Massive Massive Multiple-input And Multiple-output Systems Research Articles

Convolutional-Neural-Network-Based Detection Algorithm for Uplink Multiuser Massive MIMO Systems

In the coming 6th generation (6G) and beyond in wireless communication, an increasing number of ultrascale intelligent factors, including mobile robot users and smart cars, will result in interference exploitation. The management of this exploitation will be a great challenge for detection algorithms in uplink massive multiple-input and multiple-output (MIMO) systems, especially for high-order quadrature amplitude modulation (QAM) signals. Artificial intelligence technology employing machine learning is one of the key approaches among the 6G technical solutions. In this paper, a convolutional-neural-network-based likelihood ascent search (CNNLAS) detection algorithm is proposed on the basis of a graphical detection model for uplink multiuser massive MIMO systems. Compared with other algorithms, the proposed CNNLAS detection algorithm has a stronger robustness against the channel estimation errors, and requires lower average received signal-to-noise ratios to obtain better bit error rate performance and to achieve the theoretical spectral efficiency with a lower polynomial average per symbol computational complexity, both for the graphical low-order and high-order QAM signals in uplink multiuser massive MIMO systems.

Multi-Ring Time Shifted Pilots for Full-Duplex Massive MIMO Systems

The time shifted pilots (TSP) is a well-known countermeasure against the pilot contamination problem in the massive multiple input and multiple output (MIMO) systems. Recently, two performance enhancement schemes evolved from TSP were developed on top of the single- and two-ring cell structures. In this letter, we aim to answer the puzzle of how to decide the number of rings [denoted by ( ${r_{\max }}$ )] and arrange the time slots for pilot, downlink and uplink transmissions [denoted by the triple ( ${P}$ , ${D}$ , ${U}$ )] on top of the multi-ring (MR) cell structures. To this end, we develop a general MR-TSP scheme by dividing a cell into multiple rings and allocating non-overlapped time-slots to different rings for transmitting pilots. Moreover, similar to the conventional TSP scheme, the same time slots can still be shared with cells belonging to different groups. Based on the findings from the numerical results, we develop a principle for deciding ${r_{\max }}$ and ( ${P}$ , ${D}$ , ${U}$ ) to meet various system configurations.

Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

Power Allocation for an Energy-Efficient Massive MIMO System With Imperfect CSI

Massive multiple-input and multiple-output (MIMO) has been recognized as a core technology for the fifth generation networks due to its huge spectral utilization brought by multiplexing gain and array gain. Most previous works on Massive MIMO systems have focused on the power optimization to maximize the system energy efficiency, in which the ergodic achievable sum-rate is replaced with a lower bound closed-form expression, which can result in suboptimal performance. In this paper, we aim to optimize the power assignment to achieve the maximum energy efficiency for a downlink system of single-cell Massive MIMO based on a tight approximate expression for the achievable sum-rate. Considering two linear precoding strategies at base station, i.e., maximum ratio transmission and zero-forcing, we first derive the corresponding approximate closed-form of the achievable sum-rate under imperfect channel state information. Furthermore, an energy-efficient power allocation scheme is formulated as a non-convex optimization problem. An iterative power allocation algorithm that maximizes the system energy efficiency is developed based on the Lagrangian dual approach. Simulation results demonstrate the tightness of the proposed approximate closed-form expressions, and show that our designed algorithm yields much better energy efficiency performance over the existing algorithms.

Machine Learning-Based Dimension Optimization for Two-Stage Precoder in Massive MIMO Systems with Limited Feedback

A two-stage precoder is widely considered in frequency division duplex massive multiple-input and multiple-output (MIMO) systems to resolve the channel feedback overhead problem. In massive MIMO systems, users on a network can be divided into several user groups of similar spatial antenna correlations. Using the two-stage precoder, the outer precoder reduces the channel dimensions mitigating inter-group interferences at the first stage, while the inner precoder eliminates the smaller dimensions of intra-group interferences at the second stage. In this case, the dimension of effective channel reduced by outer precoder is important as it leverages the inter-group interference, the intra-group interference, and the performance loss from the quantized channel feedback. In this paper, we propose the machine learning framework to find the optimal dimensions reduced by the outer precoder that maximizes the average sum rate, where the original problem is an NP-hard problem. Our machine learning framework considers the deep neural network, where the inputs are channel statistics, and the outputs are the effective channel dimensions after outer precoding. The numerical result shows that our proposed machine learning-based dimension optimization achieves the average sum rate comparable to the optimal performance using brute-forcing searching, which is not feasible in practice.

Low Complexity Hybrid PSO-BB Detection Algorithm for Massive MIMO System

Objective: A massive Multiple Input and Multiple Output (MIMO) receiver utilizes the proposed detection algorithm to reduce the complexity. Methods: The existing research work namely Noise and Relevancy aware Low Complexity Detection (NRLCD) algorithm for massive MIMO receiver utilizes normalized cross correlation based pruning strategy to viably evacuate uncorrelated signals. However, the existing research work still has more complexity with increasingly number of iterations to find more relevant signal vector. In this research paper, it is proposed to investigate execution of massive MIMO system utilizing Continuous Phase Modulation (CPM) modulation which is used to carry out signal modulation. Then, Hybrid Particle Swarm Optimization-and-Branch-and-Bound (Hybrid PSO-BB) algorithm is proposed for low complexity detection. Findings: CPM demonstrated to give superior performance over Quadrature Amplitude Modulation (QAM) technique with the presence of phase noises. Hybrid PSO-BB is anticipated; in which the best attainable solution were found and renewed using PSO. The performance assessment of the proposed research work and existing methods is done under Adaptive Additive Gaussian Channel (AWGN) using MATLAB Communication tool box. Improvements: From the simulation results, it is inferred that the Hybrid PSO-BB algorithm is superior to the existing methods in-terms of Bit Error Rate (BER) performance and complexity. Keywords: Bit Error Rate (BER), Continuous Phase Modulation (CPM), Low Complexity, Massive Multiple Input and Multiple Output (MIMO), Particle Swarm Optimization (PSO)

Low‐overhead constant envelope precoding in multi‐cell massive MIMO systems with pilot contamination

The authors consider downlink transmission in a multi-cell massive multiple-input and multiple-output (MIMO) system and focus on decreasing the feedback overhead caused by cell cooperation in constant envelope precoding (CEP). In the literature, the single-cell case for CEP with perfect channel state information (CSI) has been studied. Here, considering full cooperation among the cells, they develop CEP for the multi-cell case. They devise a low overhead centralised construction for CEP which employs limited cooperation among the cells, providing higher system throughput. To achieve the minimum feedback overhead, a distributed realisation of CEP is proposed where each cell locally performs CEP. Furthermore, a new optimisation problem is solved to compensate for the effects of pilot contamination. In addition, to reduce the computational complexity, a relaxed iterative form is presented for the limited cooperation and the distributed scenarios. Numerical results show that in the proposed structures, despite a tremendous decrease in the system overhead, the performance confronts merely an insignificant degradation, <10%, compared to the full-cooperation case. Moreover, in the presence of imperfect CSI, the performance of the distributed structure whose imperfect CSI is compensated for, approaches the performance of this structure in the perfect CSI scenario.

MMSE detection method in uplink massive MIMO systems based on quantum computing

The minimum mean square error (MMSE) detection method involved matrix inversion operation with excessive computational burden. In this paper, we develop an improved quantum linear system algorithm to solve matrix inversion problem of the MMSE detection method in uplink massive multiple-input and multiple-output (MIMO) systems. In order to apply reasonably the robust computational power of quantum computing, we optimize the preparation of the input state and the extraction of the solution from the final entangled quantum state. We prove that this algorithm can reduce computational complexity to O(Nlog⁡N).

Physical-layer encryption in massive MIMO systems with spatial modulation

Massive multiple input and multiple output (MIMO) is a key technology of the fifth generation (5G) wireless communication systems, which brings various advantages, such as high spectral efficiency and energy efficiency. In MIMO system, spatial modulation (SM) has recently emerged as a new transmission method. In this paper, in order to improve the security in SM-MIMO, a physical layer encryption approach named chaotic antenna-index three-dimensional modulation and constellation points rotated (CATMCPR) encryption scheme is proposed, which utilizes the chaotic theory and spatial modulation techniques. The conventional physical-layer encryption in SM-MIMO suffers from spectral efficiency (SE) performance degradation and usually needs a preshared key, prior channel state information (CSI) or excess jamming power. By contrast, we show that the CATMCPR scheme can not only achieve securely communication but also improve above drawbacks. We evaluate the performances of the proposed scheme by an analysis and computer simulations.

A New View of Multi-User Hybrid Massive MIMO: Non-Orthogonal Angle Division Multiple Access

This paper presents a new view of multi-user (MU) hybrid massive multiple-input and multiple-output (MIMO) systems from array signal processing perspective. We first show that the instantaneous channel vectors corresponding to different users are asymptotically orthogonal if the angles of arrival (AOAs) of users are different. We then decompose the channel matrix into an angle domain basis matrix and a gain matrix. The former can be formulated by steering vectors and the latter has the same size as the number of RF chains, which perfectly matches the structure of hybrid precoding. A novel hybrid channel estimation is proposed by separately estimating the angle information and the gain matrix, which could significantly save the training overhead and substantially improve the channel estimation accuracy compared to the conventional beamspace approach. Moreover, with the aid of the angle domain matrix, the MU massive MIMO system can be viewed as a type of non-orthogonal angle division multiple access (ADMA) to simultaneously serve multiple users at the same frequency band. Finally, the performance of the proposed scheme is validated by computer simulation results.

A Low Complexity Data Detection Algorithm for Uplink Multiuser Massive MIMO Systems

A major challenge for uplink multiuser massive multiple-input and multiple-output (MIMO) systems is the data detection problem at the receiver due to the substantial increase in the dimensions of MIMO systems. The optimal maximum likelihood detector is impractical for such large wireless systems, because it suffers from exponential complexity in terms of the number of users. Therefore, suboptimal alternatives with reduced complexity, such as the linear minimum mean square error (LMMSE) detector, are necessary. However, the LMMSE detector still introduces high computational complexity, mainly caused by the computation of the Gram matrix and matrix inversion. To reduce the computational complexity of data detection while achieving satisfactory bit error rate (BER) performance, we initially proposed an iterative data detection algorithm that exploits the coordinate descent method (CDM)-based algorithmic framework for uplink multiuser massive MIMO systems. We then developed a reduced-complexity hardware implementation algorithm by leveraging the “one-at-a-time” update property of the CDM-based algorithmic framework. Simulation results revealed that the proposed CDM-based detector provides the same or improved BER performance than the classical LMMSE algorithm at a lower complexity for different test scenarios.

Channel Estimation and IQ Imbalance Compensation for Uplink Massive MIMO Systems With Low-Resolution ADCs

In this paper, two techniques to compensate inphase/quadrature-phase imbalance (IQI) are investigated in the uplink-quantized massive multiple-input and multiple-output (MIMO) systems for different models of randomized IQI parameters. One is referred to as combined-signal-based channel estimation and compensation (CCEC) and the other is denoted by effective channel estimation and compensation (ECEC). First, an independent automatic gain control (AGC) scheme is proposed to calibrate the dynamic range of both the I branch and the Q branch. By doing that, different quantization steps are used for analog-to-digital converters following the AGCs in these two branches at each receive antenna. Second, considering the impacts of both quantization and IQI, we give the details of channel estimation and IQI compensation for both the CCEC and the ECEC using bilinear generalized approximate message passing (Bi-GAMP). Moreover, to exploit the Bi-GAMP for ECEC reasonably, we theoretically derive the probability density function PDF of the elements in the effective channel for the case where only RX IQI is considered. Furthermore, we extend the ECEC to the case where both RX IQI and TX IQI are incorporated into the systems and derive the similar pdf as well. Finally, we use the numerical results to testify the validity of our theoretical analysis and the fact that the analytic PDF can be approximated by a Gaussian distribution when the IQI parameters are relatively small. Compared with other classical methods, the proposed methods can obtain better performance based on the Monte Carlo simulation results.

Analyzing Uplink SINR and Rate in Massive MIMO Systems Using Stochastic Geometry

This paper proposes a stochastic geometry framework to analyze the signal-to-noise-and-interference ratio (SINR) and rate performance in a large-scale uplink massive multiple-input and multiple-output (MIMO) network. Based on the model, expressions are derived for spatial average SINR distributions over user and base station distributions with maximum ratio combining (MRC) and zero-forcing (ZF) receivers. We show that, using massive MIMO, the uplink SINR in certain urban marcocell scenarios is limited by interference. In the interference-limited regime, the results reveal that for MRC receivers, a superlinear (polynomial) scaling law between the number of base station antennas and scheduled users per cell preserves the uplink signal-to-interference ratio (SIR) distribution, while a linear scaling applies to ZF receivers. ZF receivers are shown to outperform MRC receivers in the SIR coverage, and the performance gap is quantified in terms of the difference in the number of antennas to achieve the same SIR distribution. Numerical results verify the analysis. It is found that the optimal compensation fraction in fractional power control to optimize rate is generally different for MRC and ZF receivers. Besides, simulations show that the scaling results derived from the proposed framework apply to the networks, where base stations are distributed according to a hexagonal lattice.

Fixed-point Implementation of Approximate Message Passing (AMP) algorithm in massive MIMO systems

In massive multiple input and multiple output (MIMO) systems the challenge is the detection of the individual signals from the composite signal with a large system limit. The optimal detector becomes prohibitively complex. The approximate message passing (AMP) algorithm, designed for compressed sensing, has attracted researchers to counter this problem due to its reduced complexity with a large system limit. For this reason the AMP algorithm has been used for detection in massive MIMO systems. In this paper, we focus on implementing this algorithm in a fixed-point format. To obtain an implementation friendly architecture, we propose approximations for the mean and variance estimation functions within the algorithm. These estimation functions are obtained using the log-sum approximation, then taking the exponent of the result. The log-sum approximation is obtained by the Jacobian logarithm with a correction function. We also provide a modification of the correction function for the approximations that best suits our case. We then transform the algorithm with the approximated functions to fixed-point and provide a BER performance for the algorithm with the variables set to 16-bit word lengths using the hybrid “ScaledDouble” data types.

MF 기반 다중 사용자 Massive MIMO 시스템의 최적 기지국 안테나 수 및 사용자 수 분석

본 논문은 다중 사용자 (multiuser) 다중 안테나 (MIMO, multiple-input and multiple-output) 시스템을 기반으로 거대 안테나 시스템 (massive MIMO system)에 대한 성능 분석을 진행한다. 하향 링크 프레임 구조를 고려한 평균 셀 용량을 도출하고, 해당 평균 셀 용량을 기지국 안테나 수 및 사용자 수에 대하여 분석한다. 평균 셀 용량은 기지국 안테나 수 및 사용자 수에 대해 오목 함수 (concave function)이며 이러한 특징을 통해 최적의 기지국 안테나 수 및 사용자 수를 도출한다. 실험 결과를 통해 수식적으로 도출한 최적 안테나 수 및 사용자 수는 실험을 통한 최적 값과 일치함을 확인하였으며 도출한 최적 값을 통해 최대 값의 평균 셀 용량을 얻을 수 있음을 확인할 수 있다. In this paper, we analyze a performance of multiuser massive multiple-input and multiple-output (MIMO) system. We derive the ergodic cell capacity based on a downlink frame structure and analyze the ergodic cell capacity with respect to the number of base station (BS) antennas and the number of users. This paper shows that the ergodic cell capacity is a concave function with respect to the number of BS antennas and the number of users, and also derives the optimal numbers of BS antennas and users for the maximum cell capacity. The simulation results verify the derived analyses and show that the derived numbers of BS antennas and users provide the maximum cell capacity.