Performance Of Massive MIMO Systems Research Articles

Simulation of Massive MIMO Antennas for Improved Link Throughput

Massive MIMO technology involves deploying multiple antennas on both ends of a communication link, which boosts spatial diversity and signal-to-noise ratio. By using multi-user MIMO transmission and techniques like spatial multiplexing and beamforming, it increases capacity, spectral efficiency, and data rates. Simulations conducted with tools like Octave software help engineers to optimize designs, predict system behavior, and assess the impact of antenna quantity on downlink throughput. This study addresses the increasing demand for high downlink data rates within modern communication networks. It explores the performance of Massive MIMO systems through simulations using Octave software, focusing on the quantity of antenna elements (M), and considering both spectral efficiency (SE) and data throughput. Results show higher M enhances both spectral efficiency and data rates. Efficiency dips at M=10, surges at M=40, and slowly, but steadily grows from M=50 to 100. Similar trends in data rates indicate M's impact on transmission performance. SE values range from 5.8442 bps/Hz (M = 10) to 24.1608 bps/Hz (M = 100), while data rates range from 116.8834 Mbps (M = 10) to 483.2158 Mbps (M = 100). These insights offer practical guidance for engineers and could facilitate the optimization of wireless communication setups and addressing the imperative for improved data rates. The study advances the understanding of Massive MIMO's potential, providing a roadmap for effectively leveraging its capabilities across diverse communication network scenarios.

Enhanced resource allocation strategies to improve the spectral efficiency in massive MIMO systems

The accuracy of the channel state information is important for correct channel estimation. However, when conducting channel estimation, more resources are allocated to pilots for estimation compared to data transmission. Furthermore, when the number of users increases, the number of pilots for estimation increases. Subsequently, there is an increase in the transmission overhead and hence reduces the spectral efficiency. Therefore, the advantage of obtaining channel state information is significantly reduced. To improve the performance of massive MIMO systems, the study analyses the tradeoff between the number of resources required to correctly estimate the channel using pilots to avoid interference while maintaining optimum spectral efficiency in massive MIMO antennas. Therefore, this study proposes an algorithm to address the challenge of optimum resource allocation in a massive MIMO. Pilot Frequency reuse, max–min fairness algorithm, and Zadoff–Chu sequences were adopted to achieve optimal allocation of resources and reduce interference for users in different cells using the same frequencies. The results reveal improved performance in terms of spectral efficiency with the adoption of the resource optimization approach. The study contributes to the performance improvement of massive MIMO antennas for 5 G communications.

Optimization of resource allocation in FDD massive MIMO systems

The performance of massive MIMO systems relies heavily on the availability of Channel State Information at the Transmitter (CSIT). A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CSI feedback stage. In fact, the downlink communication generally includes three stages, i.e., pilot training, CSI feedback, and data transmission. These three stages are mutually related and jointly determine the overall system performance. Unfortunately, there exist few studies on the reduction of CSIT acquisition overhead from the global point of view. In this paper, we integrate the Minimum Mean Square Error (MMSE) channel estimation, Random Vector Quantization (RVQ) based limited feedback and Maximal Ratio Combining (MRC) precoding into a unified framework for investigating the resource allocation problem. In particular, we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio (SNR) based on the deterministic equivalence theory. Then the three performance metrics (the spectral efficiency, energy efficiency, and total energy consumption) oriented problems are formulated analytically. With practical system requirements, these three metrics can be collaboratively optimized. Finally, we propose an optimization solver to derive the optimal partition of channel coherence time. Experiment results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.

Evaluation of different quantization resolution levels on the BER performance of massive MIMO systems under different operating scenarios

The massive MIMO system is one of the main technologies in the fifth generation (5G) of telecommunication systems, also recognized as a highly large-scale system. Constantly in massive MIMO systems, the base station (BS) is provided with a large number of antennas, and this large number of antennas need high-quantization resolution levels analog-to-digital converters (ADCs). In this situation, there will be more power consumption and hardware costs. This paper presents the simulation performance of a suggested method to investigate and analyze the effects of different quantization resolution levels of ADCs on the bit error rate (BER) performance of massive MIMO system under different operating scenarios using MATLAB software. The results show that the SNR exceeds 12 dB accounts for only 0.001% of BER signals when the number of antennas 60 with low quantization a 2 bits’ levels ADCs, approximately. But when the antenna number rises to 300, the SNR exceeds 12 dB accounts for almost 0.01% of BER transmitted signals. Comparably with the BER performance of high quantization, 4 bits-quantization resolution levels ADCs with the same different antennas have a slight degradation. Therefore, the number of antennas is a very important influence factor.

The Role of Correlation in the Performance of Massive MIMO Systems

Massive multiple-input multiple-output (m-MIMO) is considered as an essential technique to meet the high data rate requirements of future sixth generation (6G) wireless communications networks. The vast majority of m-MIMO research has assumed that the channels are uncorrelated. However, this assumption seems highly idealistic. Therefore, this study investigates the m-MIMO performance when the channels are correlated and the base station employs different antenna array topologies, namely the uniform linear array (ULA) and uniform rectangular array (URA). In addition, this study develops analyses of the mean square error (MSE) and the regularized zero-forcing (RZF) precoder under imperfect channel state information (CSI) and a realistic physical channel model. To this end, the MSE minimization and the spectral efficiency (SE) maximization are investigated. The results show that the SE is significantly degraded using the URA topology even when the RZF precoder is used. This is because the level of interference is significantly increased in the highly correlated channels even though the MSE is considerably minimized. This implies that using a URA topology with relatively high channel correlations would not be beneficial to the SE unless an interference management scheme is exploited.

1-Bit Massive MIMO Transmission: Embracing Interference with Symbol-Level Precoding

The deployment of large-scale antenna arrays for cellular base stations (BSs), termed as `Massive MIMO', has been a key enabler for meeting the ever-increasing capacity requirement for 5G communication systems and beyond. Despite their promising performance, fully-digital massive MIMO systems require a vast amount of hardware components including radio frequency chains, power amplifiers, digital-to-analog converters (DACs), etc., resulting in a huge increase in terms of the total power consumption and hardware costs for cellular BSs. Towards both spectrally-efficient and energy-efficient massive MIMO deployment, a number of hardware limited architectures have been proposed, including hybrid analog-digital structures, constant-envelope transmission, and use of low-resolution DACs. In this paper, we overview the recent interest in improving the error-rate performance of massive MIMO systems deployed with 1-bit DACs through precoding at the symbol level. This line of research goes beyond traditional interference suppression or cancellation techniques by managing interference on a symbol-by-symbol basis. This provides unique opportunities for interference-aware precoding tailored for practical massive MIMO systems. Firstly, we characterize constructive interference (CI) and elaborate on how CI can benefit the 1-bit signal design by exploiting the traditionally undesired multi-user interference as well as the interference from imperfect hardware components. Subsequently, we overview several solutions for 1-bit signal design to illustrate the gains achievable by exploiting CI. Finally, we identify some challenges and future research directions for 1-bit massive MIMO systems that are yet to be explored.

Performance Enhancement of Massive MIMO Using Deep Learning-Based Channel Estimation

Massive Multiple-Input Multiple-Output (massive MIMO) system relies on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex (FDD) networks. However, transmission of massive MIMO system is subject to excessive feedback overhead. In this paper, we propose a Deep Learning (DL) approach-based channel estimation technique to enhance the performance of massive MIMO system. This technique is used to enhance recovery quality and improve trade-off between compression ratio (CR) and complexity of massive MIMO system. The proposed technique is based upon using the Channel State Information Network combined with gated recurrent unit (CsiNet-GRU). Moreover, the dropout method is used in the proposed technique to reduce overfitting during the learning process. The simulation results demonstrate that the proposed CsiNet-GRU technique results in a significant improvement in performance when compared with existing techniques used in conjunction with massive MIMO systems.

Performance of Massive MIMO Systems under the Effect of Fading Channel

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The harmful influence of employing a large number of pilot-resources on the performance of massive MIMO systems

One of the severe constraints that limit the implementation of massive multi-input multi-output (M-MIMO) systems is called pilot contamination (PC). To overcome this problem, soft pilot reuse (SPR) strategy was proposed, which aims to properly allocate the Pilot Sequences (PS) to the users based on their properties e.g., Quality-of-Service. Despite its flaws, which can damage the spectral efficiency, the SPR strategy has been, heavily, employed as a decontaminating strategy. To reveal the weakness of this approach, this paper adopts a SPR based-strategy that separates the users within cells based on their large scale fading coefficients, therefore, because the edge-users (EUs) are mostly affected by the PC compared to the centre-users (CUs), a set of orthogonal pilots is allocated to the EUs of the overall cells, while the CUs of each cell are obliged to reuse the same set of orthogonal pilots as the CUs of adjacent cells.

Data Aided MSE-Optimal Time Varying Channel Tracking in Massive MIMO Systems

The temporal variations in the wireless propagation channel, referred to as channel aging, cause a mismatch between the estimated channel and the channel state at the time of data detection. This mismatch has been shown to severely impair the performance of massive MIMO systems. In this paper, we present data aided MSE-optimal channel tracking algorithms to decode the received symbols and update the channel estimates at the base station (BS) and the UEs. These algorithms combine ideas from Kalman filtering for channel tracking and deterministic equivalent analysis for symbol estimation. In the uplink case, we first develop a minimum mean squared error (MMSE) data estimator and the MSE-optimal channel predictor based on the Kalman filtering algorithm. We analytically show that the updated channel estimate obtained from the estimates of the data symbols leads to significantly larger signal to interference-plus-noise ratio (SINR), and hence achievable rate, compared to that obtained from the channel estimate based on pilot symbols. Following this, in the downlink case, we develop an algorithm to track the effective channel at the UEs and analyze its MSE, SINR and achievable rate performance. We show that tracking the effective downlink channel mitigates the effects of channel aging and leads to improved performance. However, since the beamforming matrices at the BS are not updated, downlink channel tracking is not as effective as uplink channel tracking. Finally, via Monte Carlo simulations, we validate our derived results, and demonstrate the gains achievable by tracking the time-varying channels in massive MIMO systems.

The harmful influence of employing a large number of pilot-resources on the performance of massive MIMO systems

The harmful influence of employing a large number of pilot-resources on the performance of massive MIMO systems

Assessment of Uplink Massive MIMO in Scattering Environment

This paper investigates the performance of massive MIMO systems under the effect of multipath propagation environment. Linear Minimum Mean Squared Error (MMSE) is considered to assess the performance of BPBK/OFDM based uplink massive MIMO transmission. Bit Error Rate (BER) and channel capacity in Non Line Of Site (NLOS) multipath fading environment are presented. The results show a correlation between the number of antennas and the performance of the system.

A Pilot Allocation Scheme Based on Joint User Classification and Coalition Game for Massive MIMO Three-Tier HetNets

Many researches demonstrate that pilot contamination is a major factor that affects the overall performance of massive MIMO systems. Based on this, this paper proposes a dynamic pilot allocation scheme based on joint user classification and coalition game (JUC-CG) to mitigate pilot contamination for Massive MIMO Three-tier heterogeneous networks (HetNets). It contains two steps: (1) According to the constraint in definition 2, the users are divided into two classes, namely A1 and A2. Users in pico-cell and sufferred from strong interference are classified into class A1, and remaining users of HetNets are classified into class A2. (2) The users of class A1 use a set of orthogonal pilots, and the users of class A2 reuse another set of orthogonal pilots by means of coalition game. In the coalition game for the users of class A2, users are divided into several disjoint user sub-coalitions, users belonging to different sub-coalitions are allocated different orthogonal pilot sequences, and users in the same sub-coalition reuse the same pilot sequence. The corresponding pilot allocation algorithm of JUC-CG is given. From the algorithm, we can see that JUC-CG scheme is flexible and can be used for scenarios that all users and base stations (BSs) are randomly distributed (such as multi-tier HetNets, ultra-dense HetNets and so on). In addition, the pilot allocation algorithm can obtain the overall optimal solution through cyclic searching. The simulation results demonstrate that the JUC-CG scheme can effectively reduce the average mean square error (MSE) of user signal detection and improve spectral efficiency (SE) in the uplink.

Enhancing uplink/downlink performance of massive MIMO system using time-shifted pilot signal transmission with pilot hopping

Enhancing uplink/downlink performance of massive MIMO system using time-shifted pilot signal transmission with pilot hopping

Modified Conjugate Gradient Algorithms for Gram Matrix Inversion of Massive MIMO Downlink Linear Precoding

This paper deals with various low complexity algorithms for higher order matrix inversion involved in massive MIMO system precoder design. The performance of massive MIMO systems is optimized by the process of precoding which is divided into linear and nonlinear. Nonlinear precoding techniques are most complex precoding techniques irrespective of its performance. Hence, linear precoding is generally preferred in which the complexity is mainly contributed by matrix inversion algorithm. To solve this issue, Krylov subspace algorithm such as Conjugate Gradient (CG) was considered to be the best choice of replacement for exact matrix inversions. But CG enforces a condition that the matrix needs to be Symmetric Positive Definite (SPD). If the matrix to be inverted is asymmetric then CG fails to converge. Hence in this paper, a novel approach for the low complexity inversion of asymmetric matrices is proposed by applying two different versions of CG algorithms- Conjugate Gradient Squared (CGS) and Bi-conjugate Gradient (Bi-CG). The convergence behavior and BER performance of these two algorithms are compared with the existing CG algorithm. The results show that these two algorithms outperform CG in terms of convergence speed and relative residue.

Time Shifted Pilot Signal Transmission With Pilot Hopping To Improve The Uplink Performance of Massive MIMO System For Next Generation Network

The paucity of pilot signals in Massive MIMO system is a vital issue. To accommodate substantial number of users, pilot signals are reused. This leads to interference, resulting in pilot contamination and degrades channel estimation at the Base Station (BS). Hence, mitigation of pilot contamination is exigency in Massive MIMO system. The proposed Time Shifted Pilot Signal Transmission with Pilot signal Hopping (TSPTPH), addresses the pilot contamination issue by transmitting pilot signals in non-overlapping time interval with hopping of pilot signals in each transmission slot. Hopping is carried by switching user to new a pilot signal in each transmission slot, resulting in random change of interfering users. This contributes to the change in channel coefficient, which leads to improved channel estimation at the BS and therefore enhances the efficiency of Massive MIMO system. In this system, Uplink Signal Power to Interference plus Noise Power Ratio (SINR) and data-rate are calculated for pilot signal reuse factor 1 and 3, by estimating the channel with Least Square estimation. The proposed system also reduces the uplink Signal power for data transmission of each User Equipment for normalized spectral efficiency with rising number of antennas at the BS and thus improves battery life.

Effect of Number of Base Station Antennas on the Performance of Massive MIMO System

Effect of Number of Base Station Antennas on the Performance of Massive MIMO System

Sum-Rate Performance of Massive MIMO Systems in Highly Scattering Channel with Semi-Orthogonal and Random User Selection

The sum-rate performance of a massive MIMO system is dependent on the adequate user and antenna selection techniques. It is best when all the selected users are mutually orthogonal (MOU) and antennas are selected based on maximum channel gain. In the case, when number of antennas in massive MIMO is approximately hundreds, the computational complexity cost in joint user and antenna selection is very high. Semi-orthogonal user selection (SUS) is preferred to reduce interference and complexity cost. In highly scattering Rayleigh fading channel random user selection (RUS) technique can be used because of small loss of system sum-rate compared toSUSandMOUtechniques. In this paper we propose a joint user and antenna selection algorithm where users are scheduled using semi-orthogonality measure and antenna selection is based on maximum channel gain. In addition, an algorithm for joint user and antenna selection is reported here with RUS for user selection and maximum channel gain for antenna selection. We explore the system sum-rate performance of a massive MIMO system using these algorithms for the case of a highly scattering Rayleigh fading channel. We consider various precoding techniques MMSE, ZFBF, and MRT and a range of SNR. We also explore the effect of spatial and multiuser diversity on system sum-rates using proposed algorithms. The sum-rate obtained by algorithms-1 is greater than the algorithm-2 about 1–3% but second one is much simpler.

Optimal Transmit Antenna Selection for Massive MIMO Wiretap Channels

In this paper, we study the impacts of transmit antenna selection on the secrecy performance of massive MIMO systems. We consider a wiretap setting in which a fixed number of transmit antennas are selected and then confidential messages are transmitted over them to a multi-antenna legitimate receiver while being overheard by a multi-antenna eavesdropper. For this setup, we derive an accurate approximation of the instantaneous secrecy rate. Using this approximation, it is shown that in some wiretap settings under antenna selection the growth in the number of active antennas enhances the secrecy performance of the system up to some optimal number and degrades it when this optimal number is surpassed. This observation demonstrates that antenna selection in some massive MIMO settings not only reduces the RF-complexity, but also enhances the secrecy performance. We then consider various scenarios and derive the optimal number of active antennas analytically using our large-system approximation. Numerical investigations show an accurate match between simulations and the analytic results.

Pilot Contamination Reduction Using Time-Domain Channel Sparsity in Massive MIMO-OFDM Systems

The pilot contamination problem in multi-cell multi-user massive MIMO systems is addressed in this paper. The overall performance of massive MIMO system is limited to the accuracy of the channel-state information (CSI) estimation at base station (BS). In time-division duplex systems, BS attains CSI using uplink-training sequence (TS) sent by users. The pilot contamination problem caused by using non-orthogonal TSs is the main challenge in this method. In this paper, a novel channel estimation method in the presence of pilot contamination is presented. The proposed method exploits sparse nature of the channel impulse response (CIR). Due to the pilot contamination and noise, the estimated CIR is not sparse anymore and consists of the desired and interference taps. To distinguish the desired taps from interferences, first, the initial estimation of the channel frequency response and equivalent CIR is achieved by taking EVD of received data covariance matrix. Finally, the initial estimated CIR will be modified using compressed-sensing methods. Simulation results indicate that the proposed approach achieves much better performance in terms of BER and sum-rate in comparison with the conventional methods.