492 research outputs found
Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors
Iterative reweighted algorithms, as a class of algorithms for sparse signal
recovery, have been found to have better performance than their non-reweighted
counterparts. However, for solving the problem of multiple measurement vectors
(MMVs), all the existing reweighted algorithms do not account for temporal
correlation among source vectors and thus their performance degrades
significantly in the presence of correlation. In this work we propose an
iterative reweighted sparse Bayesian learning (SBL) algorithm exploiting the
temporal correlation, and motivated by it, we propose a strategy to improve
existing reweighted algorithms for the MMV problem, i.e. replacing
their row norms with Mahalanobis distance measure. Simulations show that the
proposed reweighted SBL algorithm has superior performance, and the proposed
improvement strategy is effective for existing reweighted algorithms.Comment: Accepted by ICASSP 201
Random Beamforming with Heterogeneous Users and Selective Feedback: Individual Sum Rate and Individual Scaling Laws
This paper investigates three open problems in random beamforming based
communication systems: the scheduling policy with heterogeneous users, the
closed form sum rate, and the randomness of multiuser diversity with selective
feedback. By employing the cumulative distribution function based scheduling
policy, we guarantee fairness among users as well as obtain multiuser diversity
gain in the heterogeneous scenario. Under this scheduling framework, the
individual sum rate, namely the average rate for a given user multiplied by the
number of users, is of interest and analyzed under different feedback schemes.
Firstly, under the full feedback scheme, we derive the closed form individual
sum rate by employing a decomposition of the probability density function of
the selected user's signal-to-interference-plus-noise ratio. This technique is
employed to further obtain a closed form rate approximation with selective
feedback in the spatial dimension. The analysis is also extended to random
beamforming in a wideband OFDMA system with additional selective feedback in
the spectral dimension wherein only the best beams for the best-L resource
blocks are fed back. We utilize extreme value theory to examine the randomness
of multiuser diversity incurred by selective feedback. Finally, by leveraging
the tail equivalence method, the multiplicative effect of selective feedback
and random observations is observed to establish the individual rate scaling.Comment: Submitted in March 2012. To appear in IEEE Transactions on Wireless
Communications. Part of this paper builds upon the following letter: Y. Huang
and B. D. Rao, "Closed form sum rate of random beamforming", IEEE Commun.
Lett., vol. 16, no. 5, pp. 630-633, May 201
An Analytical Framework for Heterogeneous Partial Feedback Design in Heterogeneous Multicell OFDMA Networks
The inherent heterogeneous structure resulting from user densities and large
scale channel effects motivates heterogeneous partial feedback design in
heterogeneous networks. In such emerging networks, a distributed scheduling
policy which enjoys multiuser diversity as well as maintains fairness among
users is favored for individual user rate enhancement and guarantees. For a
system employing the cumulative distribution function based scheduling, which
satisfies the two above mentioned desired features, we develop an analytical
framework to investigate heterogeneous partial feedback in a general
OFDMA-based heterogeneous multicell employing the best-M partial feedback
strategy. Exact sum rate analysis is first carried out and closed form
expressions are obtained by a novel decomposition of the probability density
function of the selected user's signal-to-interference-plus-noise ratio. To
draw further insight, we perform asymptotic analysis using extreme value theory
to examine the effect of partial feedback on the randomness of multiuser
diversity, show the asymptotic optimality of best-1 feedback, and derive an
asymptotic approximation for the sum rate in order to determine the minimum
required partial feedback.Comment: To appear in IEEE Trans. on Signal Processin
Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation
We examine the recovery of block sparse signals and extend the framework in
two important directions; one by exploiting signals' intra-block correlation
and the other by generalizing signals' block structure. We propose two families
of algorithms based on the framework of block sparse Bayesian learning (BSBL).
One family, directly derived from the BSBL framework, requires knowledge of the
block structure. Another family, derived from an expanded BSBL framework, is
based on a weaker assumption on the block structure, and can be used when the
block structure is completely unknown. Using these algorithms we show that
exploiting intra-block correlation is very helpful in improving recovery
performance. These algorithms also shed light on how to modify existing
algorithms or design new ones to exploit such correlation and improve
performance.Comment: Matlab codes can be downloaded at:
https://sites.google.com/site/researchbyzhang/bsbl, or
http://dsp.ucsd.edu/~zhilin/BSBL.htm
Performance Analysis of Heterogeneous Feedback Design in an OFDMA Downlink with Partial and Imperfect Feedback
Current OFDMA systems group resource blocks into subband to form the basic
feedback unit. Homogeneous feedback design with a common subband size is not
aware of the heterogeneous channel statistics among users. Under a general
correlated channel model, we demonstrate the gain of matching the subband size
to the underlying channel statistics motivating heterogeneous feedback design
with different subband sizes and feedback resources across clusters of users.
Employing the best-M partial feedback strategy, users with smaller subband size
would convey more partial feedback to match the frequency selectivity. In order
to develop an analytical framework to investigate the impact of partial
feedback and potential imperfections, we leverage the multi-cluster subband
fading model. The perfect feedback scenario is thoroughly analyzed, and the
closed form expression for the average sum rate is derived for the
heterogeneous partial feedback system. We proceed to examine the effect of
imperfections due to channel estimation error and feedback delay, which leads
to additional consideration of system outage. Two transmission strategies: the
fix rate and the variable rate, are considered for the outage analysis. We also
investigate how to adapt to the imperfections in order to maximize the average
goodput under heterogeneous partial feedback.Comment: To appear in IEEE Trans. on Signal Processin
Support Recovery of Sparse Signals
We consider the problem of exact support recovery of sparse signals via noisy
measurements. The main focus is the sufficient and necessary conditions on the
number of measurements for support recovery to be reliable. By drawing an
analogy between the problem of support recovery and the problem of channel
coding over the Gaussian multiple access channel, and exploiting mathematical
tools developed for the latter problem, we obtain an information theoretic
framework for analyzing the performance limits of support recovery. Sharp
sufficient and necessary conditions on the number of measurements in terms of
the signal sparsity level and the measurement noise level are derived.
Specifically, when the number of nonzero entries is held fixed, the exact
asymptotics on the number of measurements for support recovery is developed.
When the number of nonzero entries increases in certain manners, we obtain
sufficient conditions tighter than existing results. In addition, we show that
the proposed methodology can deal with a variety of models of sparse signal
recovery, hence demonstrating its potential as an effective analytical tool.Comment: 33 page
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