On the Performance and Analysis of Massive MIMO for 5G Wireless Systems

2021 ◽  
Author(s):  
◽  
Callum Thomas Neil
Keyword(s):  
Spatial Correlation ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Wireless Systems ◽  
Technical Solution ◽  
Channel Models ◽  
Channel Matrix ◽  
Wide Range ◽  
Processing Techniques

<p>A novel technical solution, and paradigm shift, envisioned to achieve the significant spectral efficiency enhancements required for Fifth Generation (5G) wireless systems is massive multiple-input-multiple-output (MIMO). Massive MIMO systems scale up the number of transmit (TX) and receive (RX) antennas by at least an order of magnitude relative to conventional multi-user MIMO systems, which have been a key feature in current wireless standards, such as Long Term Evolution. Thus, massive MIMO leverages the spatial dimension by providing significant increases in all the virtues of conventional MIMO systems but on a much larger scale. Namely, data rate, link reliability, energy efficiency, and multiplexing gains can all be increased with massive MIMO systems, while simultaneously reducing inter-user interference through digital processing techniques. Further motivating the surge in research of massive MIMO systems are the additional channel properties which occur when operating with large dimensions. These properties arise as a result of random matrix theory asymptotics and under these conditions random variables become deterministic, simplifying analysis and allowing simple processing techniques to become (near) optimal. These idealistic properties, however, are based on the assumptions of an independent and identically distributed channel matrix with an infinite number of TX antennas.  Physical contraints typically prohibit the deployment of large numbers of TX antennas. It therefore seems natural to determine the number of TX antennas required for large MIMO systems to begin to exhibit these favourable asymptotic properties. Analytically deriving the first and second moments of the composite Wishart channel matrix and numerically defining three convergence metrics, the rate of channel convergence is examined. Limiting matched-filter (MF) and zero-forcing precoding signal-to-interference-plus-noise-ratio (SINR) performances are then analytically derived and rate of convergence shown. Coordinated distributed MIMO systems can mitigate the detrimental effects of spatial correlation relative to a colocated MIMO system. The instantaneous and limiting MF SINR performance of a distributed massive MIMO system is derived, allowing clear insights into the effects of imperfect channel state information, spatial correlation, link gains and number of antenna clusters. The wide bandwidths vacant at millimeter-wave (mmWave) frequency bands are suitable for 5G wireless systems since they occupy regions of uncongested spectrum which enable large contiguous bandwidth carriers. Spatial correlation of an arbitrary antenna array topology is analytically derived for a mmWave channel model. Numerically, the effects of mutual coupling amongst antenna elements is then shown on the effective spatial correlation, eigenvalue structure and user rate of different antenna topologies.   Channel models and measurements across a wide range of candidate bands for 5G wireless systems are then considered, motivated by the different propagation and spatial characteristics between different bands and different channel models within the same band. Key channel modelling and spatial parameter differences are identified and, in turn, their impact on various antenna topologies investigated, in terms of system sum rate, channel eigenvalue structure, effective degrees of freedom and massive MIMO convergence properties.</p>

scholarly journals Iterative Multiuser Equalization for Subconnected Hybrid mmWave Massive MIMO Architecture

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
R. Magueta ◽  
V. Mendes ◽  
D. Castanheira ◽  
A. Silva ◽  
R. Dinis ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Hybrid Approach ◽  
Wireless Systems ◽  
Low Complexity ◽  
Base Station ◽  
Analog Part ◽  
Processing Techniques ◽  
Fully Connected ◽  
Rf Chain

Millimeter waves and massive MIMO are a promising combination to achieve the multi-Gb/s required by future 5G wireless systems. However, fully digital architectures are not feasible due to hardware limitations, which means that there is a need to design signal processing techniques for hybrid analog-digital architectures. In this manuscript, we propose a hybrid iterative block multiuser equalizer for subconnected millimeter wave massive MIMO systems. The low complexity user-terminals employ pure-analog random precoders, each with a single RF chain. For the base station, a subconnected hybrid analog-digital equalizer is designed to remove multiuser interference. The hybrid equalizer is optimized using the average bit-error-rate as a metric. Due to the coupling between the RF chains in the optimization problem, the computation of the optimal solutions is too complex. To address this problem, we compute the analog part of the equalizer sequentially over the RF chains using a dictionary built from the array response vectors. The proposed subconnected hybrid iterative multiuser equalizer is compared with a recently proposed fully connected approach. The results show that the performance of the proposed scheme is close to the fully connected hybrid approach counterpart after just a few iterations.

scholarly journals Efficient Detectors based on Group Detection for Massive MIMO systems

2018 ◽  
Author(s):  
Thanh-Binh Nguyen ◽  
Minh-Tuan Le ◽  
Vu-Duc Ngo ◽  
Tien-Dong Nguyen ◽  
Huy-Dung Han
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Bell Laboratory ◽  
Channel Matrix ◽  
Input Multiple Output ◽  
Group Detection ◽  
Ber Performance

In Multiple Input Multiple Output (MIMO) systems, the complexities of detectors depend on the size of the channel matrix. In Massive MIMO systems, detection complexity becomes remarkably higher because the dimensions of the channel matrix get much larger. In order to recover the signals in the up-link of a Massive MIMO system at reduced complexities, we first divide the system into two sub-systems. After that, we apply the Minimum Mean Square Error (MMSE) and Bell Laboratory Layer Space Time (BLAST) detectors to each subsystem, resulting in the so-called MMSE-GD and BLAST-GD detectors, respectively. To further enhance the BER performance of Massive MIMO systems under the high-load conditions, we propose two additional detectors, called MMSE-IGD and BLAST-IGD by respectively applying the conventional MMSE and BLAST on the sub-systems in an iterative manner. It is shown via computer simulation and analytical results that the proposed detectors enable the system to achieve not only higher BER performance but also low detection complexities as compared to the conventional linear detectors. Moreover, the MMSE-IGD and BLAST-IGD can significantly improve BER performance of Massive MIMO systems.

MIMO Systems in a Composite Fading and Generalized Noise Scenario: A Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Keerti Tiwari
Keyword(s):  
System Performance ◽  
Performance Enhancement ◽  
Channel Model ◽  
Mimo Systems ◽  
Mimo System ◽  
Spatial Diversity ◽  
Spatial Multiplexing ◽  
Channel Models ◽  
Efficient System ◽  
Composite Channel

: Multiple-input multiple-output (MIMO) systems have been endorsed to enable future wireless communication requirements. The efficient system designing appeals an appropriate channel model, that considers all the dominating effects of wireless environment. Therefore, some complex or less analytically acquiescent composite channel models have been proposed typically for single-input single-output (SISO) systems. These models are explicitly employed for mobile applications, though, we need a specific study of a model for MIMO system which can deal with radar clutters and different indoor/outdoor and mobile communication environments. Subsequently, the performance enhancement of MIMO system is also required in such scenario. The system performance enhancement can be examined by low error rate and high capacity using spatial diversity and spatial multiplexing respectively. Furthermore, for a more feasible and practical system modeling, we require a generalized noise model along with a composite channel model. Thus, all the patents related to MIMO channel models are revised to achieve the near optimal system performance in real world scenario. This review paper offers the methods to improve MIMO system performance in less and severe fading as well as shadowing environment and focused on a composite Weibull-gamma fading model. The development is the collective effects of selecting the appropriate channel models, spatial multiplexing/detection and spatial diversity techniques both at the transmitter and the receivers in the presence of arbitrary noise.

scholarly journals The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1552
Author(s):  
Tongzhou Han ◽  
Danfeng Zhao
Keyword(s):  
Power Control ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Time Varying ◽  
Input Multiple Output ◽  
Efficiency Performance ◽  
A Cell ◽  
Time Varying Channel

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.

scholarly journals Maximizing Energy Efficiency for Consumption Circuit Power in Downlink Massive MIMO Wireless Networks

2017 ◽  
Vol 7 (6) ◽  
pp. 2977 ◽  
Author(s):  
Adeeb Salh ◽  
Lukman Audah ◽  
Nor Shahida M. Shah ◽  
Shipun A. Hamzah
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Data Rate ◽  
Optimal Number ◽  
Zero Forcing ◽  
High Data ◽  
Circuit Power

<span>Massive multi-input–multi-output (MIMO) systems are crucial to maximizing energy efficiency (EE) and battery-saving technology. Achieving EE without sacrificing the quality of service (QoS) is increasingly important for mobile devices. We first derive the data rate through zero forcing (ZF) and three linear precodings: maximum ratio transmission (MRT), zero forcing (ZF), and minimum mean square error (MMSE). Performance EE can be achieved when all available antennas are used and when taking account of the consumption circuit power ignored because of high transmit power. The aim of this work is to demonstrate how to obtain maximum EE while minimizing power consumed, which achieves a high data rate by deriving the optimal number of antennas in the downlink massive MIMO system. This system includes not only the transmitted power but also the fundamental operation circuit power at the transmitter signal. Maximized EE depends on the optimal number of antennas and determines the number of active users that should be scheduled in each cell. We conclude that the linear precoding technique MMSE achieves the maximum EE more than ZF and MRT</span><em></em><span>because the MMSE is able to make the massive MIMO system less sensitive to SNR at an increased number of antennas</span><span>.</span>

scholarly journals Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 301
Author(s):  
Samarendra Nath Sur ◽  
Rabindranath Bera ◽  
Akash Kumar Bhoi ◽  
Mahaboob Shaik ◽  
Gonçalo Marques
Keyword(s):  
Channel Capacity ◽  
Massive Mimo ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Mimo System ◽  
Correlation Effect ◽  
Performance Comparison ◽  
Lattice Reduction ◽  
Detection Algorithms ◽  
Minimum Mean Squared Error

Massive multi-input-multi-output (MIMO) systems are the future of the communication system. The proper design of the MIMO system needs an appropriate choice of detection algorithms. At the same time, Lattice reduction (LR)-aided equalizers have been well investigated for MIMO systems. Many studies have been carried out over the Korkine–Zolotareff (KZ) and Lenstra–Lenstra–Lovász (LLL) algorithms. This paper presents an analysis of the channel capacity of the massive MIMO system. The mathematical calculations included in this paper correspond to the channel correlation effect on the channel capacity. Besides, the achievable gain over the linear receiver is also highlighted. In this study, all the calculations were further verified through the simulated results. The simulated results show the performance comparison between zero forcing (ZF), minimum mean squared error (MMSE), integer forcing (IF) receivers with log-likelihood ratio (LLR)-ZF, LLR-MMSE, KZ-ZF, and KZ-MMSE. The main objective of this work is to show that, when a lattice reduction algorithm is combined with the convention linear MIMO receiver, it improves the capacity tremendously. The same is proven here, as the KZ-MMSE receiver outperforms its counterparts in a significant margin.

scholarly journals Statistical Beamforming for Massive MIMO Systems with Distinct Spatial Correlations

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6255
Author(s):  
Taehyoung Kim ◽  
Sangjoon Park
Keyword(s):  
Massive Mimo ◽  
Degrees Of Freedom ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
High Signal ◽  
Spatial Correlations ◽  
Channel Correlation ◽  
Low Correlation ◽  
Noise Ratio

In this paper, we propose a novel statistical beamforming (SBF) method called the partial-nulling-based SBF (PN-SBF) to serve a number of users that are undergoing distinct degrees of spatial channel correlations in massive multiple-input multiple-output (MIMO) systems. We consider a massive MIMO system with two user groups. The first group experiences a low spatial channel correlation, whereas the second group has a high spatial channel correlation, which can happen in massive MIMO systems that are based on fifth-generation networks. By analyzing the statistical signal-to-interference-plus-noise ratio, it can be observed that the statistical beamforming vector for the low-correlation group should be designed as the orthogonal complement for the space spanned by the aggregated channel covariance matrices of the high-correlation group. Meanwhile, the spatial degrees of freedom for the high-correlation group should be preserved without cancelling the interference to the low-correlation group. Accordingly, a group-common pre-beamforming matrix is applied to the low-correlation group to cancel the interference to the high-correlation group. In addition, to deal with the intra-group interference in each group, the post-beamforming vector for each group is designed in the manner of maximizing the signal-to-leakage-and-noise ratio, which yields additional performance improvements for the PN-SBF. The simulation results verify that the proposed PN-SBF outperforms the conventional SBF schemes in terms of the ergodic sum rate for the massive MIMO systems with distinct spatial correlations, without the rate ceiling effect in the high signal-to-noise ratio region unlike conventional SBF schemes.

scholarly journals Signal Detection Based on Parallel Group Detection Algorithm for High-Load Massive MIMO Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Thanh-Binh Nguyen ◽  
Minh-Tuan Le ◽  
Vu-Duc Ngo
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Detection Algorithm ◽  
High Load ◽  
Signal Recovery ◽  
Parallel Group ◽  
Group Detection ◽  
Practical Complexity ◽  
Ber Performance

In this paper, a parallel group detection (PGD) algorithm is proposed in order to address the degradation in the bit error rate (BER) performance of linear detectors when they are used in high-load massive MIMO systems. The algorithm is constructed by converting the equivalent extended massive MIMO system into two subsystems, which can be simultaneously detected by the classical detection procedures. Then, using the PGD and the classical ZF as well as the QR-decomposition- (QRD-) based detectors, we proposed two new detectors, called ZF-based PGD (ZF-PGD) and QRD-based PGD (QRD-PGD). The PGD is further combined with the sorted longest basis (SLB) algorithm to make the signal recovery more accurate, thereby resulting in two new detectors, namely, the ZF-PGD-SLB and the QRD-PGD-SLB. Various complexity evaluations and simulations prove that the proposed detectors can significantly improve the BER performance compared to their classical linear and QRD counterparts with the practical complexity levels. Hence, our proposed detectors can be used as efficient means of estimating the transmitted signals in high-load massive MIMO systems.

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