Secure Massive MIMO System with Two-Way Relay Cooperative Transmission in 6G Networks

Abstract With the advent of Internet of Everything (IoE) and the era of big data, massive multiple-input multiple-output (MIMO) is considered an essential technology to meet the growing communication requirements for beyond 5G and the forthcoming 6G networks. This paper considers a secure massive MIMO system, where the legitimate user and the base station (BS) exchange messages via two-way relays with the presence of passive eavesdroppers. To achieve the trade-off between the physical-layer security and communication reliability, we design a cooperative transmission mode based on multiple-relay collaboration, where some relays broadcast the received signals and other relays act as friendly jammers to prevent the interception by eavesdroppers. A quantum chemical reaction optimization (QCRO) algorithm is proposed to find the most suitable scheme for multiple-relay collaboration. Simulation results highlight excellent performance of the proposed transmission mode under QCRO in different communication scenarios, which can be considered a potential solution for the security issue in future wireless networks.

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A companding approach for PAPR suppression in OFDM based massive MIMO system

Journal of Optical Communications ◽

10.1515/joc-2020-0255 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Ajay Kumar Yadav ◽

Pritam Keshari Sahoo ◽

Yogendra Kumar Prajapati

Keyword(s):

Massive Mimo ◽

Orthogonal Frequency Division Multiplexing ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Average Power ◽

Base Station ◽

Convex Optimization Problem ◽

Multiple Input ◽

Input Multiple Output ◽

Mimo Ofdm

Abstract Orthogonal frequency division multiplexing (OFDM) based massive multiuser (MU) multiple input multiple output (MIMO) system is popularly known as high peak-to-average power ratio (PAPR) issue. The OFDM-based massive MIMO system exhibits large number of antennas at Base Station (BS) due to the use of large number of high-power amplifiers (HPA). High PAPR causes HPAs to work in a nonlinear region, and hardware cost of nonlinear HPAs are very high and also power inefficient. Hence, to tackle this problem, this manuscript suggests a novel scheme based on the joint MU precoding and PAPR minimization (PP) expressed as a convex optimization problem solved by steepest gradient descent (GD) with μ-law companding approach. Therefore, we develop a new scheme mentioned to as MU-PP-GDs with μ-law companding to minimize PAPR by compressing and enlarging of massive MIMO OFDM signals simultaneously. At CCDF = 10−3, the proposed scheme (MU-PP-GDs with μ-law companding for Iterations = 100) minimizes the PAPR to 3.70 dB which is better than that of MU-PP-GDs, (iteration = 100) as shown in simulation results.

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Deep Learning Based Analog Beamforming Design for Millimetre Wave Massive MIMO System

10.21203/rs.3.rs-661112/v1 ◽

2021 ◽

Author(s):

Rajdeep Singh Sohal ◽

Vinit Grewal ◽

Jaipreet Kaur ◽

Maninder Lal Singh

Keyword(s):

Deep Learning ◽

Massive Mimo ◽

Large Scale ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Design Procedure ◽

Base Station ◽

Small Scale ◽

Millimetre Wave ◽

Input Multiple Output

Abstract Analog beamforming (ABF) architectures for both large-scale antennas at base station (BS) and small-scale antennas at user side in millimetre wave (mmWave) channel are constructed and investigated in this paper with the aid of deep learning (DL) techniques. Transmit and receive beamformers are selected through offline training of ABF network that accepts input as channel. The joint optimization of both beamformers based on DL for maximization of spectral efficiency (SE) for massive multiple input multiple output (M-MIMO) system has been employed. This design procedure is carried out under imperfect channel state information (CSI) conditions and the proposed design of precoders and combiners shows robustness to imperfect CSI. The simulation results verify the superiority in terms of SE of deep neutral network (DNN) enabled beamforming (BF) design of mmWave massive MIMO system compared with the conventional BF algorithms while lessening the computational complexity.

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Implementation of a Zero-Forcing Precoding Algorithm Combined with Adaptive Beamforming Based on WiMAX System

International Journal of Antennas and Propagation ◽

10.1155/2013/976301 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Hyunwook Yang ◽

Seungwon Choi

Keyword(s):

Mimo System ◽

Multiple Input Multiple Output ◽

Experimental System ◽

Multiple Access Interference ◽

Adaptive Beamforming ◽

Base Station ◽

Zero Forcing ◽

Single Antenna ◽

Input Multiple Output ◽

Wimax System

We propose a novel precoding algorithm that is a zero-forcing (ZF) method combined with adaptive beamforming in the Worldwide Interoperability for Microwave Access (WiMAX) system. In a Multiuser Multiple-Input Multiple-Output (MU-MIMO) system, ZF is used to eliminate the Multiple Access Interference (MAI) in order to allow several users to share a common resource. The adaptive beamforming algorithm is used to achieve the desired SNR gain. The experimental system consists of a WiMAX base station that has 2 MIMO elements, each of which is composed of three-array antennas and two mobile terminals, each of which has a single antenna. Through computer simulations, we verified that the proposed method outperforms the conventional ZF method by at least 2.4 dB when the BER is 0.1%, or 1.7 dB when the FER is 1%, in terms of the SNR. Through a hardware implementation of the proposed method, we verified the feasibility of the proposed method for realizing a practical WiMAX base station to utilize the channel resources as efficiently as possible.

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The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels

Entropy ◽

10.3390/e23111552 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1552

Author(s):

Tongzhou Han ◽

Danfeng Zhao

Keyword(s):

Power Control ◽

Massive Mimo ◽

Mimo Systems ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Time Varying ◽

Input Multiple Output ◽

Efficiency Performance ◽

A Cell ◽

Time Varying Channel

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.

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A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments

Sensors ◽

10.3390/s19010164 ◽

2019 ◽

Vol 19 (1) ◽

pp. 164 ◽

Cited By ~ 11

Author(s):

Zahra Mokhtari ◽

Maryam Sabbaghian ◽

Rui Dinis

Keyword(s):

Massive Mimo ◽

Orthogonal Frequency Division Multiplexing ◽

Mimo Systems ◽

Low Cost ◽

Multiple Input Multiple Output ◽

Base Station ◽

Aging Effects ◽

Hardware Impairments ◽

Single Carrier ◽

Input Multiple Output

Massive multiple input multiple output (MIMO) technology is one of the promising technologies for fifth generation (5G) cellular communications. In this technology, each cell has a base station (BS) with a large number of antennas, allowing the simultaneous use of the same resources (e.g., frequency and/or time slots) by multiple users of a cell. Therefore, massive MIMO systems can bring very high spectral and power efficiencies. However, this technology faces some important issues that need to be addressed. One of these issues is the performance degradation due to hardware impairments, since low-cost RF chains need to be employed. Another issue is the channel estimation and channel aging effects, especially in fast mobility environments. In this paper we will perform a comprehensive study on these two issues considering two of the most promising candidate waveforms for massive MIMO systems: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain processing (SC-FDP). The studies and the results show that hardware impairments and inaccurate channel knowledge can degrade the performance of massive MIMO systems extensively. However, using suitable low complex estimation and compensation techniques and also selecting a suitable waveform can reduce these effects.

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A Study of a Competitive Reinforcement Learning Approach for Joint Spatial Division and Multiplexing in Massive MIMO

ECTI Transactions on Electrical Engineering, Electronics, and Communications ◽

10.37936/ecti-eec.2021191.226832 ◽

2021 ◽

Vol 19 (1) ◽

pp. 83-93

Author(s):

Tanyaluk Deeka ◽

Boriboon Deeka ◽

Surajate On-rit

Keyword(s):

Massive Mimo ◽

Energy Use ◽

Multiple Input Multiple Output ◽

Main Idea ◽

Base Station ◽

Machine Learning Algorithms ◽

System Throughput ◽

Computational Time ◽

User Grouping ◽

Input Multiple Output

Massive Multiple-Input Multiple-Output (MIMO) is widely considered a pivotal communication technology for future generations of wireless networks. Massive MIMO uses a large number of antennas at the base station, which offers better effectiveness in spectral and energy use. However, a Frequency Division Duplex (FDD) system is challenging in reciprocity since it is difficult to estimate channels and requires feeding back channel state information. Joint Spatial Division and Multiplexing (JSDM) is a simplified FDD technique to provide massive MIMO gains. The main idea of JSDM is related to grouping users with approximately similar channel covariance. Many machine learning algorithms have been applied to conduct user grouping. In this paper, to improve the user grouping, we employ Reinforcement Guided Competitive Learning (RGCL) to the user grouping and then compare it with clustering techniques, including K-means, and sequential K-means to achieve the appropriate user grouping. The experimental results show that the RGCL technique represents better performance in computational time and system throughput than the other two above mentioned techniques, since RGCL can avoid being trapping in local minima.

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Pilot Assignment Based on Graph Coloring and Location Information in Multicell Multiuser Massive MIMO Systems

Wireless Communications and Mobile Computing ◽

10.1155/2021/9913149 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Canyun Xiong ◽

Shiyong Chen ◽

Liang Li ◽

Yucheng Wu

Keyword(s):

Graph Coloring ◽

Mimo Systems ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Base Station ◽

Location Information ◽

Angle Of Arrival ◽

Intercell Interference ◽

Interference Graph ◽

Input Multiple Output

A massive multiple-input multiple-output (MIMO) system uses a large number of antennas in the base station (BS) to serve multiple users, which significantly improves the capacity of the system. However, in time division duplex (TDD) mode, the pilot contamination (PC) is inevitable due to the multiplexing of pilots. This paper proposed a pilot assignment based on graph coloring and location information (GC-LI) to improve the performance of users. Specifically, based on graph coloring, the proposed GC-LI algorithm combines location information like the angle of arrival (AoA), distance, and correlation to construct an interference graph. Then, we calculate the interference between any two users and use the postprocessing discrete Fourier transform (DFT) filtering process to effectively distinguish the users with nonoverlapping AoAs. Finally, according to the interference graph, the GC-LI algorithm is proposed to mitigate the intercell interference (ICI) between users with the same pilot by assigning different pilots to connected users with high ICI metrics based on some regulation. Simulation results show that the GC-LI algorithm is suitable for various types of cells. In addition, compared with the existing pilot assignment algorithms based on graph coloring, users’ average signal-to-interference-plus-noise ratio (SINR) and uplink achievable sum rate (ASR) are significantly improved.

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Mitigating the Interference Caused by Pilot Contamination in Multi-cell Massive Multiple Input Multiple Output Systems Using Low Density Parity Check Codes in Uplink Scenario

Traitement du signal ◽

10.18280/ts.370619 ◽

2020 ◽

Vol 37 (6) ◽

pp. 1061-1074

Author(s):

Lokesh Bhardwaj ◽

Ritesh Kumar Mishra

Keyword(s):

Mimo System ◽

Multiple Input Multiple Output ◽

Base Station ◽

Low Density ◽

Pilot Contamination ◽

Parity Check ◽

Low Density Parity Check ◽

Multiple Input ◽

Input Multiple Output ◽

Sum Rate

The effects of pilot contamination (PC) on the performance of multi-cell multi-user massive multiple input multiple output (MC-MU-m-MIMO) system in uplink has been analyzed in this article. In a multi-cell scenario, the channel estimation (CE) at the desired cell using pilot reuse to avoid significant overhead results in poor CE due to PC. The improvement in degraded performance due to the effect of PC has been shown using low Density Parity Check (LDPC) codes. The comparative analysis of performance in terms of variation in bit error rate (BER) with the signal to noise ratio (SNR) for LDPC coded and uncoded information blocks of users has been shown when the number of cells sharing the same frequency band is varied. Further, the expression for sum-rate has been derived and its variation with the number of base station (BS) antennas has also been shown. The simulated results have shown that the LDPC coded scheme performs better than the uncoded counterpart and the sum-rate capacity increases when the strength of channel coefficients between the BS antennas of the desired cell and the users of remaining cells is less.

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Sum-Rate of Multi-User MIMO Systems with Multi-Cell Pilot Contamination in Correlated Rayleigh Fading Channel

Entropy ◽

10.3390/e21060573 ◽

2019 ◽

Vol 21 (6) ◽

pp. 573 ◽

Cited By ~ 3

Author(s):

Menghan Wang ◽

Dongming Wang

Keyword(s):

Rayleigh Fading ◽

Gaussian Approximation ◽

Mimo Systems ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Moment Generating Function ◽

Base Station ◽

Pilot Contamination ◽

Input Multiple Output ◽

Sum Rate

This paper presents some exact results on the sum-rate of multi-user multiple-input multiple-output (MU-MIMO) systems subject to multi-cell pilot contamination under correlated Rayleigh fading. With multi-cell multi-user channel estimator, we give the lower bound of the sum-rate. We derive the moment generating function (MGF) of the sum-rate and then obtain the closed-form approximations of the mean and variance of the sum-rate. Then, with Gaussian approximation, we study the outage performance of the sum-rate. Furthermore, considering the number of antennas at base station becomes infinite, we investigate the asymptotic performance of the sum-rate. Theoretical results show that compared to MU-MIMO system with perfect channel estimation and no pilot contamination, the variance of the sum-rate of the considered system decreases very quickly as the number of antennas increases.

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Deep Learning Based Antenna Selection for MIMO SDR System

Sensors ◽

10.3390/s20236987 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6987

Author(s):

Shida Zhong ◽

Haogang Feng ◽

Peichang Zhang ◽

Jiajun Xu ◽

Huancong Luo ◽

...

Keyword(s):

Decision Making ◽

Deep Learning ◽

Software Defined Radio ◽

Antenna Selection ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Base Station ◽

Input Multiple Output ◽

Set Up ◽

Time Division

In this paper, we propose and implement a novel framework of deep learning based antenna selection (DLBAS)-aided multiple-input–multiple-output (MIMO) software defined radio (SDR) system. The system is constructed with the following three steps: (1) a MIMO SDR communication platform is first constructed, which is capable of achieving uplink communication from users to the base station via time division duplex (TDD); (2) we use the deep neural network (DNN) from our previous work to construct a deep learning decision server to assist the MIMO SDR platform for making intelligent decision for antenna selection, which transforms the optimization-driven decision making method into a data-driven decision making method; and (3) we set up the deep learning decision server as a multithreading server to improve the resource utilization ratio. To evaluate the performance of the DLBAS-aided MIMO SDR system, a norm-based antenna selection (NBAS) scheme is selected for comparison. The results show that the proposed DLBAS scheme performed equally to the NBAS scheme in real-time and out-performed the MIMO system without AS with up to 53% improvement on average channel capacity gain.

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