Pradeepa Yahampath

On index assignment and the design of multiple description quantizers

The practical design of multiple description quantizers for diversity-based communication is investigated. A simulated annealing based method is proposed for obtaining the optimal index assignment for a multiple-description vector quantizer. This method can be used to design quantizers having an arbitrary number of descriptions with equal or unequal transmission rates. According to the simulation results, the proposed method yields multiple-description quantizers with performance comparable with, or better than, previously reported results.

Distributed Joint Source-Channel Coding Using Unequal Error Protection LDPC Codes

This paper presents a general approach to designing distributed joint source channel (DJSC) codes with arbitrary rates for communication of a pair of correlated binary sources over noisy channels. In this approach, both distributed compression and channel error correction are simultaneously achieved by transmitting, for each source, a fraction of the information bits together with the parity bits of a systematic channel code. This approach is shown to be asymptotically optimal, i.e., any rate-pair in the achievable rate-region can be approached as the codeword length is increased. The practical realization of such a code requi res the design of a pair of channel codes with unequal error protection (UEP) properties determined by the inter-source correlation and the channel capacity available to each source. Towards this end, a linear programming based procedure for jointly optimizing the degree profiles of a pair of irregular LDPC codes to achieve the required UEP properties is presented. Experimental results obtained with both binary symmetric channels and binary-input Gaussian channels are presented, which demonstrate that the proposed UEP-DJSC codes can significantly outperform separate source-channel codes, as well as previously reported joint source-channel coding schemes, particularly for short codeword lengths.

On finite-state vector quantization for noisy channels

Finite-state vector quantization (FSVQ) over a noisy channel is studied. A major drawback of a finite-state decoder is its inability to track the encoder in the presence of channel noise. In order to overcome this problem, we propose a nontracking decoder which directly estimates the code vectors used by a finite-state encoder. The design of channel-matched finite-state vector quantizers for noisy channels, using an iterative scheme resembling the generalized Lloyd algorithm, is also investigated. Simulation results based on encoding a Gauss-Markov source over a memoryless Gaussian channel show that the proposed decoder exhibits graceful degradation of performance with increasing channel noise, as compared with a finite-state decoder. Also, the channel-matched finite-state vector quantizers are shown to outperform channel-optimized vector quantizers having the same vector dimension and rate. However, the nontracking decoder used in the channel-matched finite-state quantizer has a higher computational complexity, compared with a channel-optimized vector-quantizer decoder. Thus, if they are allowed to have the same overall complexity (encoding and decoding), the channel-optimized vector quantizer can use a longer encoding delay and achieve similar or better performance. Finally, an example of using the channel-matched finite-state quantizer as a backward-adaptive quantizer for nonstationary signals is also presented.

Predictive Vector Quantizer Design for Distributed Source Coding

This paper investigates the design of a system of predictive vector quantizers for distributed sources with memory, in which linear prediction is used to exploit the source memory, while distributed quantization is used to exploit the correlation between sources. A training-based algorithm is proposed for jointly designing the predictors, binning functions, and reconstruction codebooks of the given system to match the intra-and inter-source correlations. In order to demonstrate the effectiveness of the algorithm, experimental results obtained by designing both scalar and vector quantizers for a set of distributed Gauss-Markov sources are presented. While the optimality of these designs is unknown, it is shown that they convincingly outperform several other alternatives.

Design of scalable decoders for sensor networks via Bayesian network learning

Minimum mean square error (MMSE) decoding in a large-scale sensor network which employs distributed quantization is considered. Given that the computational complexity of the optimal decoder is exponential in the network size, we present a framework based on Bayesian networks for designing a near-optimal decoder whose complexity is only linear in network size (hence scalable). In this approach, a complexity-constrained factor graph, which approximately represents the prior joint distribution of the sensor outputs, is obtained by constructing an equivalent Bayesian network using the maximum likelihood (ML) criterion. The decoder executes the sum-product algorithm on the simplified factor graph. Our simulation results have shown that the scalable decoders constructed using the proposed approach perform close to optimal, with both Gaussian and non-Gaussian sensor data.

Distributed joint source-channel coding of correlated binary sources in wireless sensor networks

In this paper, we present a distributed joint source-channel (DJSC) coding approach for a pair of correlated binary sources transmitted over independent binary symmetric channels. This problem is of interest in wireless sensor network applications, where encoders with low complexity and delay may be required. In the proposed method, a judiciously chosen fraction of information bits and a fraction of parity bits obtained by puncturing the output of a systematic channel code are transmitted for each source. We obtain the achievable rate region for the proposed coding scheme and show that it coincides with the Slepian-Wolf lower bound as the channel error probability approaches zero. Experimental results obtained with a practical implementation based on LDPC codes are also presented which demonstrate that for short coding block lengths (or low delay coding), the proposed DJSC coding method outperforms separate distributed source coding and channel coding.

Multiple-Description Predictive-Vector Quantization With Applications to Low Bit-Rate Speech Coding Over Networks

An algorithm for designing linear prediction-based two-channel multiple-description predictive-vector quantizers (MD-PVQs) for packet-loss channels is presented. This algorithm iteratively improves the encoder partition, the set of multiple description codebooks, and the linear predictor for a given channel loss probability, based on a training set of source data. The effectiveness of the designs obtained with the given algorithm is demonstrated using a waveform coding example involving a Markov source as well as vector quantization of speech line spectral pairs

Joint source-decoding in large scale sensor networks using Markov random field models

An approach to scalable joint source decoding in large-scale sensor networks, based on Markov-random filed (MRF) modeling of the spatio-temporal correlation in the observations is presented. This approach exploits the correlation among a multitude of sensors for joint decoding at a central decoder, while using simple distributed quantizers in individual sensors. The decoder derivations are provided for Slepian-Wolf coded quantization based on both sample-by-sample (scalar) binning and vector binning schemes constructed via channel code partitioning. Simulation results are presented to demonstrate the performance achievable with the proposed decoding approach.

Symbol Error Rate Analysis of Spatially Correlated Keyhole MIMO Channels with Space-Time Block Coding and Linear Precoding

This paper derives exact expressions for the symbol error rate (SER) of orthogonal space-time block codes over a spatially correlated multiple-input multiple-output (MIMO) channel, in which the signal propagation suffers from a keyhole effect. A correlated double Rayleigh fading keyhole channel is assumed and easy to evaluate expressions are presented for multi- level phase shift keying (M-PSK), pulse amplitude modulation (M-PAM), and quadrature amplitude modulation (M-QAM). These expressions are verified by estimating the SER via the simulation of the MIMO system. The given expressions are then used to quantify the performance improvements attainable with minimum SER linear precoding over a correlated keyhole channel. I. INTRODUCTION work, this paper derives the exact expression for SER of OSTBCs over spatially correlated double Rayleigh fading keyhole MIMO channels with precoding in the transmitter. In particular, we derive easy to evaluate analytical expressions for SER of M-PSK, M-PAM, and M-QAM signaling. These results are generalizations of those in (6) to include precoding and spatial correlation in the keyhole channel. Numerical results are presented to confirm that our analytical expressions agree with those obtained by simulation of the OSTBC-MIMO systems. Based on the SER expressions, we also investigate the performance improvements achievable with linear pre- coders optimized for the correlation statistics of the keyhole MIMO channel. These precoders are found by minimizing the SER expressions with respect to the precoder matrix. It is well known that, when the transmitter has side information about the channel correlation matrix, then the performance of OSTBCs can be improved by using a precoder at the MIMO channel input, see (8), (9) and the references therein. To our knowledge, precoding for keyhole channels has not been considered before. The results presented in this paper show that a significant reduction in SER can be obtained at moderate to low channel signal to noise ratios, by including a minimum SER precoder in the transmitter. We also show that many properties of the minimum SER precoders for Rayleigh (10) and Ricean (9) MIMO channels also hold true for the double Rayleigh fading keyhole channel as well.

Joint source-channel decoding of convolutionally encoded multiple-descriptions

The scenario considered in this paper is the transmission of a continuous information source over a set of erasure channels, by using a multiple description quantizer to deal with channel erasures and a convolutional channel code on each channel to deal with random bit errors. The diversity available in multiple descriptions is subsequently exploited in Viterbi sequence detectors to jointly decode the convolutional codes. Two approaches to joint decoding are presented and investigated. Simulation results are presented for two-channel multiple description quantization of Gaussian sources which demonstrate the potential improvements in end-to-end source distortion achievable with joint decoding of channel codes in a multiple description system. We also compare the performance of joint Viterbi detectors with that of turbo-style iterative decoding of multiple-description codes proposed earlier.