Massive Multiple-input And Multiple-output Systems Research Articles

Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach

Hybrid beamforming (HB) has emerged as a promising technology to support ultra high transmission capacity and with low complexity for Millimeter Wave (mmWave) multiple-input and multiple-output (MIMO) system. However, the design of digital and analog beamformer is a challenge task with non-convex optimization, especially for the multi-user scenario. Recently, the blooming of deep learning research provides a new vision for the signal processing of communication system. In this work, we propose a deep neural network based HB for the multi-User mmWave massive MIMO system, referred as DNHB. The HB system is formulated as an autoencoder neural network, which is trained in a style of end-to-end self-supervised learning. With the strong representation capability of deep neural network, the proposed DNHB exhibits superior performance than the traditional linear processing methods. According to the simulation results, DNHB outperforms about 2 dB in terms of bit error rate (BER) performance compared with existing methods.

Channel Estimation in Massive MIMO Systems Based on Generalized Block Adaptive Matching Pursuit Algorithm

Frequency division duplex (FDD) downlink massive multiple-input and multiple-output (MIMO) systems encounter difficulty in acquiring the sparsity and threshold parameter. As a remedy, a time-frequency block sparse channel estimation method based on compressed sensing (CS) is proposed that consists of the generalized block adaptive matching pursuit (gBAMP) algorithm. By using the time-frequency joint block sparsity of massive MIMO systems, the index sets selected in the iterative process of the algorithm are further optimized to improve the stability of the algorithm. Then, in the absence of the threshold parameter, the adaptive iterative stop condition is determined using the residual based on the Frobenius norm (F-norm), and the effectiveness of the method is proved. Simulations show that the algorithm can quickly and accurately estimate the massive MIMO channel information and outperforms its counterparts.

A Generalized VAMP-Based Channel Estimator for Uplink Quantized Massive MIMO Systems

In this study, we propose an improved channel estimator based on vector approximate message passing (VAMP) for quantized millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) systems. Using channel sparsity and the orthogonality of steering matrix, the proposed VAMP-on method designed for the on-grid scenario not only improves estimation performance but also has better convergence and inverse-free implementation. Furthermore, to achieve better estimation performance under the off-grid scenario, we provide a VAMP-off algorithm to update steering matrix and channel coefficients in an alternating fashion. Finally, simulation results are provided to demonstrate the advantages of the proposed approaches.

A 3D Wideband Two-Cluster Channel Model for Massive MIMO Vehicle-to-Vehicle Communications in Semi-Ellipsoid Environments

This paper presents a three dimensional (3D) wideband two-cluster channel model for massive multiple-input and multiple-output (MIMO) vehicle-to-vehicle (V2V) communications. In the model, we introduce multiple confocal semi-ellipsoid models to depict the distribution of clusters in roadside environments; therefore, the statistical properties for different propagation delays can be derived and investigated. Furthermore, we adopt the birth-death process to simulate the dynamic characteristics of clusters on both the array and time axes, which efficiently characterize the non-stationarity of massive MIMO communication systems. Some comparisons of the spatial cross-correlation functions (CCFs) of propagation links via the two-cluster model with and without the cluster evolution for different taps are made. In addition, we study the temporal auto-correlation functions (ACFs) of the propagation links with respect to the different moving time of the mobile transmitter (MT) and mobile receiver (MR). Simulation results of the statistical channel characteristics nicely match those of the theoretical results, which demonstrate that our model is suitable for describing real massive MIMO V2V communication environments.

Analysis of Massive MIMO Performance in an Indoor Picocell With High Number of Users

This paper presents an analysis of the massive multiple input and multiple output (MIMO) channel in an indoor picocell with a high number of active user terminals and a base station consisting of a virtual array with up to one hundred elements. The analysis is based on the results of a measurement campaign carried out in the 3.2 to 4 GHz band in a scenario of reduced size and with a symmetrical geometry, in which users are also placed in an orderly manner. The channel meets the condition of favorable propagation depending on several factors, one of the most important being the spatial distribution of users. Results concerning the inverse condition number as well as the channel sum capacity are included. Another factor that determines the performance of massive MIMO systems when operated in an orthogonal frequency division multiplexing (OFDM) framework is the frequency selectivity of the channel that limits the size of the coherence block (ChB). Focusing on the most significant results achieved, it can be concluded that the channel reaches a capacity of 89% with respect to an i.i.d. Rayleigh channel. Concerning the cumulative distribution function (CDF) of the sum capacity, it can also be observed that the tails are not very pronounced, which indicates that a homogeneous service can be given to all users. Regarding the number of samples that make up the ChB, although it is high in all cases (of the order of tens of thousands), it strongly depends on the degree of correlation used to calculate the coherence bandwidth.

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.

Performance analysis of achievable sum‐rate for downlink massive MIMO systems

SummaryMassive multiple‐input and multiple‐output (MIMO) has been recognized as a promising technology in the fifth‐generation wireless networks. Under perfect channel state information, we derive three tractable closed‐form expressions that corresponding to the lower bound, approximation, and upper bound on the achievable rate in a massive MIMO downlink system with maximum‐ratio transmission precoding. Based on the proposed closed‐form expressions, the power efficiency of the system is investigated as the number of transmit antennas increases. Simulation results demonstrate the tightness of our proposed closed‐form expressions for the achievable sum‐rate.

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.

Compressive Sensing Assisted Generalized Quadrature Spatial Modulation for Massive MIMO Systems

A novel multiple-input and multiple-output (MIMO) transmission scheme termed as generalized quadrature spatial modulation (G-QSM) is proposed. It amalgamates the concept of quadrature spatial modulation (QSM) and spatial multiplexing for the sake of achieving a high throughput, despite relying on a low number of radio frequency (RF) chains. In the proposed G-QSM scheme, the conventional constellation points of the spatial multiplexing structure are replaced by the QSM symbols, hence the information bits are conveyed both by the antenna indices as well as by the classic amplitude/phase modulated (APM) constellation points. The upper bounds of the average bit error probability (ABEP) of the proposed G-QSM system in high throughput massive MIMO configurations are derived. Furthermore, an efficient multipath orthogonal matching pursuit (EM-OMP)-based compressive sensing (CS) detector is developed for our proposed G-QSM system. Both our analytical and simulation results demonstrated that the proposed scheme is capable of providing considerable performance gains over the existing schemes in massive MIMO configurations.

On the Low-Complexity, Hardware-Friendly Tridiagonal Matrix Inversion for Correlated Massive MIMO Systems

In massive multiple-input and multiple-output (M-MIMO) systems, one of the key challenges in the implementation is the large-scale matrix inversion operation, as widely used in channel estimation, equalization, detection, and decoding procedures. Traditionally, to handle this complexity issue, several low-complexity matrix inversion approximation methods have been proposed, including the classic Cholesky decomposition and the Neumann series expansion (NSE). However, the conventional approaches failed to exploit neither the special structure of channel matrices nor the critical issues in the hardware implementation, which results in poorer throughput performance and longer processing delay. In this paper, by targeting at the correlated M-MIMO systems, we propose a modified NSE based on tridiagonal matrix inversion approximation (TMA) to accommodate the complexity as well as the performance issue in the conventional hardware implementation, and analyze the corresponding approximation errors. Meanwhile, we investigate the very-large-scale integration implementation for the proposed detection algorithm based on a Xilinx Virtex-7 XC7VX690T FPGA platform. It is shown that for correlated massive MIMO systems, it can achieve near minimum mean square error performance and 630 Mb/s throughput. Compared with other benchmark systems, the proposed pipelined TMA detector can get high throughput-to-hardware ratio. Finally, we also propose a fast iteration structure for further research.

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)

Exploiting Dynamic Sparsity for Downlink FDD-Massive MIMO Channel Tracking

Accurate channel tracking with a small pilot overhead is vital for real-time massive multiple-input and multiple-output (MIMO) communication over a dynamic channel. Recently, compressive sensing has been applied to reduce the pilot overheads by exploiting the spatial and/or temporal correlation of massive MIMO channels. However, most existing channel estimation and tracking algorithms are based on oversimplified channel models with restrictive assumptions, and thus perform poorly under realistic channels. In this paper, we consider downlink frequency-division duplexing-massive MIMO system operating with limited scattering around the base station and flat fading channel is considered. We propose a two-dimensional Markov Model (2D-MM) to capture the 2-D dynamic sparsity (i.e., structured sparsity in the spatial domain and probabilistic temporal dependency of channel in the temporal domain) of massive MIMO channels. The 2D-MM has the flexibility to model different propagation environments in practice. We derive an effective message passing algorithm called dynamic turbo orthogonal approximate message passing (D-TOAMP) to recursively track a dynamic massive MIMO channel with a 2D-MM prior. The proposed D-TOAMP algorithm does not require knowledge of the 2D-MM channel parameters, which could be automatically learned through the expectation maximization framework. Extensive simulations show that the proposed D-TOAMP can achieve significant gains over the existing algorithms under realistic channels.

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).

Asymptotic Orthogonal Space Shift Keying in Massive MIMO Systems

We investigate space shift keying, which is a simple form of spatial modulation. The bit error rate performance of line-of-sight (LOS) multiple-input and multiple-output (MIMO) was obtained in the literature with orthogonal subchannels for indoor millimeter-wave (mmWave) communications. It is essential to impose a rigid restriction on the distance between the transmitter and the receiver and antenna spacing to maintain orthogonal subchannels. However, in wireless communications, it is not viable to meet the strict distance requirements since user equipment is portable. We propose asymptotic orthogonal space shift keying with mobile stations. The asymptotic orthogonal space shift keying approaches orthogonal subchannels for a large number of transmitter and receiver antennas in correlated subchannels. This result is valuable for future wireless MIMO communications in mmWave frequencies. The massive MIMO renders the subchannel correlation symmetrical so that they can cancel each other. The example correlation model is employed for a numerical illustration. The proposed performance analysis is based on a large number of miniaturized antennas available in mmWave frequencies.

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.

Low Complexity Hybrid Precoding for Broadband mmWave Massive MIMO Systems

Hybrid precoding becomes a candidate for Millimeter wave (mmWave) massive MIMO (Multiple-Input and Multiple-Output) systems because it can extremely reduce power consumption and high c ...