1,707 research outputs found
Effects of Mobility on User Energy Consumption and Total Throughput in a Massive MIMO System
Macroscopic mobility of wireless users is important to determine the
performance and energy effciency of a wireless network, because of the temporal
correlations it introduces in the consumed power and throughput. In this work
we introduce a methodology that obtains the long time statistics of such
metrics in a network. After describing the general approach, we present a
specific example of the uplink channel of a mobile user in the vicinity of a
massive MIMO base-station antenna array. To guarantee a fixed SINR and rate,
the user inverts the path-loss channel power, while moving around in the cell.
To calculate the long time distribution of the consumed energy of the user, we
assume his movement follows a Brownian motion, and then map the problem to the
solution of the minimum eigenvalue of a partial differential equation, which
can be solved either analytically, or numerically very fast. We also treat the
throughput of a single user. We then discuss the results and how they can be
generalized if the mobility is assumed to be a Levy random walk. We also
provide a roadmap to use this technique when one considers multiple users and
base stations.Comment: Submitted to ITW 201
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
Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks
Conventional cellular wireless networks were designed with the purpose of
providing high throughput for the user and high capacity for the service
provider, without any provisions of energy efficiency. As a result, these
networks have an enormous Carbon footprint. In this paper, we describe the
sources of the inefficiencies in such networks. First we present results of the
studies on how much Carbon footprint such networks generate. We also discuss
how much more mobile traffic is expected to increase so that this Carbon
footprint will even increase tremendously more. We then discuss specific
sources of inefficiency and potential sources of improvement at the physical
layer as well as at higher layers of the communication protocol hierarchy. In
particular, considering that most of the energy inefficiency in cellular
wireless networks is at the base stations, we discuss multi-tier networks and
point to the potential of exploiting mobility patterns in order to use base
station energy judiciously. We then investigate potential methods to reduce
this inefficiency and quantify their individual contributions. By a
consideration of the combination of all potential gains, we conclude that an
improvement in energy consumption in cellular wireless networks by two orders
of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843
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
Interference Management in 5G Reverse TDD HetNets with Wireless Backhaul: A Large System Analysis
This work analyzes a heterogeneous network (HetNet), which comprises a macro
base station (BS) equipped with a large number of antennas and an overlaid
dense tier of small cell access points (SCAs) using a wireless backhaul for
data traffic. The static and low mobility user equipment terminals (UEs) are
associated with the SCAs while those with medium-to-high mobility are served by
the macro BS. A reverse time division duplexing (TDD) protocol is used by the
two tiers, which allows the BS to locally estimate both the intra-tier and
inter-tier channels. This knowledge is then used at the BS either in the uplink
(UL) or in the downlink (DL) to simultaneously serve the macro UEs (MUEs) and
to provide the wireless backhaul to SCAs. A geographical separation of
co-channel SCAs is proposed to limit the interference coming from the UL
signals of MUEs. A concatenated linear precoding technique employing either
zero-forcing (ZF) or regularized ZF is used at the BS to simultaneously serve
MUEs and SCAs in DL while nulling interference toward those SCAs in UL. We
evaluate and characterize the performance of the system through the power
consumption of UL and DL transmissions under the assumption that target rates
must be satisfied and imperfect channel state information is available for
MUEs. The analysis is conducted in the asymptotic regime where the number of BS
antennas and the network size (MUEs and SCAs) grow large with fixed ratios.
Results from large system analysis are used to provide concise formulae for the
asymptotic UL and DL transmit powers and precoding vectors under the above
assumptions. Numerical results are used to validate the analysis in different
settings and to make comparisons with alternative network architectures.Comment: 14 pages, 12 figures. To appear IEEE J. Select. Areas Commun. --
Special Issue on HetNet
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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