Massive MIMO Research Articles

Semisupervised Representation Contrastive Learning for Massive MIMO Fingerprint Positioning

Semisupervised Representation Contrastive Learning for Massive MIMO Fingerprint Positioning

Massive MIMO Precoding and Beamforming Techniques for Future Generation Communication System

Massive MIMO, which was initially suggested, is a critical technique for meeting the rapidly growing data demand of 5G networks. MIMO technology may provide the propagation channel with a greater degree of flexibility in terms of multiplexing gain or diversity gain. New disruptive technologies have been developed in response to the large increase in data traffic that the forthcoming 5G mobile communication system. This is a critical research area for 5G wireless communication since massive MIMO has the possible system to significantly boost throughput and data transmission charges, as well as residual energy and energy efficiency. As a result of three years of fast development in massive MIMO technology, spectrum efficiency has been enhanced to an unprecedented level by the combination of growing antennas and adopting an extremely duplex communication mechanism. It is achieved by modeling the interference signal first and then constructing an exactly opposite signal added to a pre-distorted signal to cancel it. Additionally, this article discusses precoding methods and energy efficiency.

On the Performance of MMSE Channel Estimation in Massive MIMO Systems over Spatially Correlated Rician Fading Channels

Massive multiple-input-multiple-output (M-MIMO) offers remarkable advantages in terms of spectral, energy, and hardware efficiency for future wireless systems. However, its performance relies on the accuracy of channel state information (CSI) available at the transceivers. This makes channel estimation pivotal in the context of M-MIMO systems. Prior research has focused on evaluating channel estimation methods under the assumption of spatially uncorrelated fading channel models. In this study, we evaluate the performance of the minimum-mean-square-error (MMSE) estimator in terms of the normalized mean square error (NMSE) in the uplink of M-MIMO systems over spatially correlated Rician fading. The NMSE allows for easy comparison of different M-MIMO configurations, serving as a relative performance indicator. Besides, it is an advantageous metric due to its normalization, scale invariance, and consistent performance indication across diverse scenarios. In the system model, we assume imperfections in channel estimation and that the random angles in the correlation model follow a Gaussian distribution. For this scenario, we derive an accurate closed-form expression for calculating the NMSE, which is validated via Monte-Carlo simulations. Our numerical results reveal that as the Rician K-factor decreases, approaching Rayleigh fading conditions, the NMSE improves. Additionally, spatial correlation and a reduction in the antenna array interelement spacing lead to a reduction in NMSE, further enhancing the overall system performance.

Analysis of Performance Improvement of Planar Antenna Arrays With Optimized Square Rings for Massive MIMO Applications

The power pattern of the conventional fully optimized planar arrays can be properly reshaped according to the required user-defined constraints. However, the practical implementation of such fully optimized large planar arrays is complex and expensive. This paper introduces a new and efficient technique that is capable of providing better performance and almost the same power pattern shapes as that of the conventional fully optimized planar arrays by optimally adjusting the element amplitude and phase excitations of the outer-square rings instead of all elements' excitations. The proposed technique starts with a massive fully planar array then divides it into two contiguous sub-planar arrays which are both symmetric about the original array center. The elements excitation amplitudes or phases of the outer sub-planar array are only adjusted to form the desired power pattern shapes, while the amplitudes or phases of the central sub-planar array elements which have usually higher weights than the outer elements are made constants (i.e., they made ones for the case of amplitude-only control and zeros for the phase-only control). The results demonstrate the capability of the proposed planar array to form the required power patterns with far less number of the adjustable elements.

Machine Learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks

The following research focuses on the use of machine learning-based beamforming algorithms to improve Massive Multiple Input Multiple Output (MIMO) systems in 5G networks. Four unique algorithms namely, the Deep Learning Beamforming Algorithm (DLBA), Reinforcement Learning-Based Doa Estimation Algorithm (RLBEA), Clustering based beam forming algorithm(CBA) and GeneticAlgorithm Based Beam Forming Algoeithm were developed after which each of them was undertook evaluation. Widespread trials, in a simulated 5G environment, have revealed that the DLBA and RLBA considerably outperform other technologies by means of system throughput SINR as well Both the DLBA and RLBA achieved high system throughput, increased SINR levels and low BER. CBA and GABA, using clustering and genetic algorithms as their approaches, displayed moderate values on all assessed composite measures. This research offers important insights on the adaptability and learning potential of machine-learning based beamforming algorithms highlighting their ability to improve efficiency in wireless communication networks during the 5G revolution

Analysis of Outage Probability for Massive MIMO Systems Under Multi-Cell and Imperfect CSI Environments

Analysis of Outage Probability for Massive MIMO Systems Under Multi-Cell and Imperfect CSI Environments

Spatial Structure Matching-Based DoA Estimation and Tracking for Integrated Sensing and Communication Massive MIMO OFDM System

Spatial Structure Matching-Based DoA Estimation and Tracking for Integrated Sensing and Communication Massive MIMO OFDM System

Asymptotic SEP Analysis and Optimization of Linear-Quantized Precoding in Massive MIMO Systems

Asymptotic SEP Analysis and Optimization of Linear-Quantized Precoding in Massive MIMO Systems

Novel Beamforming Design for IRS-Assisted Cell-Free Massive MIMO Systems

Novel Beamforming Design for IRS-Assisted Cell-Free Massive MIMO Systems

Blind and Semi-blind Channel Estimation and Collision Resolution for the Uplink of MU-MIMO and Massive MIMO Systems for B5G Networks

Blind and Semi-blind Channel Estimation and Collision Resolution for the Uplink of MU-MIMO and Massive MIMO Systems for B5G Networks

Spatial-Chirp Codebook-Based Hierarchical Beam Training for Extremely Large-Scale Massive MIMO

Spatial-Chirp Codebook-Based Hierarchical Beam Training for Extremely Large-Scale Massive MIMO

A Low-Cost Compact Dual-Polarized Patch Antenna Array for 5G Massive MIMO Base Station

A Low-Cost Compact Dual-Polarized Patch Antenna Array for 5G Massive MIMO Base Station

Performance analysis of massive MIMO using various transmit precoding schemes for a wireless network

SummaryAn evaluation of various transmit precoding schemes is proposed in this thesis on basis of spectral efficiency (SE) of massive MIMO (m‐MIMO) system in 2 GHz spectrum. The present generation of wireless networks must be rethought due to the rapidly rising user base, huge volume data transfer, wide coverage requirement, and exponential rise in data rate. 5G mobile network includes a multi‐fold increase in network capacity, energy conservation, decreased latency, higher coverage probability, and a seamless connection. m‐MIMO has provided good solution for enhancing the data rate with the coverage, overcoming the effects of multipath fading and has also increased diversity of the system. Massiveness of MIMO system along with 5G network is the key technology to address the issue of SE, throughput, improvement of QoS (quality of service), coverage probability, power delay profile, diversity simulation, energy efficiency, and improvement in bit error rate. Parameters such as SE, throughput, and coverage probability are important for any communication system, and their improvement can increase overall capacity of cellular network system and could meet the requirement of upcoming demand for mobile network. The research paper is focused on performance analysis of m‐MIMO with reference to the SE of various transmit precoding schemes. Simulation results show that SE provided by M‐MMSE (multi‐cell minimum mean‐squared error) technique of precoding ranks highest for the same number of antennas and pilot reuse factor as compared to other schemes.

Privacy and convergence analysis for the internet of medical things using massive MIMO

Machine learning is the analysis based on data that gives strategic decisions to cultivate an accurate and stable framework for different applications. Access to medical data with the utmost privacy and high data rates is still a challenging problem. To accomplish the above-mentioned features, the performance of federated learning (FL) with 5G massive multiple-input-multiple-output (MIMO) is investigated for IoMT systems. This provides an energy-efficient and privacy-preserving solution with high throughput for digital health system. In the proposed model, the uplink scenario is investigated using different detection techniques. The performance are evaluated at the central server and edge devices for different signal-to-noise ratios (SNRs) and different fading channels. The ML bit error rate (BER) is better than MRC but with higher complexity. The accuracy obtained is approximately 90% with an improvement of around 8% to 9% as compared to the baseline approach.

Improving the latency for 5G/B5G based smart healthcare connectivity in rural area

Smart hospitals are poised to greatly enhance life quality by offering persistent health monitoring capabilities. Remote healthcare and surgery, which are highly dependent on low latency, have seen a transformative improvement with the advent of 5G technology. This has facilitated a new breed of healthcare services, including monitoring and remote surgical procedures. The enhanced features of 5G, such as Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC), have enabled the development of advanced healthcare systems. These systems reduce the need for direct patient contact in hospitals, which is especially pertinent as 5G becomes more widespread. This research presents novel hybrid detection algorithms, specifically QR decomposition with M-algorithm maximum likelihood-minimum mean square error (QRM-MLD-MMSE) and QRM-MLD-ZF (zero forcing), for use in Massive MIMO (M-MIMO) technology. These methods aim to decrease the latency in MIMO-based Non-Orthogonal Multiple Access (NOMA) waveforms while ensuring optimal bit error rate (BER) performance. We conducted simulations to evaluate parameters like BER and power spectral density (PSD) over Rician and Rayleigh channels using both the proposed hybrid and standard algorithms. The study concludes that our hybrid algorithms significantly enhance BER and PSD with lower complexity, marking a substantial improvement in 5G communication for smart healthcare applications.

Signal detection algorithms for massive MIMO system

As 5G communication networks are maturing, we have higher and higher requirements for the detection of communication signals. In this paper, for the Massive MIMO system signal detection problem, we mainly summarize the detection algorithms that can be used to replace the traditional ZF and MMSE, so as to avoid large-scale matrix inverse and reduce the computational complexity. It mainly includes the general iterative method, typically represented by SSOR, which makes the transmit signal matrix constantly close to the ideal value by iterating; the other is the level expansion class solution method, which takes the order expansion of the level as the initial value of the iteration to accelerate the convergence rate of the algorithm, typically represented by the MLI algorithm. However, today where the demand for communication is gradually increasing and the number of users is constantly getting larger, the performance of the above algorithms may degrade seriously, so the AI signal detection algorithm is a good alternative, which learns autonomously through deep neural networks, including model-driven and data-driven schemes.

A deep learning approach for reduce CSI feedback overhead in massive MIMO

The Massive MIMO system in FDD communication mode is still one of the indispensable technologies in future cellular communication systems. In order to reduce CSI feedback overhead and enhance the quality of CSI channel reconstruction, this paper proposes a RiCsiNet network model and designs an iNetBlock module with larger convolutional kernels and a wider network structure. By connecting parallel convolution kernels of 1 × 1, 3 × 3, 5 × 5, and 7 × 7, both the width of the network and the receptive field are increased simultaneously, enabling calculation of different feature details at each layer. The compressed CSI is accurately estimated through residual connections at the BS end. The network model proposed in this paper, when considering both the model parameter size and the estimated CSI accuracy, outperforms the existing algorithm model in the NMSE index of 70%. In this paper, we verify that using different learning rate decay algorithms to train the model, a good learning rate decay algorithm can effectively improve the quality of the model, the best result is 12% higher than the worst result. Furthermore, we demonstrate that training different network models with varying amounts of data can not only save time but also maximize CSI channel estimation quality on the BS side. Finally, the RiCsiNet model was verified using different SNR under COST2100 channel model. QuaDRiGa data sets with different truncated subcarriers are used to verify the RiCsiNet model. The CSI estimated by the RiCsiNet model all showed high accuracy.

The Evolution of Mobile Communication: A Comprehensive Survey on 5G Technology

This comprehensive survey meticulously navigates the evolution of mobile communication, focusing on the revolutionary strides introduced by 5G technology. The study elucidates primary objectives, research methodology, pivotal findings, and implications inherent in the trajectory of mobile communication. It aims for a detailed examination of the technological landscape, outlining features that distinguish 5G from its predecessors. The research methodology involves an exhaustive literature review, culminating in a comprehensive overview of 5G technology. Key findings highlight 5G's transformative capacities, spanning unparalleled data transfer speeds, low-latency communication, and applications like IoT, augmented reality, and autonomous vehicles. The study underscores 5G's far-reaching impact across various sectors. Serving as both a retrospective analysis and a valuable resource, the survey guides researchers, industry professionals, and policymakers through the intricate landscape of 5G technology. Addressing the imperative of accommodating increasing mobile data traffic, the 5G network strives for improved spectrum utilization, energy efficiency, and QoS parameters. Notable innovations include Massive MIMO, promising high spectral efficiency. The focus on efficacy, accuracy, and channel estimation techniques is evident. The article explores specific requirements of 5G wireless communication systems, addressing challenges faced by earlier mobile generations. The advent of 5G emerges as a pivotal solution, aiming for a 1000-fold capacity gain and assuring quality-of-service, higher spectral efficiency, ultra-reliable communication, and facilitating massive machinetype communication. Challenges in traffic load and resource consumption necessitate innovative approaches. The research reviews emerging wireless technologies, including an analysis of Massive MIMO as an efficient solution. The paper promises to be a valuable resource, contributing to the seamless development of state-of-the-art 5G and B5G networks, recognizing the importance of continued research and innovation in the dynamic landscape of mobile communication technologies.

Distributed Hybrid Precoding for Edge-Computing-Assisted Cell-Free Massive MIMO Systems With Local CSI

Distributed Hybrid Precoding for Edge-Computing-Assisted Cell-Free Massive MIMO Systems With Local CSI

Spectrum and Energy Efficiency of Massive MIMO for Hybrid Architectures With Phase Shifter and Switches in IoT Networks

Spectrum and Energy Efficiency of Massive MIMO for Hybrid Architectures With Phase Shifter and Switches in IoT Networks