946 research outputs found
Massive MIMO for Next Generation Wireless Systems
Multi-user Multiple-Input Multiple-Output (MIMO) offers big advantages over
conventional point-to-point MIMO: it works with cheap single-antenna terminals,
a rich scattering environment is not required, and resource allocation is
simplified because every active terminal utilizes all of the time-frequency
bins. However, multi-user MIMO, as originally envisioned with roughly equal
numbers of service-antennas and terminals and frequency division duplex
operation, is not a scalable technology. Massive MIMO (also known as
"Large-Scale Antenna Systems", "Very Large MIMO", "Hyper MIMO", "Full-Dimension
MIMO" & "ARGOS") makes a clean break with current practice through the use of a
large excess of service-antennas over active terminals and time division duplex
operation. Extra antennas help by focusing energy into ever-smaller regions of
space to bring huge improvements in throughput and radiated energy efficiency.
Other benefits of massive MIMO include the extensive use of inexpensive
low-power components, reduced latency, simplification of the media access
control (MAC) layer, and robustness to intentional jamming. The anticipated
throughput depend on the propagation environment providing asymptotically
orthogonal channels to the terminals, but so far experiments have not disclosed
any limitations in this regard. While massive MIMO renders many traditional
research problems irrelevant, it uncovers entirely new problems that urgently
need attention: the challenge of making many low-cost low-precision components
that work effectively together, acquisition and synchronization for
newly-joined terminals, the exploitation of extra degrees of freedom provided
by the excess of service-antennas, reducing internal power consumption to
achieve total energy efficiency reductions, and finding new deployment
scenarios. This paper presents an overview of the massive MIMO concept and
contemporary research.Comment: Final manuscript, to appear in IEEE Communications Magazin
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
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
A survey and tutorial of electromagnetic radiation and reduction in mobile communication systems
This paper provides a survey and tutorial of electromagnetic (EM) radiation exposure and reduction in mobile communication systems. EM radiation exposure has received a fair share of interest in the literature; however, this work is one of the first to compile the most interesting results and ideas related to EM exposure in mobile communication systems and present possible ways of reducing it. We provide a comprehensive survey of existing literature and also offer a tutorial on the dosimetry, metrics, international projects as well as guidelines and limits on the exposure from EM radiation in mobile communication systems. Based on this survey and given that EM radiation exposure is closely linked with specific absorption rate (SAR) and transmit power usage, we propose possible techniques for reducing EM radiation exposure in mobile communication systems by exploring known concepts related to SAR and transmit power reduction in mobile systems. Thus, this paper serves as an introductory guide to EM radiation exposure in mobile communication systems and provides insights toward the design of future low-EM exposure mobile communication networks
A New Look at Cell-Free Massive MIMO: Making It Practical With Dynamic Cooperation
This paper takes a new look at Cell-free Massive MIMO (multiple-input
multiple-output) through the lens of the dynamic cooperation cluster framework
from the Network MIMO literature. The purpose is to identify and address
scalability issues that appear in prior work. We provide distributed algorithms
for initial access, pilot assignment, cluster formation, precoding, and
combining that are scalable in the sense of being implementable with
arbitrarily many users. Interestingly, the suggested precoding and combining
outperform conjugate beamforming and matched filtering, respectively, while
also being fully distributed.Comment: To appear at the 2019 IEEE International Symposium on Personal,
Indoor and Mobile Radio Communications (IEEE PIMRC 2019), 6 pages, 5 figure
Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms
Algorithms for Massive MIMO uplink detection and downlink precoding typically
rely on a centralized approach, by which baseband data from all antenna modules
are routed to a central node in order to be processed. In the case of Massive
MIMO, where hundreds or thousands of antennas are expected in the base-station,
said routing becomes a bottleneck since interconnection throughput is limited.
This paper presents a fully decentralized architecture and an algorithm for
Massive MIMO uplink detection and downlink precoding based on the Stochastic
Gradient Descent (SGD) method, which does not require a central node for these
tasks. Through a recursive approach and very low complexity operations, the
proposed algorithm provides a good trade-off between performance,
interconnection throughput and latency. Further, our proposed solution achieves
significantly lower interconnection data-rate than other architectures,
enabling future scalability.Comment: Manuscript accepted for publication in IEEE Transactions on Signal
Processin
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