Massive multiple-input multiple-output MIMO is one of themost promising technologies for the next generation of wirelesscommunication networks because it has the potential to providegame-changing improvements in spectral efficiency SE and energyefficiency EE. This monograph summarizes many years ofresearch insights in a clear and self-contained way and providesthe reader with the necessary knowledge and mathematical toolsto carry out independent research in this area. Starting froma rigorous definition of Massive MIMO, the monograph coversthe important aspects of channel estimation, SE, EE, hardwareefficiency HE, and various practical deployment considerations.From the beginning, a very general, yet tractable, canonical systemmodel with spatial channel correlation is introduced. This modelis used to realistically assess the SE and EE, and is later extendedto also include the impact of hardware impairments. Owing tothis rigorous modeling approach, a lot of classic "wisdom" aboutMassive MIMO, based on too simplistic system models, is shownto be questionable.

https://www.nowpublishers.com/article/DownloadSummary/SIG-093

Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs), which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max–min power control algorithms. Max–min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly fivefold improvement in 95%-likely per-user throughput over the small-cell scheme, and tenfold improvement when shadow fading is correlated.

/pdf/cell-free-massive-mimo-versus-small-cells-49asq7q87u.pdf

Cell-Free Massive MIMO Versus Small Cells

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs)which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max-min power control algorithms. Max-min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. 
Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user throughput over the small-cell scheme, and 10-fold improvement when shadow fading is correlated.

Cell-Free Massive MIMO versus Small Cells

Cell-free Massive multiple-input multiple-output (MIMO) comprises a large number of distributed low-cost low-power single antenna access points (APs) connected to a network controller. The number of AP antennas is significantly larger than the number of users. The system is not partitioned into cells and each user is served by all APs simultaneously. The simplest linear precoding schemes are conjugate beamforming and zero-forcing. Max–min power control provides equal throughput to all users and is considered in this paper. Surprisingly, under max–min power control, most APs are found to transmit at less than full power. The zero-forcing precoder significantly outperforms conjugate beamforming. For zero-forcing, a near-optimal power control algorithm is developed that is considerably simpler than exact max–min power control. An alternative to cell-free systems is small-cell operation in which each user is served by only one AP for which power optimization algorithms are also developed. Cell-free Massive MIMO is shown to provide five- to ten-fold improvement in 95%-likely per-user throughput over small-cell operation.

Precoding and Power Optimization in Cell-Free Massive MIMO Systems

Massive MIMO is a reality—What is next?: Five promising research directions for antenna arrays

Cell-Free Massive MIMO systems comprise a large number of distributed, low cost, and low power access point antennas, connected to a network controller. The number of antennas is significantly larger than the number of users. The system is not partitioned into cells and each user is served by all access point antennas simultaneously. In this paper, we define cell-free systems and analyze algorithms for power optimization and linear pre-coding. Compared with the conventional small-cell scheme, Cell-Free Massive MIMO can yield more than ten-fold improvement in terms of 5%-outage rate.

Cell-Free Massive MIMO systems

Cell-Free Massive MIMO comprises a large number of distributed single-antenna access points (APs) serving a much smaller number of users. There is no partitioning into cells and each user is served by all APs. In this paper, the uplink performance of cell-free systems with minimum mean squared error (MMSE) and large scale fading decoding (LSFD) receivers is investigated. The main idea of LSFD receiver is to maximize achievable throughput using only large scale fading coefficients between APs and users. Capacity lower bounds for MMSE and LSFD receivers are derived. An asymptotic approximation for signal-to-interference-plus-noise ratio (SINR) of MMSE receiver is derived as a function of large scale fading coefficients only. The obtained approximation is accurate even for a small number of antennas. MMSE and LSFD receivers demonstrate five-fold and two-fold gains respectively over matched filter (MF) receiver in terms of 5%-outage rate.

Performance of cell-free massive MIMO systems with MMSE and LSFD receivers

Motivated by recent developments in time-division duplex massive multiple-input multiple-output (MIMO) systems, this paper investigates semi-blind channel estimation for multiuser MIMO systems. An expectation-maximization (EM) algorithm is derived for semi-blind channel estimation and a tractable EM algorithm is obtained by assuming a Gaussian distribution for the unknown data symbols, which improves channel estimates even when the data symbols are drawn from a finite constellation, such as quadrature phase-shift keying. An alternate EM algorithm is also derived by employing suitable priors on the channel coefficients and it is shown to outperform the EM algorithm with no priors in the low signal-to-noise ratio regime. To analytically understand the performance of the semi-blind scheme, Cramer–Rao bounds (CRBs) for semi-blind channel estimation are derived for deterministic and stochastic (Gaussian) data symbol models. To get insight into the behavior of a massive MIMO system, the asymptotic behavior of the CRBs as the number of antennas at the base station grows is analyzed. The numerical results show the benefits of semi-blind estimation algorithm as measured by the mean squared error. In particular, the performance of the EM algorithm becomes closer to the genie-aided maximum likelihood estimator based on known data symbols as the number of antennas increases. This result is consistent with the asymptotic analysis of the two CRBs indicating that semi-blind channel estimation for massive MIMO systems is very promising.

Semi-blind Channel Estimation for Multiuser Massive MIMO Systems

Cell-Free Massive MIMO comprises a large number of distributed single-antenna access points (APs) serving a much smaller number of users. There is no partitioning into cells and each user is served by all APs. 
In this paper, the uplink performance of cell-free systems with minimum mean squared error (MMSE) and large scale fading decoding (LSFD) receivers is investigated. The main idea of LSFD receiver is to maximize achievable throughput using only large scale fading coefficients between APs and users. Capacity lower bounds for MMSE and LSFD receivers are derived. An asymptotic approximation for signal-to-interference-plus-noise ratio (SINR) of MMSE receiver is derived as a function of large scale fading coefficients only. The obtained approximation is accurate even for a small number of antennas. MMSE and LSFD receivers demonstrate five-fold and two-fold gains respectively over matched filter (MF) receiver in terms of 5%-outage rate.

Elina Nayebi

Papers

Precoding and Power Optimization in Cell-Free Massive MIMO Systems

Cell-Free Massive MIMO systems

Performance of cell-free massive MIMO systems with MMSE and LSFD receivers

Semi-blind Channel Estimation for Multiuser Massive MIMO Systems

Performance of Cell-Free Massive MIMO Systems with MMSE and LSFD Receivers