172 research outputs found
Performance Analysis of Cell-Free Massive MIMO Systems: A Stochastic Geometry Approach
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cell-free (CF) massive multiple-input-multiple-output (MIMO) has emerged as an alternative deployment for conventional cellular massive MIMO networks. As revealed by its name, this topology considers no cells, while a large number of multi-antenna access points (APs) serves simultaneously a smaller number of users over the same time/frequency resources through time-division duplex (TDD) operation. Prior works relied on the strong assumption (quite idealized) that the APs are uniformly distributed, and actually, this randomness was considered during the simulation and not in the analysis. However, in practice, ongoing and future networks become denser and increasingly irregular. Having this in mind, we consider that the AP locations are modeled by means of a Poisson point process (PPP) which is a more realistic model for the spatial randomness than a grid or uniform deployment. In particular, by virtue of stochastic geometry tools, we derive both the downlink coverage probability and achievable rate. Notably, this is the only work providing the coverage probability and shedding light on this aspect of CF massive MIMO systems. Focusing on the extraction of interesting insights, we consider small-cells (SCs) as a benchmark for comparison. Among the findings, CF massive MIMO systems achieve both higher coverage and rate with comparison to SCs due to the properties of favorable propagation, channel hardening, and interference suppression. Especially, we showed for both architectures that increasing the AP density results in a higher coverage which saturates after a certain value and increasing the number of users decreases the achievable rate but CF massive MIMO systems take advantage of the aforementioned properties, and thus, outperform SCs. In general, the performance gap between CF massive MIMO systems and SCs is enhanced by increasing the AP density. Another interesting observation concerns that a higher path-loss exponent decreases the rate while the users closer to the APs affect more the performance in terms of the rate.Peer reviewe
Ubiquitous Cell-Free Massive MIMO Communications
Since the first cellular networks were trialled in the 1970s, we have
witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic
growth has been managed by a combination of wider bandwidths, refined radio
interfaces, and network densification, namely increasing the number of antennas
per site. Due its cost-efficiency, the latter has contributed the most. Massive
MIMO (multiple-input multiple-output) is a key 5G technology that uses massive
antenna arrays to provide a very high beamforming gain and spatially
multiplexing of users, and hence, increases the spectral and energy efficiency.
It constitutes a centralized solution to densify a network, and its performance
is limited by the inter-cell interference inherent in its cell-centric design.
Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive
MIMO system implementing coherent user-centric transmission to overcome the
inter-cell interference limitation in cellular networks and provide additional
macro-diversity. These features, combined with the system scalability inherent
in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO
from prior coordinated distributed wireless systems. In this article, we
investigate the enormous potential of this promising technology while
addressing practical deployment issues to deal with the increased
back/front-hauling overhead deriving from the signal co-processing.Comment: Published in EURASIP Journal on Wireless Communications and
Networking on August 5, 201
Massive MIMO for Dependable Communication
Cellular communication is constantly evolving; currently 5G systems are being deployed and research towards 6G is ongoing. Three use cases have been discussed as enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communication (URLLC). To fulfill the requirements of these use cases, new technologies are needed and one enabler is massive multiple-input multiple-output (MIMO). By increasing the number of antennas at the base station side, data rates can be increased, more users can be served simultaneously, and there is a potential to improve reliability. In addition, it is possible to achieve better coverage, improved energy efficiency, and low-complex user devices. The performance of any wireless system is limited by the underlying channels. Massive MIMO channels have shown several beneficial properties: the array gain stemming from the combining of the signals from the many antennas, improved user separation due to favourable propagation -- where the user channels become pair-wise orthogonal -- and the channel hardening effect, where the variations of channel gain decreases as the number of antennas increases. Previous theoretical works have commonly assumed independent and identically distributed (i.i.d.) complex Gaussian channels. However, in the first studies on massive MIMO channels, it was shown that common outdoor and indoor environments are not that rich in scattering, but that the channels are rather spatially correlated. To enable the above use cases, investigations are needed for the targeted environments. This thesis focuses on the benefits of deploying massive MIMO systems to achieve dependable communication in a number of scenarios related to the use cases. The first main area is the study of an industrial environment and aims at characterizing and modeling massive MIMO channels to assess the possibility of achieving the requirements of URLLC in a factory context. For example, a unique fully distributed array is deployed with the aim to further exploit spatial diversity. The other main area concerns massive MIMO at sub-GHz, a previously unexplored area. The channel characteristics when deploying a physically very large array for IoT networks are explored. To conclude, massive MIMO can indeed bring great advantages when trying to achieve dependable communication. Although channels in regular indoor environments are not i.i.d. complex Gaussian, the model can be justified in rich scattering industrial environments. Due to massive MIMO, the small-scale fading effects are reduced and when deploying a distributed array also the large-scale fading effects are reduced. In the Internet-of-Things (IoT) scenario, the channel is not as rich scattering. In this use case one can benefit from the array gain to extend coverage and improved energy efficiency, and diversity is gained due to the physically large array
Channel Hardening in Massive MIMO: Model Parameters and Experimental Assessment
Reliability is becoming increasingly important for many applications
envisioned for future wireless systems. A technology that could improve
reliability in these systems is massive MIMO (Multiple-Input Multiple-Output).
One reason for this is a phenomenon called channel hardening, which means that
as the number of antennas in the system increases, the variations of channel
gain decrease in both the time- and frequency domain. Our analysis of channel
hardening is based on a joint comparison of theory, measurements and
simulations. Data from measurement campaigns including both indoor and outdoor
scenarios, as well as cylindrical and planar base station arrays, are analyzed.
The simulation analysis includes a comparison with the COST 2100 channel model
with its massive MIMO extension. The conclusion is that the COST 2100 model is
well suited to represent real scenarios, and provides a reasonable match to
actual measurements up to the uncertainty of antenna patterns and user
interaction. Also, the channel hardening effect in practical massive MIMO
channels is less pronounced than in complex independent and identically
distributed (i.i.d.) Gaussian channels, which are often considered in
theoretical work.Comment: Accepted to IEEE Open Journal of the Communications Societ
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
Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise
Cell-free Massive MIMO (multiple-input multiple-output) refers to a
distributed Massive MIMO system where all the access points (APs) cooperate to
coherently serve all the user equipments (UEs), suppress inter-cell
interference and mitigate the multiuser interference. Recent works demonstrated
that, unlike co-located Massive MIMO, the \textit{channel hardening} is, in
general, less pronounced in cell-free Massive MIMO, thus there is much to
benefit from estimating the downlink channel. In this study, we investigate the
gain introduced by the downlink beamforming training, extending the previously
proposed analysis to non-orthogonal uplink and downlink pilots. Assuming
single-antenna APs, conjugate beamforming and independent Rayleigh fading
channel, we derive a closed-form expression for the per-user achievable
downlink rate that addresses channel estimation errors and pilot contamination
both at the AP and UE side. The performance evaluation includes max-min
fairness power control, greedy pilot assignment methods, and a comparison
between achievable rates obtained from different capacity-bounding techniques.
Numerical results show that downlink beamforming training, although increases
pilot overhead and introduces additional pilot contamination, improves
significantly the achievable downlink rate. Even for large number of APs, it is
not fully efficient for the UE relying on the statistical channel state
information for data decoding.Comment: Published in IEEE Transactions on Wireless Communications on August
14, 2019. {\copyright} 2019 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other use
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.Comment: EEE Transactions on Wireless Communications, accepted for publicatio
On the application of massive mimo systems to machine type communications
This paper evaluates the feasibility of applying massive multiple-input multiple-output (MIMO) to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel, devised to exclusively consume data traffic from machine type communications (MTC) devices, the capacity (i.e., number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time-frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without the previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically, as well as the MMSE estimator, with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of the sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines toward a better understanding of the use of massive MIMO in MTC networks
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