4,590 research outputs found
Large System Analysis of the Energy Consumption Distribution in Multi-User MIMO Systems with Mobility
In this work, we consider the downlink of a single-cell multi-user MIMO
system in which the base station (BS) makes use of antennas to communicate
with single-antenna user equipments (UEs). The UEs move around in the cell
according to a random walk mobility model. We aim at determining the energy
consumption distribution when different linear precoding techniques are used at
the BS to guarantee target rates within a finite time interval . The
analysis is conducted in the asymptotic regime where and grow large
with fixed ratio under the assumption of perfect channel state information
(CSI). Both recent and standard results from large system analysis are used to
provide concise formulae for the asymptotic transmit powers and beamforming
vectors for all considered schemes. These results are eventually used to
provide a deterministic approximation of the energy consumption and to study
its fluctuations around this value in the form of a central limit theorem.
Closed-form expressions for the asymptotic means and variances are given.
Numerical results are used to validate the accuracy of the theoretical analysis
and to make comparisons. We show how the results can be used to approximate the
probability that a battery-powered BS runs out of energy and also to design the
cell radius for minimizing the energy consumption per unit area. The imperfect
CSI case is also briefly considered.Comment: 8 figures, 2 tables, to appear on IEEE Transactions on Wireless
Communication
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
Energy Consumption in multi-user MIMO systems: Impact of user mobility
In this work, we consider the downlink of a single-cell multi-user
multiple-input multiple-output system in which zero-forcing precoding is used
at the base station (BS) to serve a certain number of user equipments (UEs). A
fixed data rate is guaranteed at each UE. The UEs move around in the cell
according to a Brownian motion, thus the path losses change over time and the
energy consumption fluctuates accordingly. We aim at determining the
distribution of the energy consumption. To this end, we analyze the asymptotic
regime where the number of antennas at the BS and the number of UEs grow large
with a given ratio. It turns out that the energy consumption is asymptotically
a Gaussian random variable whose mean and variance are derived analytically.
These results can, for example, be used to approximate the probability that a
battery-powered BS runs out of energy within a certain time period.Comment: 5 pages, 3 figures, 1 table, conference. IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2014
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
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
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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