arXiv subjects

Bhaskar D. Rao

Publications and source records attributed to Bhaskar D. Rao.

46 records · Page 3Linked to original sources

arXiv2013

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.

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arXiv2013

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.

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arXiv2012

Sparse Signal Recovery in the Presence of Intra-Vector and Inter-Vector Correlation

This work discusses the problem of sparse signal recovery when there is correlation among the values of non-zero entries. We examine intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector model, as well as their combination. Algorithms based on the sparse Bayesian learning are presented and the benefits of incorporating correlation at the algorithm level are discussed. The impact of correlation on the limits of support recovery is also discussed highlighting the different impact intra-vector and inter-vector correlations have on such limits.

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arXiv2011

On the optimal frequency selectivity to maximize multiuser diversity in an OFDMA scheduling system

We consider an orthogonal frequency division multiple access (OFDMA) scheduling system. A scheduling unit block consists of contiguous multiple subcarriers. Users are scheduled based on their block average throughput in a proportional fair way. The multiuser diversity gain increases with the degree and dynamic range of channel fluctuations. %Lack of diversity in a limited frequency selective channel may decrease the sum rate. However, a decrease of the block average throughput in a too much selective channel may lessen the sum rate as well. In this paper, we first study optimal channel selectivity in view of maximizing the maximum of the block average throughput of an arbitrary user. Based on this study, we then propose a method to determine a per-user optimal cyclic delay when cyclic delay diversity (CDD) is used to enhance the sum rate by increasing channel selectivity for a limited fluctuating channel. We show that the proposed technique achieves better performance than a conventional fixed cyclic delay scheme and that the throughput is very close to the optimal sum rate possible with CDD.

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arXiv2011

Support Recovery of Sparse Signals in the Presence of Multiple Measurement Vectors

This paper studies the problem of support recovery of sparse signals based on multiple measurement vectors (MMV). The MMV support recovery problem is connected to the problem of decoding messages in a Single-Input Multiple-Output (SIMO) multiple access channel (MAC), thereby enabling an information theoretic framework for analyzing performance limits in recovering the support of sparse signals. Sharp sufficient and necessary conditions for successful support recovery are derived in terms of the number of measurements per measurement vector, the number of nonzero rows, the measurement noise level, and especially the number of measurement vectors. Through the interpretations of the results, in particular the connection to the multiple output communication system, the benefit of having MMV for sparse signal recovery is illustrated providing a theoretical foundation to the performance improvement enabled by MMV as observed in many existing simulation results. In particular, it is shown that the structure (rank) of the matrix formed by the nonzero entries plays an important role on the performance limits of support recovery.

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arXiv2011

Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning

We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlations and thus their performance degrades significantly with the correlations. In this work, we propose a block sparse Bayesian learning framework which models the temporal correlations. In this framework we derive two sparse Bayesian learning (SBL) algorithms, which have superior recovery performance compared to existing algorithms, especially in the presence of high temporal correlations. Furthermore, our algorithms are better at handling highly underdetermined problems and require less row-sparsity on the solution matrix. We also provide analysis of the global and local minima of their cost function, and show that the SBL cost function has the very desirable property that the global minimum is at the sparsest solution to the MMV problem. Extensive experiments also provide some interesting results that motivate future theoretical research on the MMV model.

stat.ML↗

arXiv2011

Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity

A trend in compressed sensing (CS) is to exploit structure for improved reconstruction performance. In the basic CS model, exploiting the clustering structure among nonzero elements in the solution vector has drawn much attention, and many algorithms have been proposed. However, few algorithms explicitly consider correlation within a cluster. Meanwhile, in the multiple measurement vector (MMV) model correlation among multiple solution vectors is largely ignored. Although several recently developed algorithms consider the exploitation of the correlation, these algorithms need to know a priori the correlation structure, thus limiting their effectiveness in practical problems. Recently, we developed a sparse Bayesian learning (SBL) algorithm, namely T-SBL, and its variants, which adaptively learn the correlation structure and exploit such correlation information to significantly improve reconstruction performance. Here we establish their connections to other popular algorithms, such as the group Lasso, iterative reweighted $\ell_1$ and $\ell_2$ algorithms, and algorithms for time-varying sparsity. We also provide strategies to improve these existing algorithms.

stat.CO↗

arXiv2011

Sum rate analysis of a reduced feedback OFDMA system employing joint scheduling and diversity

We consider joint scheduling and diversity to enhance the benefits of multiuser diversity in an \OFDMA{} system. The \OFDMA{} spectrum is assumed to consist of $\Nrb$ resource blocks and the reduced feedback scheme consists of each user feeding back channel quality information (\CQI) for only the best-$\NFb$ resource blocks. Assuming largest normalized \CQI{} scheduling and a general value for $\NFb$, we develop a unified framework to analyze the sum rate of the system for both the quantized and non-quantized \CQI{} feedback schemes. Based on this framework, we provide closed-form expressions for the sum rate for three different multi-antenna transmitter schemes; Transmit antenna selection (\TAS), orthogonal space time block codes (\OSTBC) and cyclic delay diversity (\CDD). Furthermore, we approximate the sum rate expression and determine the feedback ratio $(\frac{\NFb}{\Nrb})$ required to achieve a sum rate comparable to the sum rate obtained by a full feedback scheme.

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arXiv2011

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 $\ell_2$ 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 $\ell_2$ algorithms.

stat.ML↗

arXiv2010

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.

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