Performance Of Massive Multiple-input Multiple-output Systems Research Articles

Developing Laboratory Experiments to teach Undergraduate Engineering Students Multi-User Massive MIMO Technology for 5G Cellular Networks

An effective method of facilitating student learning in a laboratory environment is “practice by doing”. The study of modules related to signal-processing for digital communications requires deep mathematical and theoretical foundations but the practice goal is not really emphasized in the undergraduate curriculum at the University of Mauritius. This causes the students to lose interest in the corresponding modules resulting in high rate of failures. In this work, we propose to develop laboratory experiments with a view to bridge the gap between theoretical and practical aspects in the field of Massive Multiple-Input Multiple-Output (MIMO) systems for 5G cellular networks. Various laboratory scenarios are set up that consider a Maximum Ratio Combining (MRC) receiver in the uplink with an uncorrelated Rayleigh fading channel. Moreover, from a signal processing perspective to enhance the student’s understanding, we analyze the efficiency and error performance of Massive MIMO systems with MPSK and MQAM modulation schemes as well as perfect and imperfect channel estimates. The leading industry software package MATLAB R2022 is used to develop all laboratory experiments and the codes are elaborated in this analysis. Data collected from the experiments are used to generate spectral efficiency and error performance curves which can be used for future research. The findings underscore the significance of accounting for both scenarios and illuminate promising avenues for future research in the realm of massive MIMO education and learning. The assimilation of MATLAB® flowcharts for each MRC receiver, MPSK, and MQAM with perfect and imperfect Channel State Information (CSI) adds further depth to the study, ensuring a comprehensive understanding of the intricacies of massive MIMO systems. Ultimately, this contribution helps in the nurturing of expertise such that future generations of wireless communication pioneers can be inspired.

Orthogonal beamforming technique for massive MIMO systems

AbstractBeamforming represents a pivotal technology in massive multiple-input multiple-output (MIMO) systems, as it facilitates the regulation of transmission and reception operations. Beamforming techniques’ categorization is based either on their hardware architecture or implementation strategy. This paper proposes an orthogonal beamforming technology founded on a specific implementation method that utilizes predetermined orthogonal beams to serve users. The suggested approach incorporates numerous orthogonal beams relying on a substantial number of antennas at the base station. The primary objective of this approach is to enhance the performance of massive MIMO systems by augmenting spectral efficiency and accommodating more users. The proposed beamforming approach is well suited for millimeter frequency bands. The purpose of this paper is to explore the suggested orthogonal beamforming technology. The concept of this approach is described at first and then followed by an evaluation of its efficacy for a single user through the allocation of orthogonal beams. The suggested approach is also examined in the context of multiuser systems, and the results are compared with the adaptive ZF beamforming technique. Furthermore, the paper presents solutions to the issues that may arise in multiuser systems, for example, ensuring that each orthogonal beam is assigned to only one user. The simulations conducted in this study demonstrate that the suggested approach outperforms the ZF technique in terms of both the spectral efficiency and the number of serviced users. Specifically, the suggested approach can enhance SE by approximately 40.6% over the ZF technique, and it can support up to double the number of users when compared to the ZF approach.

Technology Trends for Massive MIMO towards 6G.

At the dawn of the next-generation wireless systems and networks, massive multiple-input multiple-output (MIMO) in combination with leading-edge technologies, methodologies, and architectures are poised to be a cornerstone technology. Capitalizing on its successful integration and scalability within 5G and beyond, massive MIMO has proven its merits and adaptability. Notably, a series of evolutionary advancements and revolutionary trends have begun to materialize in recent years, envisioned to redefine the landscape of future 6G wireless systems and networks. In particular, the capabilities and performance of future massive MIMO systems will be amplified through the incorporation of cutting-edge technologies, structures, and strategies. These include intelligent omni-surfaces (IOSs)/intelligent reflecting surfaces (IRSs), artificial intelligence (AI), Terahertz (THz) communications, and cell-free architectures. In addition, an array of diverse applications built on the foundation of massive MIMO will continue to proliferate and thrive. These encompass wireless localization and sensing, vehicular communications, non-terrestrial communications, remote sensing, and inter-planetary communications, among others.

High-Precision Iterative Preconditioned Gauss–Seidel Detection Algorithm for Massive MIMO Systems

Signal detection is a serious challenge for uplink massive multiple-input multiple-output (MIMO) systems. The traditional linear minimum-mean-squared error (MMSE) achieves good detection performance for such systems, but involves matrix inversion, which is computationally expensive due to a large number of antennas. Thus, several iterative methods such as Gauss–Seidel (GS) have been studied to avoid the direct matrix inversion required in the MMSE. In this paper, we improve the GS iteration in order to enhance the detection performance of massive MIMO systems with a large loading factor. By exploiting the property of massive MIMO systems, we introduce a novel initialization strategy to render a quick start for the proposed algorithm. While maintaining the same accuracy of the designed detector, the computing load is further reduced by initialization approximation. In addition, an effective preconditioner is proposed that efficiently transforms the original GS iteration into a new one that has the same solution, but a faster convergence rate than that of the original GS. Numerical results show that the proposed algorithm is superior in terms of complexity and performance than state-of-the-art detectors. Moreover, it exhibits identical error performance to that of the linear MMSE with one-order-less complexity.

Cell-Free MIMO Systems Powered by Intelligent Reflecting Surfaces

Cell-free massive multiple-input multiple-output (MIMO) and intelligent reflecting surface (IRS) are considered as the prospective multiple antenna technologies for beyond the fifth-generation (5G) networks. Cell-free MIMO systems powered by IRSs, combining both technologies, can further improve the performance of cell-free MIMO systems at low cost and energy consumption. Prior works focused on instantaneous performance metrics and relied on alternating optimization algorithms, which impose huge computational complexity and signaling overhead. To address these challenges, we propose a novel two-step algorithm that provides the long-term passive beamformers at the IRSs using statistical channel state information (S-CSI) and short-term active precoders and long-term power allocation at the access points (APs) to maximize the minimum achievable rate. Simulation results verify that the proposed scheme outperforms benchmark schemes and brings a significant performance gain to the cell-free MIMO systems powered by IRSs.

Cell-Free Massive MIMO Systems With Oscillator Phase Noise: Performance Analysis and Power Control

Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising technology for satisfying higher rate requirement of users in beyond-5G networks. This paper investigates the impact of phase noise on the performance of cell-free MIMO systems which employ a large number of multi-antenna access points (APs). For both downlink and uplink transmissions, we derive the tractable closed-form achievable rate expressions when signal detection with channel state information (CSI) and channel distribution information (CDI), respectively, by utilizing the extended deterministic equivalence method in random matrix theory. The closed-form achievable rate expressions provide efficient evaluation for the impact of phase noise on system performance. The analytical results indicate that the phase noise influences the power of the desired signal, the beamforming uncertainty gain, and the inter-user interference increment caused by the pilot contamination. In addition, the special cases that only APs have phase noise and only users have phase noise are discussed. It is found that the impact of phase noise at users is more severe than that at APs. Besides, we discuss the system performance at low and high SNR regimes. It demonstrates that the phase noise is the major limitation of the system performance in high SNR regime while it can be neglected in low SNR regime. Furthermore, we formulate the max-min power control problem for both downlink and uplink transmissions to guarantee the users’ fairness and prove that the downlink max-min problem is a quasi-concave problem while the uplink max-min problem is a geometric programming (GP) problem. Then, the phase noise-aware max-min power control schemes for both downlink and uplink transmissions are proposed to ensure the users’ fairness. Finally, numerical results are provided to validate the analytical results and the proposed phase noise-aware power control scheme.

Performance of Massive MIMO systems in cognitive spectrum sensing

Cognitive radio is a revolutionary technology that guarantees a viable solution to the spectrum scarcity problem. This prominent new technology has the power to change future technological developments forever if intelligently applied. In Cognitive Radio Networks, secondary (unlicensed) user uses the licensed spectrum band opportunistically, without creating any interference with primary user (licensed) transmission. Spectrum sensing is a vital challenge for spectrum usage and to prevent harmful interference with licensed users. Energy detection has been for a long, constituting the most widely known sensing technique in cognitive radio systems. In this paper, Neyman - Pearson criterion-based detection rules are used to incorporate the local probabilities of detection and false alarm to analyse an energy detector sensing behaviour over additive white Gaussian (AWGN) channel, fading channel and massive multiple input multiple output(MIMO) cases. Closed-form solutions for the probabilities of false alarm and detection were derived. The analytical results were verified by numerical computations using the Monte Carlo method with MATLAB.

Pilot Decontamination Using Asynchronous Fractional Pilot Scheduling in Massive MIMO Systems.

Due to large spectral efficiency and low power consumption, the Massive Multiple-Input-Multiple-Output (MIMO) became a promising technology for the 5G system. However, pilot contamination (PC) limits the performance of massive MIMO systems. Therefore, two pilot scheduling schemes (i.e., Fractional Pilot Reuse (FPR) and asynchronous fractional pilot scheduling scheme (AFPS)) are proposed, which significantly mitigated the PC in the uplink time division duplex (TDD) massive MIMO system. In the FPR scheme, all the users are distributed into the central cell and edge cell users depending upon their signal to interference plus noise ratio (SINR). Further, the capacity of central and edge users is derived in terms of sum-rate, and the ideal number of the pilot is calculated which significantly maximized the sum rate. In the proposed AFPS scheme, the users are grouped into central users and edge users depending upon the interference they receive. The central users are assigned the same set of pilots because these users are less affected by interference, while the edge users are assigned the orthogonal pilots because these users are severely affected by interference. Consequently, the pilot overhead is reduced and inter-cell interference (ICI) is minimized. Further, results verify that the proposed schemes outperform the previous proposed traditional schemes, in terms of improved sum rates.

On the Performance of Massive MIMO Systems With Low-Resolution ADCs and MRC Receivers Over Rician Fading Channels

This article considers uplink massive multiple-input multiple-output (MIMO) systems with maximum-ratio-combining (MRC) receivers over Rician fading channels. Low-resolution analog-to-digital converters (ADCs) are considered for both data detection and channel estimation. Asymptotic approximations of the spectrum efficiency (SE) for large-scale MIMO systems are derived based on random matrix theory. With these closed-form expressions, we can provide insights into the tradeoffs between the SE and the ADC resolution and study the influence of the Rician K-factors on the performance. It is shown that a large value of K-factors may lead to a better performance and alleviate the influence of quantization noise on channel estimation. Moreover, we investigate the power-scaling laws for the system under imperfect channel state information and it shows that when the number of base station (BS) antennas is very large, without loss of the SE performance, the transmission power can be scaled by the number of BS antennas while the overall performance is limited by the resolution of ADCs. The asymptotic analysis is validated by numerical results. We also show that ADCs with intermediate resolutions lead to better energy efficiency than that with high-resolution or extremely low-resolution ADCs.

The Downlink Performance of Massive MIMO Systems under Different Uplink Pilot Allocation Schemes

In the scenario of time division duplexing (TDD) massive multiple-input multiple-output (MIMO) system, when there is pilot contamination (PC) in the uplink, different pilot allocation schemes will affect the results of the uplink channel estimation. When the channel estimation results are used for downlink transmission precoding, which will further affects the downlink performance. This paper analyses the impact of different pilot allocation (PA) schemes on downlink performance. Through theoretical analysis, the system downlink signal to interference plus noise ratio (SINR) and spectral efficiency expressions of different pilot allocation schemes are obtained respectively. The simulation results show that the pilot allocation schemes with cell grouping is better than the schemes without cell grouping. The SINR increases as the number of cell grouping increases, and the spectrum efficiency increases first and then decreases as the number of cell grouping increases.

A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments.

Massive multiple input multiple output (MIMO) technology is one of the promising technologies for fifth generation (5G) cellular communications. In this technology, each cell has a base station (BS) with a large number of antennas, allowing the simultaneous use of the same resources (e.g., frequency and/or time slots) by multiple users of a cell. Therefore, massive MIMO systems can bring very high spectral and power efficiencies. However, this technology faces some important issues that need to be addressed. One of these issues is the performance degradation due to hardware impairments, since low-cost RF chains need to be employed. Another issue is the channel estimation and channel aging effects, especially in fast mobility environments. In this paper we will perform a comprehensive study on these two issues considering two of the most promising candidate waveforms for massive MIMO systems: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain processing (SC-FDP). The studies and the results show that hardware impairments and inaccurate channel knowledge can degrade the performance of massive MIMO systems extensively. However, using suitable low complex estimation and compensation techniques and also selecting a suitable waveform can reduce these effects.

Novel Spectral Efficient Technique for MIMO-OFDM Channel Estimation with Reference to PAPR and BER Analysis

With emerge of increasing research in domain of future wireless communications, massive multiple input multiple output (MIMO) attracted most of researchers interests. Massive MIMO is nothing but high speed wireless communication standards. The performance of MIMO systems is based on techniques used for channel estimation. Efficient channel estimation leads to spectral efficient wireless communications. There are number of channel estimation techniques presented recently in literature with pros and cons. The recent method shows the spectral and bit error rate (BER) efficiency, however apart from this, there is need of improving the peak to average power ratio (PAPR). Recently we proposed, novel channel estimation method as the existing channel estimation techniques failed to effectively solve the inter symbol interference (ISI) problem. The presence of ISI in MIMO-OFDM may leads to worst performance. Our proposed blind channel estimation is combined with independent component analysis (ICA) hence this method is called as hybrid ICA (HICA) to minimize the ISI effect. The extensive simulation analysis of proposed HICA required to claiming the scalability as well as reliability. In this paper, proposed study on additional performance metrics such as PAPR and computational costs (energy) along with BER and spectral efficiency performances. The result claims that HICA is not improving the PAPR and energy performances significantly.

Efficient Detectors based on Group Detection for Massive MIMO systems

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.

Sum Rate Analysis and Power Allocation for Massive MIMO Systems With Mismatch Channel

Massive multiple-input multiple-output (MIMO) has been regarded as one of the key technologies of fifth-generation cellular systems due to its excellent performance in spectral and energy efficiency, whose performance has also been widely studied. However, most related works focus only on the impact of the wireless channels. In fact, its performance is affected not only by the wireless channel but also by the transceiver radio frequency (RF) circuits. Random variation of RF gain would lead to a mismatch channel, where the downlink is not the transpose of the uplink in time-division duplex (TDD) mode. Therefore, the impact of the transceiver RF circuits should be considered when we evaluate the performance of the massive MIMO systems. In this paper, we develop a detailed analysis on the downlink sum rate of the massive MIMO systems and derive its closed-form expressions using maximum ratio transmission and zero-forcing (ZF) precoding. The derived results provide some good insight into how the system performance is affected by the RF mismatch parameters. Based on the analytical results, we further investigate the optimal power allocation scheme to maximize the sum rate subject to the total power constraint and lowest rate requirement. For the simplest case of user equipment side mismatch with the ZF precoding, we apply the water-filling solution, while for the other mismatch cases, we conduct a convex relaxation on the non-convex problem through lower bound inequality, variable substitution, and Taylor expansion techniques, before applying some convex optimization solving tools. In the end, we propose an iterative algorithm to successively improve the iterative results for approaching the optimal solution. Simulations demonstrate that, for the massive MIMO systems with RF mismatch, our power allocation schemes achieve significant capacity improvement relative to an equal power scheme, and it performs well for the ZF precoding in the case of the RF mismatch only at the base station.

Impacts of K ‐fading on the performance of massive MIMO systems

Massive multiple-input multiple-output (MIMO) has been identified as a key technology for next generation cellular systems. The performance of a multi-cellular system with large antenna arrays at the base station and single antenna user terminals, operating under a composite fading-shadowing environment are considered. The outage probability of the considered system is evaluated for the uplink transmission that uses a maximum ratio combining receiver.

Evaluation of Massive MIMO Considering Real Propagation Characteristics in the 20-GHz Band

This paper presents multiple-input multiple-output (MIMO) measurements in the 20-GHz band, and evaluates the basic performance of massive MIMO systems when considering 100 elements at a base station (BS) and seven user equipment units. First, it is shown that zero forcing (ZF) is essential as a transmission scheme in massive MIMO systems, whereas the maximum-ratio combining method cannot reduce interference effectively in such systems. In addition, it is shown that analog–digital hybrid beamforming can be used to reduce the calculation complexity and improve hardware implementation. Next, we compared the Shannon capacity with the achievable bit rate (ABR), using ZF for seven-user MIMO transmissions with 100 elements at the BS. When the number of degrees of freedom in the array antenna is guaranteed, the ABR is almost the same as the Shannon capacity. Moreover, multiuser transmission is realized by linear decoding. Thus, decoding methods such as ZF are effective in massive MIMO systems.

Large System Analysis of Power Normalization Techniques in Massive MIMO

Linear precoding has been widely studied in the context of Massive multiple-input-multiple-output (MIMO) together with two common power normalization techniques, namely, matrix normalization (MN) and vector normalization (VN). Despite this, their effect on the performance of Massive MIMO systems has not been thoroughly studied yet. The aim of this paper is to fulfill this gap by using large system analysis. Considering a system model that accounts for channel estimation, pilot contamination, arbitrary pathloss, and per-user channel correlation, we compute tight approximations for the signal-to-interference-plus-noise ratio and the rate of each user equipment in the system while employing maximum ratio transmission (MRT), zero forcing (ZF), and regularized ZF precoding under both MN and VN techniques. Such approximations are used to analytically reveal how the choice of power normalization affects the performance of MRT and ZF under uncorrelated fading channels. It turns out that ZF with VN resembles a sum rate maximizer while it provides a notion of fairness under MN. Numerical results are used to validate the accuracy of the asymptotic analysis and to show that in Massive MIMO, non-coherent interference and noise, rather than pilot contamination, are often the major limiting factors of the considered precoding schemes.

An opportunistic scheduling algorithm using aged CSI in massive MIMO systems

In time-division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, the spatial multiplexing gain cannot be fully achieved since available pilot resources at each channel coherence time interval are limited. In this paper, we propose an Opportunistic user Scheduling algorithm, termed OpSAC, that uses Aged Channel state information (CSI) to increase spatial multiplexing gain without incurring additional pilot overhead. Assuming the base station (BS) employs two popular precoders of maximum ratio transmission (MRT) and zero-forcing (ZF), we first derive their closed-form lower bounds on the achievable sum-rate under channel aging. According to the analysis results, we develop a heuristic solution that estimates the amount of channel variation for each user by using correlation of CSI samples, and exploits channel conditions to opportunistically schedule more users by using aged CSI, thereby enhancing spectral efficiency. Through numerical analysis and simulation we confirm that the proposed lower bounds are very tight, and show the impact of channel aging on the performance of massive MIMO systems. In addition, it is shown that OpSAC achieves near-optimal performance and considerably outperforms the conventional user scheduling algorithm that uses current CSI only.

Performance Analysis of Mixed-ADC Massive MIMO Systems Over Rician Fading Channels

The practical deployment of massive multiple-input multiple-output (MIMO) in future fifth generation (5G) wireless communication systems is challenging due to its high hardware cost and power consumption. One promising solution to address this challenge is to adopt the low-resolution analog-to-digital converter (ADC) architecture. However, the practical implementation of such architecture is challenging due to the required complex signal processing to compensate the coarse quantization caused by low-resolution ADCs. Therefore, few high-resolution ADCs are reserved in the recently proposed mixed-ADC architecture to enable low-complexity transceiver algorithms. In contrast to previous works over Rayleigh fading channels, we investigate the performance of mixed-ADC massive MIMO systems over the Rician fading channel, which is more general for the 5G scenarios like Internet of Things (IoT). Specially, novel closed-form approximate expressions for the uplink achievable rate are derived for both cases of perfect and imperfect channel state information (CSI). With the increasing Rician $K$-factor, the derived results show that the achievable rate will converge to a fixed value. We also obtain the power-scaling law that the transmit power of each user can be scaled down proportionally to the inverse of the number of base station (BS) antennas for both perfect and imperfect CSI. Moreover, we reveal the trade-off between the achievable rate and energy efficiency with respect to key system parameters including the quantization bits, number of BS antennas, Rician $K$-factor, user transmit power, and CSI quality. Finally, numerical results are provided to show that the mixed-ADC architecture can achieve a better energy-rate trade-off compared with the ideal infinite-resolution and low-resolution ADC architectures.

Optimal beamforming-selection spatial precoding using population-based stochastic optimization for massive wireless MIMO communication systems

The beamforming-based spatial precoding (BBSP) method has been proposed to reduce the overheads of the downlink training and the channel state information feedback in the frequency-division duplex (FDD) massive multiple-input-multiple-output (MIMO) wireless communication systems. However, the original BBSP method suffers from the interference problem at user equipments (UEs) because of using a set of pre-defined fixed beamforming coefficients. Moreover, the BBSP method can not deal with the performance degradation due to mutual coupling (MC) effect because of massive antennas deployed at transmitter and receiver. This paper presents a precoding method that incorporates a beamforming-selection spatial precoding (BSSP) scheme with a population-based stochastic optimization algorithm such that the designed beamforming coefficients can greatly reduce the severe interference between UEs and alleviate the MC effect on the performance of massive MIMO systems. The proposed method can not only achieve better bit error rate (BER) performance than the conventional BBSP method, but also preserves the advantages of the BBSP method having lower overheads of the downlink training and the CSI feedback. In particular, we propose an appropriate fitness function based on an averaged BER formula for the population-based stochastic optimization algorithm to find the optimal beamforming coefficients. Numerical simulations are also presented for both the urban-macro and the urban-micro wireless MIMO scenarios to validate the superior BER performance of the proposed precoding method as compared to the existing BBSP method.