304 research outputs found
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
Foundations of User-Centric Cell-Free Massive MIMO
Imagine a coverage area where each mobile device is communicating with a
preferred set of wireless access points (among many) that are selected based on
its needs and cooperate to jointly serve it, instead of creating autonomous
cells. This effectively leads to a user-centric post-cellular network
architecture, which can resolve many of the interference issues and
service-quality variations that appear in cellular networks. This concept is
called User-centric Cell-free Massive MIMO (multiple-input multiple-output) and
has its roots in the intersection between three technology components: Massive
MIMO, coordinated multipoint processing, and ultra-dense networks. The main
challenge is to achieve the benefits of cell-free operation in a practically
feasible way, with computational complexity and fronthaul requirements that are
scalable to enable massively large networks with many mobile devices. This
monograph covers the foundations of User-centric Cell-free Massive MIMO,
starting from the motivation and mathematical definition. It continues by
describing the state-of-the-art signal processing algorithms for channel
estimation, uplink data reception, and downlink data transmission with either
centralized or distributed implementation. The achievable spectral efficiency
is mathematically derived and evaluated numerically using a running example
that exposes the impact of various system parameters and algorithmic choices.
The fundamental tradeoffs between communication performance, computational
complexity, and fronthaul signaling requirements are thoroughly analyzed.
Finally, the basic algorithms for pilot assignment, dynamic cooperation cluster
formation, and power optimization are provided, while open problems related to
these and other resource allocation problems are reviewed. All the numerical
examples can be reproduced using the accompanying Matlab code.Comment: This is the authors' version of the manuscript: \"Ozlem Tugfe Demir,
Emil Bj\"ornson and Luca Sanguinetti (2021), "Foundations of User-Centric
Cell-Free Massive MIMO", Foundations and Trends in Signal Processing: Vol.
14, No. 3-4, pp 162-47
Scalable Cell-Free Massive MIMO Systems
Imagine a coverage area with many wireless access points that cooperate to
jointly serve the users, instead of creating autonomous cells. Such a cell-free
network operation can potentially resolve many of the interference issues that
appear in current cellular networks. This ambition was previously called
Network MIMO (multiple-input multiple-output) and has recently reappeared under
the name Cell-Free Massive MIMO. The main challenge is to achieve the benefits
of cell-free operation in a practically feasible way, with computational
complexity and fronthaul requirements that are scalable to large networks with
many users. We propose a new framework for scalable Cell-Free Massive MIMO
systems by exploiting the dynamic cooperation cluster concept from the Network
MIMO literature. We provide a novel algorithm for joint initial access, pilot
assignment, and cluster formation that is proved to be scalable. Moreover, we
adapt the standard channel estimation, precoding, and combining methods to
become scalable. A new uplink and downlink duality is proved and used to
heuristically design the precoding vectors on the basis of the combining
vectors. Interestingly, the proposed scalable precoding and combining
outperform conventional maximum ratio processing and also performs closely to
the best unscalable alternatives.Comment: To appear in IEEE Transactions on Communications, 14 pages, 6 figure
Achieving Large Multiplexing Gain in Distributed Antenna Systems via Cooperation with pCell Technology
In this paper we present pCellTM technology, the first commercial-grade
wireless system that employs cooperation between distributed transceiver
stations to create concurrent data links to multiple users in the same
spectrum. First we analyze the per-user signal-to-interference-plus-noise ratio
(SINR) employing a geometrical spatial channel model to define volumes in space
of coherent signal around user antennas (or personal cells, i.e., pCells). Then
we describe the system architecture consisting of a general-purpose-processor
(GPP) based software-defined radio (SDR) wireless platform implementing a
real-time LTE protocol stack to communicate with off-the-shelf LTE devices.
Finally we present experimental results demonstrating up to 16 concurrent
spatial channels for an aggregate average spectral efficiency of 59.3 bps/Hz in
the downlink and 27.5 bps/Hz in the uplink, providing data rates of 200 Mbps
downlink and 25 Mbps uplink in 5 MHz of TDD spectrum.Comment: IEEE Asilomar Conference on Signals, Systems, and Computers, Nov.
8-11th 2015, Pacific Grove, CA, US
Joint Fronthaul Load Balancing and Computation Resource Allocation in Cell-Free User-Centric Massive MIMO Networks
We consider scalable cell-free massive multiple-input multiple-output
networks under an open radio access network paradigm comprising user equipments
(UEs), radio units (RUs), and decentralized processing units (DUs). UEs are
served by dynamically allocated user-centric clusters of RUs. The corresponding
cluster processors (implementing the physical layer for each user) are hosted
by the DUs as software-defined virtual network functions. Unlike the current
literature, mainly focused on the characterization of the user rates under
unrestricted fronthaul communication and computation, in this work we
explicitly take into account the fronthaul topology, the limited fronthaul
communication capacity, and computation constraints at the DUs. In particular,
we systematically address the new problem of joint fronthaul load balancing and
allocation of the computation resource. As a consequence of our new
optimization framework, we present representative numerical results
highlighting the existence of an optimal number of quantization bits in the
analog-to-digital conversion at the RUs.Comment: 13 pages, 5 figures, submitted to IEEE Transactions on Wireless
Communication
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