shark optimization (WSO) and Particle swarm optimization (PSO). The results show an overall, 1-10 percent of improvement of the parameter wnen compared with other research articles. The maximum value achieved is 49.84 bps/Hz, which is three times higher as per to the 3GPP and International Telecommunication Unioin (ITU) release document.

Parametric Analysis of SiO<sub>2 </sub>MOSFET Based Absorber for 5G Massive MIMO Base Station

The performance of the SiO2 MOSFET-based absorber as a solution to arching within transmission lines (used for RF signal transportation) has been realized and analyzed at 28 GHz using the reflected signal from the RX branch of 5G massive MIMO base station. The reflected signal from the receiver (RX) branch of base stations may lead to interference, thus creating a performance reducing condition (arching) within the transmission lines. For optimum performance in the 5G regime, the SiO2 MOSFET has been used to solve the problem of arching within the transmission line under large field intensities of a standing wave resulting from the impedance. The SiO2 MOSFET-based absorber has been observed for a reflectivity of -79.5 dB and a rectification efficiency greater than 17 %

DESIGN OF CONVEX AND CONCAVE DUAL BENT ARRAY FOR 5G LENS ANTENNA SYSTEM

Previous research on the multi-beam system for the fifth generation (5G) application has shown that the number of feed elements of a lens antenna system at the mobile base station is increasing due to the massive MIMO requirements. The design of the array circuits of the 5G lens antenna systems will be more complicated due to the feeding structure, which consists of the power dividers and phase shifters, thus contributing to higher feeding losses. The antenna fabrication process also becomes more complicated. Besides that, the conventional array feed also produces a single radiation beam only. Thus, a large space is needed, where array feed antennas should be arranged to perform wide-angle beam scanning for 5G massive MIMO operation. Therefore, to overcome these issues, this paper uses a bent array configuration as the lens antenna feed to produce bifurcated beam radiation and has a wide beam angle from the feed radiator to the lens edge. Multiple bent arrays are arranged on the lens axis to generate a multi-beam by cylindrical lens antenna. This paper shows a comparison of the bent array in convex and concave configurations. This process investigates the mutual coupling when more than one bent array is arranged as feed radiators. By comparing the arrays in convex and concave structures, it is observed that the convex structure produces a better-bifurcated beam with a smaller middle lobe. The antenna also produces the same bifurcated beam shifting angle, θs for both ports, when s = 4λ is used. It is verified as the optimum spacing value between two bent array structures by the optimization process.

A Novel Digital Predistortion Based on Flexible Characteristic Detection for 5G Massive MIMO Transmitters

In this letter, a novel digital predistortion (DPD) technique based on characteristic detection of power amplifiers (PAs) and flexible tuning box is proposed for linearizing large-scale multiple-input-multiple-output (MIMO) system. In this new method, before linearizing the digital block subarray, the PA characteristics of each transmitting chain are detected first. In this way, all PAs can be linearized dynamically without wasting computation, and the performance can be guaranteed. Experiments are carried out to verify the effectiveness of the structure, an mmWave beamforming transmitter was used for measurement and excitation with two long-term evolution (LTE) input signals of single-carrier 30 MHz and four-carrier 20 MHz. The experimental results show that the proposed technique has lower computational complexity and can achieve excellent linearization performance.

Efficient Resource Optimization of 5G Networks Enabling QoS and QoE in IoT Applications

This paper elaborates the recent enhancements on IoT capabilities and efficiencies. From power, coverage, cost, complexity, device density, core network protocol, spectrum efficiency perspectives, it describes a comprehensive blueprint for driving IoT optimizations. For better mobile broadband experience, enabling Gigabit-class throughput with advanced 5G network techniques, millimeter wave, massive MIMO, carrier aggregation, and LAA benefit massive IoT improvements. Supporting Gigabit-class data rates for high-performance IoT requires high power efficiency. eMTC (enhanced machine-type communication) optimizes for the broadest range of IoT applications with VoLTE and mobility. NB-IoT (narrowband IoT) provides optimizations for high throughput and low delay LPWAN IoT use cases. Index Terms-LTE IoT, eMTC, NB-IoT, QoS, QoE.

5G Massive MIMO Signal Detection Algorithm Based on Deep Learning.

Aiming at the problems of poor signal detection effect caused by many interference factors in large-scale MIMO technology scene, this paper proposes a 5G massive MIMO signal detection algorithm based on deep learning. Firstly, the MIMO system model based on neural network is constructed, and Deep Neural Network (DNN) detection is introduced into the receiver of the traditional MIMO system to obtain the information bits or codewords and channel state information transmitted by transmitters. Then, the end-to-end training method is adopted to make neural network learn the mapping relationship of information bits or codewords transmitted by system transceivers. Furthermore, DNN detector is improved based on Simplified Message Passing Detection (sMPD) algorithm, and the correction factor is updated continuously to optimize network parameters to realize the accurate detection and decoding of the MIMO system. Finally, the proposed algorithm is experimentally analyzed based on the TensorFlow deep learning framework. Experimental results show that when signal-to-noise ratio is 10 dB, the bit error rate and mean square error are lower than 0.005 and 0.1, respectively.

Methodology for Electromagnetic Field Exposure Assessment of 5G Massive MIMO Antennas Accounting for Spatial Variability of Radiated Power

The technology of massive Multiple Input Multiple Output (maMIMO) antennas introduces new challenges in the Radio Frequency Electromagnetic Fields (RF EMF) exposure assessment of 5G base stations due to the aleatory changes of the radiation direction. The beamforming capabilities of the antenna modify the radiation pattern both in space and in time domain in order to adapt the electromagnetic footprint to the changes of user positions, mobility, and service requests. Recently, Base Station (BS) power monitoring counters per Angular Regions (AR) were introduced in maMIMO antenna technology for monitoring the power associated to specific angular directions. By analyzing the time averaged power reported by ARs power monitoring counters the paper proposes, making use of simulations, a methodology to quantify a statistical spatial power reduction factor (0.05 to 0.5, on the base of the assumptions adopted in this paper) to be applied in the RF EMF exposure assessment process accounting for the variability of the maMIMO antenna radiation pattern. The proposed approach enhances and completes the methodology already described in normative documents for characterizing time variations of the total radiated power. The paper is completed with a sensitivity analysis for the size of each AR with respect to the antenna beam size; the analysis shows that a good spatial resolution is achieved when the AR Region has almost the same size as the beam’s half power beam width.

Machine Learning Based Beam Selection With Low Complexity Hybrid Beamforming Design for 5G Massive MIMO Systems

In this paper, we present an energy-efficient joint machine learning based beam-user selection and low complexity hybrid beamforming for the multiuser massive multiple-input multiple-output (MIMO) downlink system. Massive MIMO system is a key enabler of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5^{th}$ </tex-math></inline-formula> generation (5G) technology which is being used in vehicle-to-everything (V2X) communications and other Internet-of-Things (IoT). The large number of antennas at the base-station side causes high power consumption in the radio frequency (RF) chain. Hybrid beamforming techniques are used to reduce the number of RF chains with minimal performance loss. This paper proposes a low complexity orthogonal hybrid beamforming design with the aid of a machine learning based beam-user selection scheme. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Householder</i> (HH) reflectors are used to generate the orthogonal analog beamforming (ABF) matrix. We use a feedforward neural network (FFNN) beam-user selection scheme. The proposed HH ABF + FFNN scheme provides better energy-efficiency (EE) performance in the ill-conditioned massive MIMO channel. It has been shown that the green SE-EE point (41.35 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b/s/Hz</i> , 0.8 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b/Hz/J</i> ) can be obtained by the proposed HH ABF + FFNN scheme as compared to the (33.77 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b/s/Hz</i> , 0.63 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b/Hz/J</i> ) from the state-of-the-art Discrete Fourier transform based techniques.

Review On Channel Estimation In 5g Massive Mimo Using Tdm and Ai

Review On Channel Estimation In 5g Massive Mimo Using Tdm and Ai

GPU based Image Compression with Scaling-Hard-Threshold Using HWT (Haar-Wavelet-Transform) + RL (Run-Length) Pair Encoding

In 5G MASSIVE MIMO the main problem for better video communication is of transmission bandwidth issues which in turn reflects in video conferencing, Mobile-learning, gaming, graphics applications etc. However, in regard to a versatile framework i.e. a computerized camera with web and correspondence office, at that point the transmission capacity for correspondence and additionally stockpiling are of genuine concern. Tremendous valuable data must be put away and recovered proficiently for down to earth purposes. This is the novel methodology that scaling technique has proposed for 2D Haar wavelet transform based compression of picture utilizing parameter CR- ratio of Compression with PSNR improvement, with maintaining picture quality, less bandwidth usage with better compression ratio which increase by increasing speed with the help of NVIDIA GPU.