Massive MIMO has Unlimited Capacity

The capacity of cellular networks can be improved by the unprecedented array gain and spatial multiplexing offered by Massive MIMO. Since its inception, the coherent interference caused by pilot contamination has been believed to create a finite capacity limit, as the number of antennas goes to infinity. In this paper, we prove that this is incorrect and an artifact from using simplistic channel models and suboptimal precoding/combining schemes. We show that with multicell MMSE precoding/combining and a tiny amount of spatial channel correlation or large-scale fading variations over the array, the capacity increases without bound as the number of antennas increases, even under pilot contamination. More precisely, the result holds when the channel covariance matrices of the contaminating users are asymptotically linearly independent, which is generally the case. If also the diagonals of the covariance matrices are linearly independent, it is sufficient to know these diagonals (and not the full covariance matrices) to achieve an unlimited asymptotic capacity.Comment: To appear in IEEE Transactions on Wireless Communications, 17 pages, 7 figure

Pilot Decontamination over Time Frequency and Space Domains in Multi-Cell Massive MIMO System

In this article, we show that Pilot contamination problem can be seen as a source separation problem using time, frequency, and space domains. Our method capitalizes on a nonunitary joint diagonalization of spatial quadratic time-frequency (STFD) signal representation to identify the desired and interfering users. We first compute the noise subspace from the STFD matrices selected appropriately. Secondly, we use the noise subspace obtained to estimate the Elevation (El) and the Azimuth (Az) angles for which the MUSIC cost function is maximized. Numerical simulations are provided to illustrate the effectiveness and the behavior of the proposed approach

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Aerospace Medicine and Biology: A continuing supplement 180, May 1978

This special bibliography lists 201 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1978

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 142

This bibliography lists 256 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1975 for aerospace medicine and biology

Channel estimation in massive MIMO systems

Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference. The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity. This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes. System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance

Channel Estimation in Massive Multi-User MIMO Systems Based on Low-Rank Matrix Approximation

In recent years, massive Multi-User Multi-Input Multi-Output (MU-MIMO) system has attracted significant research interests in mobile communication systems. It has been considered as one of the promising technologies for 5G mobile wireless networks. In massive MU-MIMO system, the base station (BS) is equipped with a very large number of antenna elements and simultaneously serves a large number of single-antenna users. Compared to traditional MIMO system with fewer antennas, massive MU-MIMO system can offer many advantages such as significant improvements in both spectral and power efficiencies. However, the channel estimation in massive MU-MIMO system is particularly challenging due to large number of channel matrix entries to be estimated within a limited coherence time interval. This problem occurs in a single-cell case where both dimensions of the channel matrix grow large. Also, It happens in the multi-cell setting due to the pilot contamination effect. In this thesis, the problem of channel estimation in both single-cell and multi-cell time division duplex (TDD) massive MU-MIMO systems is studied. Thus, two-channel estimation namely “nuclear norm (NN)” and “iterative weighted nuclear norm (IWNN)” approximation techniques are proposed to solve the channel estimation problem in both systems. First, channel estimation in a single-cell TDD massive MU-MIMO system is formulated as a convex nuclear norm optimization problem with regularization parameter γ. In this study, the regularization parameter γ is selected based on the cross-validation (CV) curve method. The simulation results in terms of the normalized mean square error (NMSE) and uplink achievable sum-rate (ASR) are provided to show the effectiveness of the NN proposed scheme compared to the conventional least square (LS) estimator. Then, the IWNN approximation is proposed to improve the performance of the NN method. Thus, the channel estimation in a single-cell TDD massive MU-MIMO system is formulated as a weighted nuclear norm optimization problem. The simulation results show the effectiveness of the IWNN estimation approach compared to the standard NN and conventional LS estimation methods in terms of the NMSE and ASR. Second, both previous estimation techniques are extended to apply in a multi-cell TDD massive MU-MIMO system to mitigate pilot contamination effect. The simulation results in terms of the NMSE and uplink ASR show that the IWNN scheme outperforms the NN and LS estimations in the presence of high pilot contamination effect. Finally, a novel channel estimation scheme namely “Approximate minimum mean square error (AMMSE)” is proposed to reduce the computational complexity of the minimum mean square error (MMSE) estimator which was proposed for multi-cell TDD massive MU-MIMO system. Furthermore, a brief analysis of the computational complexity regarding the number of multiplications of the proposed AMMSE estimator is provided. It has been shown that the complexity of the proposed AMMSE estimator is reduced compared to the conventional MMSE estimator. The simulation results in terms of the NMSE and the uplink ASR performances show the proposed AMMSE estimation performance is almost the same as the conventional MMSE estimator under two different scenarios: noise-limited and pilot contamination