4,516 research outputs found
MIMO Interference Alignment Over Correlated Channels with Imperfect CSI
Interference alignment (IA), given uncorrelated channel components and
perfect channel state information, obtains the maximum degrees of freedom in an
interference channel. Little is known, however, about how the sum rate of IA
behaves at finite transmit power, with imperfect channel state information, or
antenna correlation. This paper provides an approximate closed-form
signal-to-interference-plus-noise-ratio (SINR) expression for IA over
multiple-input-multiple-output (MIMO) channels with imperfect channel state
information and transmit antenna correlation. Assuming linear processing at the
transmitters and zero-forcing receivers, random matrix theory tools are
utilized to derive an approximation for the post-processing SINR distribution
of each stream for each user. Perfect channel knowledge and i.i.d. channel
coefficients constitute special cases. This SINR distribution not only allows
easy calculation of useful performance metrics like sum rate and symbol error
rate, but also permits a realistic comparison of IA with other transmission
techniques. More specifically, IA is compared with spatial multiplexing and
beamforming and it is shown that IA may not be optimal for some performance
criteria.Comment: 21 pages, 7 figures, submitted to IEEE Transactions on Signal
Processin
Interference Alignment with Limited Feedback on Two-cell Interfering Two-User MIMO-MAC
In this paper, we consider a two-cell interfering two-user multiple-input
multiple-output multiple access channel (MIMO-MAC) with limited feedback. We
first investigate the multiplexing gain of such channel when users have perfect
channel state information at transmitter (CSIT) by exploiting an interference
alignment scheme. In addition, we propose a feedback framework for the
interference alignment in the limited feedback system. On the basis of the
proposed feedback framework, we analyze the rate gap loss and it is shown that
in order to keep the same multiplexing gain with the case of perfect CSIT, the
number of feedback bits per receiver scales as , where and denote the number of
transmit antennas and a constant, respectively. Throughout the simulation
results, it is shown that the sum-rate performance coincides with the derived
results.Comment: 6 pages, 2 figures, Submitted ICC 201
Multi-Antenna Cooperative Wireless Systems: A Diversity-Multiplexing Tradeoff Perspective
We consider a general multiple antenna network with multiple sources,
multiple destinations and multiple relays in terms of the
diversity-multiplexing tradeoff (DMT). We examine several subcases of this most
general problem taking into account the processing capability of the relays
(half-duplex or full-duplex), and the network geometry (clustered or
non-clustered). We first study the multiple antenna relay channel with a
full-duplex relay to understand the effect of increased degrees of freedom in
the direct link. We find DMT upper bounds and investigate the achievable
performance of decode-and-forward (DF), and compress-and-forward (CF)
protocols. Our results suggest that while DF is DMT optimal when all terminals
have one antenna each, it may not maintain its good performance when the
degrees of freedom in the direct link is increased, whereas CF continues to
perform optimally. We also study the multiple antenna relay channel with a
half-duplex relay. We show that the half-duplex DMT behavior can significantly
be different from the full-duplex case. We find that CF is DMT optimal for
half-duplex relaying as well, and is the first protocol known to achieve the
half-duplex relay DMT. We next study the multiple-access relay channel (MARC)
DMT. Finally, we investigate a system with a single source-destination pair and
multiple relays, each node with a single antenna, and show that even under the
idealistic assumption of full-duplex relays and a clustered network, this
virtual multi-input multi-output (MIMO) system can never fully mimic a real
MIMO DMT. For cooperative systems with multiple sources and multiple
destinations the same limitation remains to be in effect.Comment: version 1: 58 pages, 15 figures, Submitted to IEEE Transactions on
Information Theory, version 2: Final version, to appear IEEE IT, title
changed, extra figures 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
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