Downlink SLNR based precoding and power control in massive MIMO systems with time-shifted pilots

The base station (BS) density influences the coverage performance of cellular communication system. In massive multiple input multiple output (MIMO) system, each BS is equipped with large scale antenna array. The stochastic geometric model is employed because of its analytical tractability. This paper demonstrates that BS density has to be below a certain bound in order to satisfy the coverage requirement in massive MIMO system. Closed-form expression for the BS density bounded by maximum outage probability is derived using Lambert W function. Moreover, the expression for the upper bound of successful area spectral efficiency (ASE) is also derived. Our study claims that increasing BS density will lead to the deterioration of coverage performance in massive MIMO system.

Recently, massive multiple input multiple output (MIMO) is considered as a promising technology to significantly improve the spectral and energy efficiency of fifth generation (5G) networks. Massive MIMO increases the energy efficiency (EE) by three orders of magnitude as compared to single antenna systems. In this paper, we review and analyze energy efficient resource allocation techniques for massive MIMO system. To this end, the energy efficiency of massive massive MIMO with linear processing is formulated. Analytical formulation is provided for the spectral efficiency (SE) and total system power consumption model. The tradeoff on the spectral and energy efficiency is analyzed. The impacts of propagation and system parameters on the SE-EE tradeoff are investigated. Then, large scale fading based analytical approximation for the energy efficiency is formulated. Finally, numerical simulation is done to validate the theoretical analysis.

As a combination of spatial modulation (SM) system and massive multiple-input multiple-output (MIMO) system, massive SM aided MIMO (SM-MIMO) system is recently proposed. In broadband scenarios, single-carrier (SC) modulation is introduced to massive SM-MIMO system, thus massive SC-SM MIMO system is proposed for uplink multi-user transmission over frequency-selective fading channels. In this study, the uplink spectral efficiency (SE) of multi-cell massive SC-SM MIMO system is analysed, meanwhile the pilot contamination effect is taken into consideration. A tight SE lower bound of multi-cell massive SC-SM MIMO system is proposed with maximum ratio (MR) combining, which also takes into account the imperfect channel estimation, transmit antenna correlation and path loss. The tightness of the authors' proposed closed-form SE lower bound is shown via simulation results. The optimal pilot reuse factor can be determined with different system configurations by simulations, and the pilot reuse factor that is larger than one is more suitable for less TAs and user equipments.

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.

This paper studies approximate solutions for the statistical properties of the outage intervals of the instantaneous capacity in massive multiple- input multiple- output (MIMO) sys- tems with orthogonal space-time block code (OSTBC) over Rayleigh fading channels. We take advantage from the fact that the probability density function (PDF) of the channel power gain can be approximated by a left-truncated Gaussian distribution if the number of transmit and receive antennas is large. Assuming a symmetrical Doppler power spectral density (PSD), a closed- form expression is presented for the Rice probability function of the outage durations. This function, in general, approximates the PDF of the outage intervals only over its initial part. By applying the asymptotic behavior of level crossings of Gaussian processes, an alternative expression, valid only for low capacity thresholds, is obtained, which, in turn, is applied to determine an approximate solution for the kth-order moments of the outage intervals. Also presented is the PDF of the so- called instantaneous capacity loss with respect to the average capacity. Simulation results have been shown to be in good agreement with the theoretical approximations, thereby confirming their validity. Although, the focus of the study is on massive MIMO systems, it has been observed that the approximations are accurate even for a moderate number of antennas such as the case of 4×4 MIMO-OSTBC systems.

This paper proposes a new channel estimation scheme based on implicit pilots, optimized for a simplified massive multiple input, multiple output (MIMO), implemented with precoding, combined with Single-Carrier with Frequency-Domain Equalization (SC-FDE) modulations. We propose an iterative receiver that considers an iterative detection with interference cancellation and channel estimation. The channel estimates are usually obtained with the help of pilot symbols and/or training sequences multiplexed with data symbols. Since the required overheads in massive MIMO schemes can be too high, leading to spectral degradation, the use of superimposed pilots (i.e., pilots added to data) is an efficient alternative. Three different types of preprocessing algorithms are considered in this paper: Zero-Forcing Transmitter (ZFT), Maximum Ratio Transmitter (MRT), and Equal Gain Transmitter (EGT). The main advantage of MRT and EGT is that they do not require matrix inversions. Nevertheless, some level of interference is generated in the decoding process. Such interference is mitigated by employing an optimized iterative receiver. By employing the proposed implicit pilots, the performance of MRT and EGT is very close to the Matched Filter Bound just after a few iterations, even when the number of transmit or receiver antennas is not much higher than the number of data streams.

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

Massive multiple-input multiple-output (MIMO) systems have been drawing considerable interest. In the uplink, massive MIMO systems are commonly studied assuming that each base station (BS) decodes the signals of its user terminals separately and linearly while treating all interference as noise. Although this approach provides improved spectral efficiency (SE) in favorable channel conditions, it is generally sub-optimal from an information-theoretic perspective. In this work we characterize the SE of massive MIMO when the BSs are allowed to jointly decode the received signals. We consider two schemes for handling the interference, and derive their SEs for both finite and asymptotic number of antennas. Simulation tests of the proposed methods illustrate their gains in SE compared to standard separate linear decoding, and show that the standard approach fails to capture the actual achievable rates of massive MIMO systems, particularly when the interference is dominant.

In this work, we propose low-complexity detectors for massive multiple-input multiple-output (MIMO) systems. Particularly, we leverage variants of truncated polynomial expansion (TPE) in order to reduce the computational complexity of the signal detection in the uplink direction. Linear detectors such as zero-forcing (ZF) and minimum mean square error (MMSE) involve expensive matrix-matrix multiplication and matrix inversion operations. TPE-based detectors are appropriate candidates for approximating these linear detectors. However, tuning the normalization factor of TPE-based detectors may require calculating the minimum and the maximum eigenvalues of the channel Gram matrix. These calculations become computationally expensive for some massive MIMO systems, especially for systems with a large ratio of single-antenna user terminals to the number of antennas at the base station, i.e., loading factor. We propose to tune the normalization factor using appropriate approximations for the extreme eigenvalues. The proposed TPE-based detectors exhibit a bit error performance similar to that of the TPE-based detector with the optimal normalization factor. Moreover, our proposed detectors achieve the error performance of ZF and MMSE for different loading factors of spatially correlated and uncorrelated massive MIMO channels. The computational complexity of the proposed detector is proportional to the number of base station antennas and the number of users.

Cell-free massive multiple-input multiple-output (MIMO) system is a promising architecture for next generation wireless systems by deploying a very large number of distributed access points (APs), which simultaneously serve a smaller number of user equipments (UEs) over the same time-frequency resources. It guarantees uniformly good service at high spectral efficiency with simple linear precoding techniques and max-min power control. In this article, we propose a new joint maximum-ratio and zero-forcing (JMRZF) precoding scheme, where part of APs are combined to perform centralized zero-forcing (ZF), while other APs apply simple maximum-ratio transmission (MRT). Our proposed precoder offers an adaptable trade-off between the spectral efficiency and front-haul signalling overhead. A corresponding AP subset selection scheme is also proposed which is based on large-scale fading coefficients. A closed-form expression for the achievable spectral efficiency of our proposed scheme is derived, which represents a generalized result including both fully distributed MRT and fully centralized ZF cases. Based on this closed-form expression, max-min power control is formulated and solved via the second order cone and first order methods. The former can obtain the global optimal solution, but its computational complexity is very high. On the other hand, the latter technique is sub-optimal, yet, it has very low computational complexity. Hence, it is suitable for large-scale cell-free massive MIMO systems with hundreds or thousands of APs and users. Numerical results show that our proposed JMRZF scheme can substantially outperform the local precoding schemes, even when a small part of APs are combined to deploy ZF and is implementable even when each AP has very few antennas. In addition, it is shown that our max-min power controls improves the spectral efficiency significantly, compared to the uniform power control scheme.

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Massive Multiple Input Multiple Output (MIMO) systems can significantly improve the system performance and capacity by using a large number of antenna elements at the base station (BS). To reduce the system complexity and hardware cost, low complexity antenna selection techniques can be used to choose the best antenna subset while keeping the system performance at a certain required level. In this paper, Tabu Search (TS) and three bio-inspired optimization algorithms were used for antenna selection in Massive MIMO systems. The three bio-inspired algorithms were: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Bee Colony (ABC). Simulations showed promising results for the TS by achieving higher capacity with GA than PSO and ABC, and much shorter CPU time than any of the bio-inspired techniques.

Massive Multiple Input Multiple Output (MIMO) system is regarded as an emerging technology to enhance the capacity (achievable rate) of wireless communication systems. In this paper, we present an iterative algorithm that adapts transmit signal vector and power of the user using game theory to enhance the physical-layer security (achievable secrecy rate) of massive MIMO system in the presence of eavesdropper and jammers. The proposed algorithm takes account of impact of jammers while meeting the target Signal-to-Interference-plus-Noise Ratio (SINR) and the impact of both eavesdropper and jammers while evaluating the secrecy rate of the users. Furthermore, we present the existence and uniqueness of Nash equilibrium of the game used in this paper. We evaluate the performance of the algorithm using numerical results obtained from simulations. When both eavesdropper and jammers are present close to the users, secrecy rate of legitimate users degrade significantly.

Massive multiple-input multiple-output (MIMO) technology – employing base stations with dozens or hundreds of antennas – has emerged over the past decade from a theoretical concept into a cornerstone of 5G wireless communications. A key enabler of massive MIMO’s spectral efficiency gains is beamforming, which adapts the antenna array’s radiation pattern to direct signal energy toward intended users (and away from interferers). By coherently combining signals across a large antenna array, massive MIMO can simultaneously serve multiple users on the same time-frequency resource, dramatically increasing capacity. Early works showed that as the number of antennas grows, simple linear precoding (beamforming) schemes like maximum-ratio transmission or zero-forcing can achieve near-optimal performance, eliminating inter-user interference under favorable propagation. These revelations, along with pioneering concepts around 2010, spurred intensive research into practical beamforming strategies over the last ten years. This literature review provides a comprehensive overview of beamforming strategies in massive MIMO systems, spanning roughly 2015–2025, covering both theoretical advancements and real-world applications. We chart the evolution from conventional analog and digital beamforming to modern hybrid techniques, and discuss associated optimization methods.

Massive multiple-input multiple-output (MIMO) using a large number of antennas at both transmitter and receiver sides based on quasi-orthogonal space time block code (QOSTBC) is presented. Space-time block code (STBC) is a MIMO transmit strategy that applies transmit diversity and high reliability. QOSTBC is attractive because it achieves higher code rate than orthogonal STBC and lower decoding complexity than non-orthogonal STBC. We present the performance of massive MIMO systems using the QOSTBC with multiple antennas up to the 16 × 16 configuration. The performances of 2 × 2, 4 × 4, 8 × 8 and 16 × 16 massive MIMO systems have been presented. Simulation results show that the massive MIMO systems with QOSTBC give significant performance improvement with full rate and full diversity, compared with previously considered massive MIMO systems.