426 research outputs found
Channel Hardening in Massive MIMO - A Measurement Based Analysis
Wireless-controlled robots, cars and other critical applications are in need
of technologies that offer high reliability and low latency. Massive MIMO,
Multiple-Input Multiple-Output, is a key technology for the upcoming 5G systems
and is one part of the solution to increase the reliability of wireless
systems. More specifically, when increasing the number of base station antennas
in a massive MIMO systems the channel variations decrease and the so-called
channel hardening effect appears. This means that the variations of the channel
gain in time and frequency decrease. In this paper, channel hardening in
massive MIMO systems is assessed based on analysis of measurement data. For an
indoor scenario, the channels are measured with a 128-port cylindrical array
for nine single-antenna users. The analysis shows that in a real scenario a
channel hardening of 3.2-4.6 dB, measured as a reduction of the standard
deviation of the channel gain, can be expected depending on the amount of user
interaction. Also, some practical implications and insights are presented.Comment: Accepted to SPAWC 201
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
Temporal Analysis of Measured LOS Massive MIMO Channels with Mobility
The first measured results for massive multiple-input, multiple-output (MIMO)
performance in a line-of-sight (LOS) scenario with moderate mobility are
presented, with 8 users served by a 100 antenna base Station (BS) at 3.7 GHz.
When such a large number of channels dynamically change, the inherent
propagation and processing delay has a critical relationship with the rate of
change, as the use of outdated channel information can result in severe
detection and precoding inaccuracies. For the downlink (DL) in particular, a
time division duplex (TDD) configuration synonymous with massive MIMO
deployments could mean only the uplink (UL) is usable in extreme cases.
Therefore, it is of great interest to investigate the impact of mobility on
massive MIMO performance and consider ways to combat the potential limitations.
In a mobile scenario with moving cars and pedestrians, the correlation of the
MIMO channel vector over time is inspected for vehicles moving up to 29 km/h.
For a 100 antenna system, it is found that the channel state information (CSI)
update rate requirement may increase by 7 times when compared to an 8 antenna
system, whilst the power control update rate could be decreased by at least 5
times relative to a single antenna system.Comment: Accepted for presentation at the 85th IEEE Vehicular Technology
Conference in Sydney. 5 Pages. arXiv admin note: substantial text overlap
with arXiv:1701.0881
Fingerprinting-Based Positioning in Distributed Massive MIMO Systems
Location awareness in wireless networks may enable many applications such as
emergency services, autonomous driving and geographic routing. Although there
are many available positioning techniques, none of them is adapted to work with
massive multiple-in-multiple-out (MIMO) systems, which represent a leading 5G
technology candidate. In this paper, we discuss possible solutions for
positioning of mobile stations using a vector of signals at the base station,
equipped with many antennas distributed over deployment area. Our main proposal
is to use fingerprinting techniques based on a vector of received signal
strengths. This kind of methods are able to work in highly-cluttered multipath
environments, and require just one base station, in contrast to standard
range-based and angle-based techniques. We also provide a solution for
fingerprinting-based positioning based on Gaussian process regression, and
discuss main applications and challenges.Comment: Proc. of IEEE 82nd Vehicular Technology Conference (VTC2015-Fall
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin
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