Massive Multiple-input Multiple-output Communication Systems Research Articles

A Novel 3-D Beam Domain Channel Model for Maritime Massive MIMO Communication Systems Using Uniform Circular Arrays

In this paper, we first propose a 3-dimensional (3-D) non-stationary geometry-based stochastic model (GBSM) for maritime massive multiple-input multiple-output (MIMO) communication systems with the uniform circular array (UCA) configuration. To reduce the model complexity and improve the mathematical tractability, a novel beam domain channel model (BDCM) is then proposed based on the transformation of the corresponding GBSM from the array domain to the beam domain for maritime communications. In the proposed BDCM, the beamforming matrices suitable for UCA structures are constructed and their invertibility is demonstrated to ensure the practicability of the BDCM. Two methods are used to characterize the array non-stationarity in maritime massive MIMO channels. First, the evolution of clusters over the large UCA is modeled by the visibility regions (VRs) attached to individual multipath components (MPCs). Second, the sphere wavefront (SWF) effect is captured by dividing the UCAs into several sub-arrays. Based on the proposed GBSM and BDCM, some important channel statistical properties are studied and compared, including channel power, power leakage, space-time-frequency correlation function (STF-CF), and root-mean-square (RMS) Doppler/beam spreads. Also, the importance of considering the array non-stationarity in maritime communication channels is revealed.

Adaptive Diversity Algorithm Based on Block STBC for Massive MIMO Link Misalignment in UWOC Systems

In the past few years, underwater wireless optical communication (UWOC) has become a promising wireless communication technology in the underwater environment. Aiming at the problem formulation of sub-channel correlation enhancement occurring due to the joint impact of underwater link misalignment and turbulence in the process of optical signal transmission in an underwater optical massive multiple-input multiple-output (MIMO) system, we propose an adaptive diversity approach depending on partition space time block code (STBC). STBC technology is used to reduce the random fading of optical signals caused by turbulence. At the same time, the channel correlation occurring due to channel misalignment is effectively alleviated by adaptive processing. The adaptive diversity algorithm based on segmented STBC effectively improves the reliability and decrease complexity of underwater optical Massive MIMO communication systems. It determines the particular link misalignment degree by the channel gain matrix obtained from the channel estimation and selects different combinations of detectors according to the degree of misalignment to obtain the maximum gain of the received signal combination. Compared with the chunking scheme, simulation and result shows that the adaptive diversity algorithm improves the tolerance of the system to the link misalignment error from 30 mm to 60 mm under the same condition number of channel gain matrix, and it can still demodulate the source signal directly without requiring detection algorithm in case of a large error in the link misalignment.

Downlink Spectral Efficiency of Massive MIMO Systems with Mutual Coupling

Massive multiple-input multiple-output (MIMO) is a profitable technique to greatly boost spectral efficiency, which has been embraced by the fifth-generation (5G) and sixth-generation (6G) mobile communication systems. By exploiting appropriate downlink precoding algorithms, base stations (BSs) equipped with a large number of antennas are able to provide service to multiple users as well as several cells at the same time and frequency. However, the mutual coupling effect due to the compact antenna array gives misleading results in massive MIMO communication systems. In this paper, we focus on the mutual coupling effect for massive MIMO systems with maximal ratio transmission (MRT), zero-forcing (ZF), regularize ZF (RZF), and minimum mean square error (MMSE) precoding to solve the mutual coupling problem. Additionally, we construct the closed-form expressions of the spectral efficiency (SE) to evaluate the effect of mutual coupling on system performance. Simulation results validate the effectiveness of the proposed mutual coupling effect assessment method and demonstrate the significant impacts of mutual coupling on massive MIMO system performance.

A Novel 3D Beam Domain Channel Model for Massive MIMO Communication Systems

Massive multiple-input multiple-output (MIMO) channels are distinctly characterized by their array non-stationarity, which has not been considered in the existing beam domain channel models (BDCMs). In this paper, the array non-stationarity of massive MIMO channels is modeled by the spatially consistent visibility regions (VRs) over a large uniform planar array (UPA) in terms of individual multipath components (MPCs). Based on this, a novel three-dimensional (3D) BDCM incorporating the effects of array non-stationarity is proposed. Statistical properties of the proposed BDCM including channel power, power leakage, space-time-frequency correlation function (STF-CF), and beam spread are derived. The ergodic and outage capacities are evaluated. The impacts of array non-stationarity on those statistics and channel capacity are analyzed. Results suggest that the beamwidths or spatial resolutions of the BDCM for different directions are not equal due to the array non-stationarity. This in turn increases the power leakage and correlation between channel elements and reduces the beam domain channel capacity.

Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement

SummaryThe major goal of millimeter wave (mmWave) multiple‐input multiple‐output (MIMO) systems is to get effective channel state information (CSI). Most of the recent works use nuclear norm theory for recovering the low‐rank scheme of channels. Some suboptimal solutions to the rank minimization problem can occur while addressing the nuclear norm‐based convex problem, which degrades the accuracy of channel estimation. Some works recover the channel with the assumption of the mmWave channel using an over‐complete dictionary. On the other hand, the accuracy of available CSI may openly influence the efficiency of mmWave communications. The main intention of this paper is to develop an enhanced channel estimation model with an optimized hybrid deep learning model. Here, the integration of deep neural network (DNN) and long short‐term memory (LSTM) form the hybrid deep learning model termed optimized D‐LSTM, which is modified by the opposition searched exploration‐based Harris hawks optimization (OE‐HHO). The input to the proposed hybrid deep learning is taken as the correlation among the received signal vectors and the measurement matrix for predicting the beam space channel amplitude. Finally, the successful channel estimation is observed by deep hybrid learning by the experimental outcomes, which also demonstrate that the proposed channel estimation model overwhelms the conventional models in terms of Normalized Mean‐Squared Error (NMSE) and spectral efficiency. The experimental results show that the designed OE‐HHO method obtains 9.2%, 8.9%, 8.65%, and 0.47% progressed than DA, DHOA, GWO, and HHO, respectively. Therefore, higher efficiency is observed by OE‐HHO based mmWave MIMO communication system.

Full-Duplex Massive Multiple-Input, Multiple-Output Architectures: Recent Advances, Applications, and Future Directions

The increasingly demanding objectives for next-generation wireless communications have spurred recent research activities on multiantenna transceiver hardware architectures and relevant intelligent communication schemes. Among them belong full-duplex (FD) multiple-input, multiple-output (MIMO) architectures, which offer the potential for simultaneous uplink (UL) and downlink (DL) operations in the entire frequency band. However, as the number of antenna elements increases, the interference signal leaking from the transmitter (Tx) of the FD radio to its receiver (Rx) becomes more severe. In this article, we present a unified FD massive MIMO architecture comprising analog and digital transmit and receive beamforming (BF) as well as analog and digital self-interference (SI) cancellation, which can be jointly optimized for various performance objectives and complexity requirements. The performance evaluation results for applications of the proposed architecture to fully digital and hybrid analog and digital-BF operations using recent algorithmic designs as well as simultaneous communication of data and control (SCDC) signals are presented. It is shown that the proposed architecture, for both small and large numbers of antennas, enables improved spectral-efficient FD communications with fewer analog-cancellation elements compared to various benchmark schemes. The article concludes with a list of open challenges and research directions for future FD massive MIMO communication systems and their promising applications.

Energy and spectral efficiency improvement using improved shark smell‐coyote optimization for massive MIMO system

SummaryMassive multiple‐input multiple‐output (MIMO) can considerably enhance the “spectral efficiency and energy efficiency” since it is a major technique for future wireless networks. Thus, the performance needs a huge count of base station antennas to serve a smaller number of terminals in conventional MIMO methodology. Large‐scale radio frequency (RF) chains represent the large‐scale antennas. There is a need of implementing an effective massive MIMO system for maximizing the efficient performance of the system with high “spectral efficiency and energy efficiency” owing to the high cost of RF chains, and the higher power consumption. In this paper, a massive MIMO communication system is implemented to satisfy the requirements regarding “energy efficiency and spectral efficiency.” Here, the number of base station antennas, the transmit power, and beam forming vectors are optimized to maximize “energy efficiency and spectral efficiency” when the channel capacity is known to be higher than some threshold values. The novelty of this work is a new hybrid optimization adaptive shark smell‐coyote optimization (ASS‐CO) algorithm is developed for improving energy efficiency. The optimization is done with the help of the hybrid optimization ASS‐CO Algorithm. The proposed ASS‐CO algorithm‐based massive MIMO communication system is evaluated by experimental analysis. From the result analysis, the maximum resource efficiency is observed by SS‐WOA, which is 6.6%, 50%, 6.6%, 6.6%, and 6.6% maximized than rider optimization algorithm (ROA), spotted hyena optimization (SHO), lion algorithm (LA), Shark Smell Optimization (SSO), and Coyote Optimization Algorithm (COA) by taking the count of base stations as 4. The superior performance enhancement regarding “spectral efficiency and energy efficiency” is accomplished over the traditional systems.

Multicarrier technique for 5G massive MIMO system based on CDMA and CMFB

Filterbank multicarrier (FBMC) is an effective method for modulation over massive multiple-input multiple-output (MIMO) channels in the future fifth generation wireless networks. This multicarrier technique provides high spectral efficiency, and it is resilient against synchronization errors compared to the well-known orthogonal frequency division multiplexing technique. This paper introduces a new technique, that combines the Code-division multiple-access (CDMA) and cosine modulated filter banks, a specific class of FBMC, to massive MIMO communication systems. In the proposed technique, the data symbols are transmitted on different sub-bands and also, they are spread in each sub-band by CDMA code. The proposed technique reduces the interference between data symbols, which consequently improves bit-error-rate (BER) performance. Simulation results show that the overall BER performance of the proposed multicarrier transmission technique is significantly enhanced. The results obtained show that as the number of base station antennas increases, different channel distortions are averaged out, thus BER decreases (in average by 10–2) for the same signal-to-noise ratio.

The state of the art in beyond 5G distributed massive multiple-input multiple-output communication system solutions

Beyond fifth generation (5G) communication systems aim towards data rates in the tera bits per second range, with improved and flexible coverage options, introducing many new technological challenges in the fields of network architecture, signal processing, and radio frequency front-ends. One option is to move towards cell-free, or distributed massive Multiple-Input Multiple-Output (MIMO) network architectures and highly integrated front-end solutions. This paper presents an outlook on beyond 5G distributed massive MIMO communication systems, the signal processing, characterisation and simulation challenges, and an overview of the state of the art in millimetre wave antennas and electronics.

Physical (PHY) Layer Analysis of Data Transmission in MIMO Wireless Networks in Line- of-Sight (LoS) Environments

This research paper presents the Physical (PHY) layer analysis of multiple input multiple output (MIMO) systems in line-of-sight environments between a user equipment (UE) and a base station when an information data is transmitted. The data transmission in wireless networks often takes place in non-line-of-sight in urban environments and Line-of-Sight (LoS) in rural environments depending on the bandwidth, power constraints. Using MIMO technology, the data transmission takes place with increased number of transmit and receive antennas which results in reduced signal to noise ratio (SNR) requirement. In this research work, simulation for PHY layer metric of probability of error, mean square error (MSE) for data transmission in MIMO wireless networks is obtained Matrix Laboratory (MATLAB) for LoS environment which follows Rician distribution. Probability of error reduces to a significant extent when the Rician factor increases as the line-of-sight component prevails which also takes lesser power in the lower SNR limits. Further, in MIMO systems also the probability of error and MSE reduces in concatenation with single antenna terminals thereby exploring power control a significant possibility for implementation in massive MIMO and millimeter wave communication systems for 5G and 6G systems.

Asymptotic characterisation of regularised zero‐forcing receiver for imperfect and correlated massive multiple‐input multiple‐output systems

In this work, the authors present an asymptotic high-dimensional analysis of the regularised zero-forcing receiver in terms of its mean-squared error (MSE) and bit error rate (BER) when used for the recovery of binary phase-shift keying (BPSK) modulated signals in a massive multiple-input multiple-output communication system. The asymptotic analysis is carried out when the system dimensions grow simultaneously large at a fixed ratio. The authors assume that the channel matrix is spatially correlated and not perfectly known. The authors use the linear minimum mean-squared error (LMMSE) method to estimate the channel matrix. The asymptotic approximations of the MSE and BER enable us to solve various practical optimisation problems. Under MSE/BER minimisation, the authors derive (1) the optimal regularisation factor for RZF; (2) the optimal power allocation scheme. The numerical simulations match the derived asymptotic expressions, even when the problem dimensions are small.

Dual CNN-Based Channel Estimation for MIMO-OFDM Systems

Recently, convolutional neural network (CNN)-based channel estimation (CE) for massive multiple-input multiple-output communication systems has achieved remarkable success. However, complexity even needs to be reduced, and robustness can even be improved. Meanwhile, existing methods do not accurately explain which channel features help the denoising of CNNs. In this paper, we first compare the strengths and weaknesses of CNN-based CE in different domains. When complexity is limited, the channel sparsity in the angle-delay domain improves denoising and robustness whereas large noise power and pilot contamination are handled well in the spatial-frequency domain. Thus, we develop a novel network, called dual CNN, to exploit the advantages in the two domains. Furthermore, we introduce an extra neural network, called HyperNet, which learns to detect scenario changes from the same input as the dual CNN. HyperNet updates several parameters adaptively and combines the existing dual CNNs to improve robustness. Experimental results show improved estimation performance for the time-varying scenarios. To further exploit the correlation in the time domain, a recurrent neural network framework is developed, and training strategies are provided to ensure robustness to the changing of temporal correlation. This design improves channel estimation performance but its complexity is still low.

A 3D Cluster-Based Channel Model for 5G and Beyond Vehicle-to-Vehicle Massive MIMO Channels

In this paper, we propose a three-dimensional (3D) cluster-based model for the fifth generation (5G)/beyond 5G (B5G) vehicle-to-vehicle (V2V) massive multiple-input multiple-output (MIMO) wideband channels. It is the first cluster-based irregular shaped geometry-based stochastic model (IS-GBSM) to distinguish the dynamic clusters and static clusters in vehicular massive MIMO communication scenarios. This model not only considers the high time-variance, the time non-stationarity, and the vehicular traffic density (VTD) of V2V channels, but also models the massive MIMO channel characteristics, such as spherical wavefronts by 3D vector calculation and space non-stationarity. Meanwhile, this model for the first time integrates the VTD into birth-death process to model the channel characteristics of V2V and massive MIMO jointly and deeply, where a novel VTD-combined time-array cluster evolution algorithm for 5G/B5G V2V massive MIMO channel model is developed. Based on the proposed model, we derive some expressions of channel statistical properties, including time-space-frequency correlation function (STF-CF) and Doppler power spectral density (DPSD). The influence of several parameters, e.g., VTDs, vehicle moving directions, and antenna spacings, on the channel characteristics are sufficiently explored, which can provide assistance for the design of vehicular massive MIMO communication systems. Finally, the utility of the proposed model is verified by the close agreement between simulation results and measurement data.

Historical Development of Spatial Modulation and Massive MIMO Communication System with Implementation Challenges: A Review

: For wireless communication networks, massive MIMO (Multiple-Input Multiple- Output) technology has been considered a major achievement. MIMO Technology consists of equipping a base station with a large number of antennas to serve many active users. Through MIMO Technology, the ability to concentrate on transmitted signal energy in very short spaces would result in huge increases in system capacity. While this new concept provides some fascinating benefits, it creates a new tension that has drawn both academia and industry attention. Channel state information acquisition, channel feedback, instant reciprocity, statistical reciprocity, architecture and hardware impairments are few of them. This paper presents a survey on Spatially Modulated Massive MIMO's historical development.

Principle of Computation Power Optimization in Millimeter Wave Massive MIMO Systems

The computation power of baseband units (BBUs) is a major source of power consumption in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with a large number of users due to complex signal processing. The effective reduction of computation power is critical for improving system energy efficiency. In this paper, the principle of reducing the computation power of BBUs is first investigated in mmWave massive MIMO systems with a hybrid precoding structure. A recursive constraint in decomposing the baseband precoding matrix is derived for reducing the computation power of hybrid precoding systems. Furthermore, the optimal number of sub-matrices minimizing the maximum error in decomposing the baseband precoding matrix is obtained. Based on the proposed principle, consisting of the recursive constraint and the optimal number of sub-matrices, a fast Monte Carlo baseband precoding (FMCBP) algorithm is developed to reduce the computation power of BBUs and improve system energy efficiency. Simulation results show that the total transmission rate and energy efficiency of mmWave systems are coupled with the computation power of BBUs, based on the FMCBP algorithm. Moreover, the FMCBP algorithm maximally improves the energy efficiency of multi-user mmWave massive MIMO communication systems by 124 percent, compared with the conventional equivalent zero-forcing algorithm.

A Compressive Sensing Approach for Federated Learning Over Massive MIMO Communication Systems

Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices, each with its own local training data set. In this paper, we present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems in which the central server equipped with a massive antenna array communicates with the wireless devices. One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices. To overcome this challenge, we first establish a transmission strategy to construct sparse transmitted signals from the local gradient vectors at the devices. We then propose a compressive sensing algorithm enabling the server to iteratively find the linear minimum-mean-square-error (LMMSE) estimate of the transmitted signal by exploiting its sparsity. We also derive an analytical threshold for the residual error at each iteration, to design the stopping criterion of the proposed algorithm. We show that for a sparse transmitted signal, the proposed algorithm requires less computationally complexity than LMMSE. Simulation results demonstrate that the presented approach outperforms conventional linear beamforming approaches and reduces the performance gap between federated learning and centralized learning with perfect reconstruction.

Low-complexity soft-output signal detector for massive MIMO with higher order QAM constellations

As the number of base station (BS) antennas in massive multiple-input multiple-output (MIMO) systems is dramatically increased, linear detection methods are able to achieve the near optimal performance at the expense of performing the complicated matrix inversion of a high dimensional matrix, which makes hardware implementation infeasible. Meanwhile, the straightforward application of the hard symbol-to-bit decision algorithm yields a very poor performance for the higher order quadrature amplitude modulation (QAM) that is widely adopted as an efficient tool to accommodate high demand of data throughput in modern wireless systems. In this work, we propose a low-complexity soft-output signal detector for multiuser massive MIMO communication systems employing Gray-coded higher order QAM with square constellations. In the proposed detector, the two-dimensional double successive projection (2D-DSP) algorithm is utilized to iteratively realize zero-forcing (ZF) algorithm for multiuser signal recovery, which circumvents the matrix inverse operation. Moreover, a simplified implementation of Max-Log-MAP algorithm is developed to calculate log likelihood ratios (LLRs) for soft-input channel decoding by fully exploiting the bit-flipping property of Gray-coded modulation scheme along with the utilization of the channel hardening property of the massive MIMO systems. Numerical results show that the proposed soft-output detector provides a relatively good tradeoff between the complexity and performance compared with several existing detectors, and approaches the performance of the ZF algorithm with only 3 iterations.

A generalised antenna correlation model for accurate performance evaluation of 2D massive MIMO communication systems

ABSTRACT Massive multiple-input multiple-output (MIMO) is considered as a key component of the new fifth-generation (5 G) communication systems and beyond to meet the rapid explosion of global wireless data traffic. However, antenna correlation is well known to have a direct impact on the capacity of practical MIMO schemes. Therefore, research and industrial communities are still looking for novel techniques to employ a large number of antennas with minimum correlation for compact mobile devices and base stations. In this paper, a realistic and generalised correlation matrix (GCM) model is developed for the accurate performance evaluation of two-dimensional (2D) massive MIMO systems. Based on the proposed GCM model, a closed-form expression of the channel capacity is derived for spatially correlated Rayleigh-fading MIMO environment to assess the acceptable range of antenna correlation. Over different 2D antenna configurations, simulation results validate the accuracy of derived capacity expression and demonstrate that the proposed GCM model tends to provide a more realistic performance compared with the existing methods. This may help to implement a large number of antennas in a constrained-size ground plane with an efficient tradeoff between 2D array designs and required system performance.

A Novelty Massive MIMO Channel Model Based on the Birth‐and‐Death Process of Spatial Multipath Cluster

AbstractIn this paper a novelty Massive Multiple Input Multiple Output (MIMO) channel model based on the birth‐and‐death process of multipath clusters is proposed. First, Massive MIMO measurements are conducted in typical outdoSor scenarios with the bandwidth of 100 MHz at frequencies of 1.4725 and 6 GHz, respectively, following with the investigation of radio propagation characteristics over antenna array. Then we research on the subchannel correlation and nonstationary based on the channel sounding experiments. Furthermore, we build up a Markov chain to characterize the birth‐and‐death process of the multipath clusters over the array, which is helpful to bring deeply insight into the cluster evolution over the antenna array axis. And the analysis results are consistent with the theory of the cluster's visible region. At last, the paper systematically demonstrates how does the spatial correlation influence the capacity of the communication system. The whole research work aims to contribute the radio channel modeling of the fifth‐generation (5G) Massive MIMO communication system.

An Approach for Data Rate Maximization and Interference Mitigation in Massive MIMO Communication Systems Using SRPWGC-PD Algorithm

Increasing usage of wireless communication systems and demand for higher data speed is appealing newer generations of cellular communication systems to provide higher data rate along with a reliable system, i.e. system which is free from interference. 5G Communication systems are believed to provide at least ten times betterment in area throughput, i.e. data rate by increasing the spectral efficiency. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses massive antenna arrays to provide a very high beamforming gain and spatial multiplexing of users and hence increases the spectral and energy efficiency. However, although Massive MIMO suppresses intra-cell interference and uncorrelated noise to a significant amount, it cannot mitigate the Pilot Contamination (PC) which is caused by reusing the same set of pilot signals in adjacent cells. In this paper, a new Pilot Decontamination (PD) technique, named as Softly Reused Pilot Weighted Graph Coloring-based Pilot Decontamination (SRPWGC-PD) has been introduced. In the proposed scheme, a scheme named soft pilot reusing is applied to separate the users in cells into two (inner and outer) zones, and then outer-zone users are allocated with orthogonal pilots; whereas a special pilot allocation scheme named weighted graph coloring algorithm is applied to the inner zone users to make generated PC as less as possible. Simulation and numerical analysis provide an insight that the proposed scheme outperforms in most of the performance indices, when compared to the existing schemes reported in the literature.