1,761 research outputs found
Energy-Efficient Future Wireless Networks: A Marriage between Massive MIMO and Small Cells
How would a cellular network designed for high energy efficiency look like?
To answer this fundamental question, we model cellular networks using
stochastic geometry and optimize the energy efficiency with respect to the
density of base stations, the number of antennas and users per cell, the
transmit power levels, and the pilot reuse. The highest efficiency is neither
achieved by a pure small-cell approach, nor by a pure massive MIMO solution.
Interestingly, it is the combination of these approaches that provides the
highest energy efficiency; small cells contributes by reducing the propagation
losses while massive MIMO enables multiplexing of users with controlled
interference.Comment: Published at IEEE Workshop on Signal Processing Advances in Wireless
Communications (SPAWC 2015), 5 pages, 5 figure
Can Hardware Distortion Correlation be Neglected When Analyzing Uplink SE in Massive MIMO?
This paper analyzes how the distortion created by hardware impairments in a
multiple-antenna base station affects the uplink spectral efficiency (SE), with
focus on Massive MIMO. The distortion is correlated across the antennas, but
has been often approximated as uncorrelated to facilitate (tractable) SE
analysis. To determine when this approximation is accurate, basic properties of
the distortion correlation are first uncovered. Then, we focus on third-order
non-linearities and prove analytically and numerically that the correlation can
be neglected in the SE analysis when there are many users. In i.i.d. Rayleigh
fading with equal signal-to-noise ratios, this occurs when having five users.Comment: 5 pages, 3 figures, IEEE International Workshop on Signal Processing
Advances in Wireless Communications (SPAWC), 201
Spectral and Energy Efficiency of Superimposed Pilots in Uplink Massive MIMO
Next generation wireless networks aim at providing substantial improvements
in spectral efficiency (SE) and energy efficiency (EE). Massive MIMO has been
proved to be a viable technology to achieve these goals by spatially
multiplexing several users using many base station (BS) antennas. A potential
limitation of Massive MIMO in multicell systems is pilot contamination, which
arises in the channel estimation process from the interference caused by
reusing pilots in neighboring cells. A standard method to reduce pilot
contamination, known as regular pilot (RP), is to adjust the length of pilot
sequences while transmitting data and pilot symbols disjointly. An alternative
method, called superimposed pilot (SP), sends a superposition of pilot and data
symbols. This allows to use longer pilots which, in turn, reduces pilot
contamination. We consider the uplink of a multicell Massive MIMO network using
maximum ratio combining detection and compare RP and SP in terms of SE and EE.
To this end, we derive rigorous closed-form achievable rates with SP under a
practical random BS deployment. We prove that the reduction of pilot
contamination with SP is outweighed by the additional coherent and non-coherent
interference. Numerical results show that when both methods are optimized, RP
achieves comparable SE and EE to SP in practical scenarios.Comment: 32 pages, 12 figures, 3 tables. Submitted in March 2017 to IEEE
Transactions on Wireless Communication
Fundamental Asymptotic Behavior of (Two-User) Distributed Massive MIMO
This paper considers the uplink of a distributed Massive MIMO network where
base stations (BSs), each equipped with antennas, receive data from
users. We study the asymptotic spectral efficiency (as )
with spatial correlated channels, pilot contamination, and different degrees of
channel state information (CSI) and statistical knowledge at the BSs. By
considering a two-user setup, we can simply derive fundamental asymptotic
behaviors and provide novel insights into the structure of the optimal
combining schemes. In line with [1], when global CSI is available at all BSs,
the optimal minimum-mean squared error combining has an unbounded capacity as
, if the global channel covariance matrices of the users are
asymptotically linearly independent. This result is instrumental to derive a
suboptimal combining scheme that provides unbounded capacity as
using only local CSI and global channel statistics. The latter scheme is shown
to outperform a generalized matched filter scheme, which also achieves
asymptotic unbounded capacity by using only local CSI and global channel
statistics, but is derived following [2] on the basis of a more conservative
capacity bound.Comment: 6 pages, 2 figures, to be presented at GLOBECOM 2018, Abu Dhab
Massive MIMO has Unlimited Capacity
The capacity of cellular networks can be improved by the unprecedented array
gain and spatial multiplexing offered by Massive MIMO. Since its inception, the
coherent interference caused by pilot contamination has been believed to create
a finite capacity limit, as the number of antennas goes to infinity. In this
paper, we prove that this is incorrect and an artifact from using simplistic
channel models and suboptimal precoding/combining schemes. We show that with
multicell MMSE precoding/combining and a tiny amount of spatial channel
correlation or large-scale fading variations over the array, the capacity
increases without bound as the number of antennas increases, even under pilot
contamination. More precisely, the result holds when the channel covariance
matrices of the contaminating users are asymptotically linearly independent,
which is generally the case. If also the diagonals of the covariance matrices
are linearly independent, it is sufficient to know these diagonals (and not the
full covariance matrices) to achieve an unlimited asymptotic capacity.Comment: To appear in IEEE Transactions on Wireless Communications, 17 pages,
7 figure
Deploying Dense Networks for Maximal Energy Efficiency: Small Cells Meet Massive MIMO
How would a cellular network designed for maximal energy efficiency look
like? To answer this fundamental question, tools from stochastic geometry are
used in this paper to model future cellular networks and obtain a new lower
bound on the average uplink spectral efficiency. This enables us to formulate a
tractable uplink energy efficiency (EE) maximization problem and solve it
analytically with respect to the density of base stations (BSs), the transmit
power levels, the number of BS antennas and users per cell, and the pilot reuse
factor. The closed-form expressions obtained from this general EE maximization
framework provide valuable insights on the interplay between the optimization
variables, hardware characteristics, and propagation environment. Small cells
are proved to give high EE, but the EE improvement saturates quickly with the
BS density. Interestingly, the maximal EE is achieved by also equipping the BSs
with multiple antennas and operate in a "massive MIMO" fashion, where the array
gain from coherent detection mitigates interference and the multiplexing of
many users reduces the energy cost per user.Comment: To appear in IEEE Journal on Selected Areas in Communications, 15
pages, 7 figures, 1 tabl
Designing Wireless Broadband Access for Energy Efficiency: Are Small Cells the Only Answer?
The main usage of cellular networks has changed from voice to data traffic,
mostly requested by static users. In this paper, we analyze how a cellular
network should be designed to provide such wireless broadband access with
maximal energy efficiency (EE). Using stochastic geometry and a detailed power
consumption model, we optimize the density of access points (APs), number of
antennas and users per AP, and transmission power for maximal EE. Small cells
are of course a key technology in this direction, but the analysis shows that
the EE improvement of a small-cell network saturates quickly with the AP
density and then "massive MIMO" techniques can further improve the EE.Comment: Published at Small Cell and 5G Networks (SmallNets) Workshop, IEEE
International Conference on Communications (ICC), 6 pages, 5 figures, 1 tabl
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