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

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.

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.

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.

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

In Massive Multiple Input Multiple Output (MIMO) system, pilot signals are reused to accommodate exponential rise in user density. This contaminates the channel estimation for the intended user and affects the system performance. In the proposed Time-Shifted Pilot signal Transmission with Pilot Hopping (TSPTPH) system, pilot signal transmission is overlapped with downlink data transmission and pilot signal hopping is performed in each coherence interval. Hopping is achieved by switching user to a new pilot signal, this results in random change in interfering users. This changes the large-scale fading coefficient β, which is a function of radial distance between base station and user and thus improves the system performance. Proposed system enhances the uplink and downlink signal to interference plus noise ratio and data rate by estimating the channel with minimum mean square error estimation and reduces the uplink signal to noise ratio for data transmission for normalised spectral efficiency with rising number of antennas at the base station.

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.

A New Joint TSPA/WGC Pilot Contamination Reduction Strategy Based on Exact Graph Coloring Grouping Algorithm

Massive multiple-input multiple-output (MIMO) system uses a large number of antennas in the base station (BS) serving a set of users in order to increase the spectral efficiency. The pilot-contamination is considered the most remarkable impairment that limits the performance of massive MIMO system. In this paper, two different pilot assignment approaches are jointly proposed to reduce pilot contamination effect. A time-shifted pilot assignment (TSPA) approach is used, where a cellular network is divided into exclusive groups, wherein users in the same group send their uplink-pilots simultaneously, while other users receive their downlink-data. Through the uplink-pilot phase, a heuristic weighted graph coloring-based pilot assignment (WGC-PA) approach is used to reduce intra-group interference caused by pilot contamination. Different uplink-pilots are allocated to the users in the same group having the largest pilot contamination severity (PCS), whereas other users with the smallest PCS share the same uplink-pilots. To divide the cellular network into exclusive groups, we propose a cells grouping technique based on the adjacent distance between cells. This proposed technique uses a backtracking based graph coloring algorithm. It ensures that no two adjacent cells are in the same group in the time-shifted approach. The simulation results show that the proposed joint WGC/TS-PA strategy not only reduces the pilot contamination effect but also reduces computational complexity than the weighted graph coloring approach.

Calibration for Channel Reciprocity in Industrial Massive MIMO Antenna Systems

Massive multiple input multiple output (MIMO) antenna systems have received a great deal of interest due to their applicability to industrial network systems. One of the major obstacles that reducing the performance of massive MIMO system is reference signal (RS) overhead which can be increased as the number of transmitter antenna increases in frequency division duplexing (FDD) system. Using channel reciprocity in time division duplexing (TDD) system can significantly increase the RS overhead performance using channel reciprocity. However, to use the channel reciprocity, channel calibration is a significant challenge to overcome in real system design. There are various base station (BS) calibration methods that have already successfully applied to the current BS, while there are few methods for the calibration of distributed user entities (UEs) and/or industrial Internet of things (IIoT) devices. In this paper, we propose a distributed UE RF calibration method of massive MIMO systems that uses the power headroom report. The power headroom is typically reported from the UEs and/or IIoT devices to the BS periodically or aperiodically. By including additional amplitude RF impairment information in the power headroom report, we can successfully transfer the necessary information from UE to BS. The proposed scheme does not require additional feedback, and is in compliance with current standards. There are five schemes based on the kind of information transferred to BS and the way of making a calibration factor that can be multiplied to the estimated channel. Numerical analysis shows that the proposed schemes can significantly increase the spectral efficiency with little system burden, and thus can be a core technology for the realization of massive MIMO for industrial network systems.

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.

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.

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.

Impact of RF mismatches on the performance of massive MIMO systems with ZF precoding

Thanks to the channel reciprocity, the time division duplex (TDD) operation is more preferred in massive multiple-input multiple-output (MIMO) systems. Avoiding the heavy feedback of downlink channel state information (CSI) from the user equipment (UE) to the base station (BS), the uplink CSI can be exploited for the downlink precoding. However, due to the mismatches of the radio frequency (RF) circuits at both sides of the link, the whole communication channels are usually not symmetric in practical systems. This paper is focused on the RF mismatches at the UEs and the BS for the multi-user massive MIMO systems with zero forcing (ZF) precoding. The closed-form expressions of the ergodic sum-rates are derived for evaluating the impact of RF mismatches on the system performance. Theoretical analysis and simulation results show that the RF mismatches at the UEs only lead to a negligible performance loss. However, it is imperative to perform reciprocity calibration at the BS, because the RF mismatches at the BS contribute to the inter-user interference (IUI) and result in a severe system performance degradation.