279 research outputs found
Distributed MIMO Systems with Oblivious Antennas
A scenario in which a single source communicates with a single destination
via a distributed MIMO transceiver is considered. The source operates each of
the transmit antennas via finite-capacity links, and likewise the destination
is connected to the receiving antennas through capacity-constrained channels.
Targeting a nomadic communication scenario, in which the distributed MIMO
transceiver is designed to serve different standards or services, transmitters
and receivers are assumed to be oblivious to the encoding functions shared by
source and destination. Adopting a Gaussian symmetric interference network as
the channel model (as for regularly placed transmitters and receivers),
achievable rates are investigated and compared with an upper bound. It is
concluded that in certain asymptotic and non-asymptotic regimes obliviousness
of transmitters and receivers does not cause any loss of optimality.Comment: In Proc. of the 2008 IEEE International Symposium on Information
Theory (ISIT 2008), Toronto, Ontario, Canad
Optimal Channel Training in Uplink Network MIMO Systems
We consider a multi-cell frequency-selective fading uplink channel (network
MIMO) from K single-antenna user terminals (UTs) to B cooperative base stations
(BSs) with M antennas each. The BSs, assumed to be oblivious of the applied
codebooks, forward compressed versions of their observations to a central
station (CS) via capacity limited backhaul links. The CS jointly decodes the
messages from all UTs. Since the BSs and the CS are assumed to have no prior
channel state information (CSI), the channel needs to be estimated during its
coherence time. Based on a lower bound of the ergodic mutual information, we
determine the optimal fraction of the coherence time used for channel training,
taking different path losses between the UTs and the BSs into account. We then
study how the optimal training length is impacted by the backhaul capacity.
Although our analytical results are based on a large system limit, we show by
simulations that they provide very accurate approximations for even small
system dimensions.Comment: 15 pages, 7 figures. To appear in the IEEE Transactions on Signal
Processin
Integer-Forcing Linear Receivers
Linear receivers are often used to reduce the implementation complexity of
multiple-antenna systems. In a traditional linear receiver architecture, the
receive antennas are used to separate out the codewords sent by each transmit
antenna, which can then be decoded individually. Although easy to implement,
this approach can be highly suboptimal when the channel matrix is near
singular. This paper develops a new linear receiver architecture that uses the
receive antennas to create an effective channel matrix with integer-valued
entries. Rather than attempting to recover transmitted codewords directly, the
decoder recovers integer combinations of the codewords according to the entries
of the effective channel matrix. The codewords are all generated using the same
linear code which guarantees that these integer combinations are themselves
codewords. Provided that the effective channel is full rank, these integer
combinations can then be digitally solved for the original codewords. This
paper focuses on the special case where there is no coding across transmit
antennas and no channel state information at the transmitter(s), which
corresponds either to a multi-user uplink scenario or to single-user V-BLAST
encoding. In this setting, the proposed integer-forcing linear receiver
significantly outperforms conventional linear architectures such as the
zero-forcing and linear MMSE receiver. In the high SNR regime, the proposed
receiver attains the optimal diversity-multiplexing tradeoff for the standard
MIMO channel with no coding across transmit antennas. It is further shown that
in an extended MIMO model with interference, the integer-forcing linear
receiver achieves the optimal generalized degrees-of-freedom.Comment: 40 pages, 16 figures, to appear in the IEEE Transactions on
Information Theor
Media-Based MIMO: A New Frontier in Wireless Communications
The idea of Media-based Modulation (MBM), is based on embedding information
in the variations of the transmission media (channel state). This is in
contrast to legacy wireless systems where data is embedded in a Radio Frequency
(RF) source prior to the transmit antenna. MBM offers several advantages vs.
legacy systems, including "additivity of information over multiple receive
antennas", and "inherent diversity over a static fading channel". MBM is
particularly suitable for transmitting high data rates using a single transmit
and multiple receive antennas (Single Input-Multiple Output Media-Based
Modulation, or SIMO-MBM). However, complexity issues limit the amount of data
that can be embedded in the channel state using a single transmit unit. To
address this shortcoming, the current article introduces the idea of Layered
Multiple Input-Multiple Output Media-Based Modulation (LMIMO-MBM). Relying on a
layered structure, LMIMO-MBM can significantly reduce both hardware and
algorithmic complexities, as well as the training overhead, vs. SIMO-MBM.
Simulation results show excellent performance in terms of Symbol Error Rate
(SER) vs. Signal-to-Noise Ratio (SNR). For example, a LMIMO-MBM is
capable of transmitting bits of information per (complex) channel-use,
with SER at dB (or SER
at dB). This performance is achieved using a single transmission
and without adding any redundancy for Forward-Error-Correction (FEC). This
means, in addition to its excellent SER vs. energy/rate performance, MBM
relaxes the need for complex FEC structures, and thereby minimizes the
transmission delay. Overall, LMIMO-MBM provides a promising alternative to MIMO
and Massive MIMO for the realization of 5G wireless networks.Comment: 26 pages, 11 figures, additional examples are given to further
explain the idea of Media-Based Modulation. Capacity figure adde
The Practical Challenges of Interference Alignment
Interference alignment (IA) is a revolutionary wireless transmission strategy
that reduces the impact of interference. The idea of interference alignment is
to coordinate multiple transmitters so that their mutual interference aligns at
the receivers, facilitating simple interference cancellation techniques. Since
IA's inception, researchers have investigated its performance and proposed
improvements, verifying IA's ability to achieve the maximum degrees of freedom
(an approximation of sum capacity) in a variety of settings, developing
algorithms for determining alignment solutions, and generalizing transmission
strategies that relax the need for perfect alignment but yield better
performance. This article provides an overview of the concept of interference
alignment as well as an assessment of practical issues including performance in
realistic propagation environments, the role of channel state information at
the transmitter, and the practicality of interference alignment in large
networks.Comment: submitted to IEEE Wireless Communications Magazin
Uplink CoMP under a Constrained Backhaul and Imperfect Channel Knowledge
Coordinated Multi-Point (CoMP) is known to be a key technology for next
generation mobile communications systems, as it allows to overcome the burden
of inter-cell interference. Especially in the uplink, it is likely that
interference exploitation schemes will be used in the near future, as they can
be used with legacy terminals and require no or little changes in
standardization. Major drawbacks, however, are the extent of additional
backhaul infrastructure needed, and the sensitivity to imperfect channel
knowledge. This paper jointly addresses both issues in a new framework
incorporating a multitude of proposed theoretical uplink CoMP concepts, which
are then put into perspective with practical CoMP algorithms. This
comprehensive analysis provides new insight into the potential usage of uplink
CoMP in next generation wireless communications systems.Comment: Submitted to IEEE Transactions on Wireless Communications in February
201
Improving Macrocell - Small Cell Coexistence through Adaptive Interference Draining
The deployment of underlay small base stations (SBSs) is expected to
significantly boost the spectrum efficiency and the coverage of next-generation
cellular networks. However, the coexistence of SBSs underlaid to an existing
macro-cellular network faces important challenges, notably in terms of spectrum
sharing and interference management. In this paper, we propose a novel
game-theoretic model that enables the SBSs to optimize their transmission rates
by making decisions on the resource occupation jointly in the frequency and
spatial domains. This procedure, known as interference draining, is performed
among cooperative SBSs and allows to drastically reduce the interference
experienced by both macro- and small cell users. At the macrocell side, we
consider a modified water-filling policy for the power allocation that allows
each macrocell user (MUE) to focus the transmissions on the degrees of freedom
over which the MUE experiences the best channel and interference conditions.
This approach not only represents an effective way to decrease the received
interference at the MUEs but also grants the SBSs tier additional transmission
opportunities and allows for a more agile interference management. Simulation
results show that the proposed approach yields significant gains at both
macrocell and small cell tiers, in terms of average achievable rate per user,
reaching up to 37%, relative to the non-cooperative case, for a network with
150 MUEs and 200 SBSs
Energy Efficiency in MIMO Underlay and Overlay Device-to-Device Communications and Cognitive Radio Systems
This paper addresses the problem of resource allocation for systems in which
a primary and a secondary link share the available spectrum by an underlay or
overlay approach. After observing that such a scenario models both cognitive
radio and D2D communications, we formulate the problem as the maximization of
the secondary energy efficiency subject to a minimum rate requirement for the
primary user. This leads to challenging non-convex, fractional problems. In the
underlay scenario, we obtain the global solution by means of a suitable
reformulation. In the overlay scenario, two algorithms are proposed. The first
one yields a resource allocation fulfilling the first-order optimality
conditions of the resource allocation problem, by solving a sequence of easier
fractional problems. The second one enjoys a weaker optimality claim, but an
even lower computational complexity. Numerical results demonstrate the merits
of the proposed algorithms both in terms of energy-efficient performance and
complexity, also showing that the two proposed algorithms for the overlay
scenario perform very similarly, despite the different complexity.Comment: to appear in IEEE Transactions on Signal Processin
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