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

Yahampath, Pradeepa

doi:10.1109/icassp.2009.4960197

“…It is worthwhile to mention that an alternate approach, other than the lookup table has been proposed in the literature to practically implement the decoder [6,19,20]. In this approach, the decoder computes the reconstructions on the fly by estimating the posterior probabilities for quantization index qi as P (qi|Î), when a particularÎ is received.…”

Section: Related Workmentioning

confidence: 99%

Error-resilient and complexity-constrained distributed coding for large scale sensor networks

Viswanatha

¹

,

Ramaswamy

²

,

Saxena

³

et al. 2012

Proceedings of the 11th International Conference on Information Processing in Sensor Networks

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There has been considerable interest in distributed source coding within the compression and sensor network research communities in recent years, primarily due to its potential contributions to low-power sensor networks. However, two major obstacles pose an existential threat on practical deployment of such techniques in real world sensor networks, namely, the exponential growth of decoding complexity with network size and coding rates, and the critical requirement for error-resilience given the severe channel conditions in many wireless sensor networks. Motivated by these challenges, this paper proposes a novel, unified approach for large scale, error-resilient distributed source coding, based on an optimally designed classifier-based decoding framework, where the design explicitly controls the decoding complexity. We also present a deterministic annealing (DA) based global optimization algorithm for the design due to the highly non-convex nature of the cost function, which further enhances the performance over basic greedy iterative descent technique. Simulation results on data, both synthetic and from real sensor networks, provide strong evidence that the approach opens the door to practical deployment of distributed coding in large sensor networks. It not only yields substantial gains in terms of overall distortion, compared to other state-of-the-art techniques, but also demonstrates how its decoder naturally scales to large networks while constraining the complexity, thereby enabling performance gains that increase with network size.

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“…The cross-correlation among CDMA signatures assigned to two users will determine if the two channels are independent or dependent, i.e., if there is mutual interference among the two users. In many previous studies, JSC decoding of correlated sources over a interference free channels (i.e.,orthogonal channels) have been considered [6][7][8][9]. In CDMA, the most commonly employed signature waveform structure is known as directsequence spread spectrum.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Low complexity joint decoders for large scale WSNs with orthogonal CDMA channels have been previously proposed in [8,9].…”

Section: The Issue Of Computational Complexity In Decodingmentioning

confidence: 99%

“…In contrast to previous work, in this thesis we present and investigate a low complexity iterative JSC decoder for correlated sources and a CDMA channel, which exploits inter-source correlation for improved interference cancellation. More specifically, we extend the joint vector quantization (VQ) decoder presented in [9], to obtain a low complexity JSC decoder wherein the key idea is to perform iterative decoding, by using the joint VQ decoder in tandem with a soft-input soft-output MUD. With soft outputs obtained from a MUD, the joint VQ decoder utilizes all of the received information.…”

Section: The Issue Of Computational Complexity In Decodingmentioning

confidence: 99%

See 1 more Smart Citation

On joint source-channel decoding and interference cancellation in CDMA-based large-scale wireless sensor networks

Illangakoon

¹

,

Yahampath

²

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Self Cite

0

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Motivated by potential applications in wireless sensor networks, this thesis considers the problem of communicating a large number of correlated analog sources over a Gaussian multiple-access channel using non-orthogonal code-division multiple-access (CDMA). A joint source-channel decoder is presented which can exploit the inter-source correlation for interference reduction in the CDMA channel. This decoder uses a linear minimum mean square error (MMSE) multi-user detector (MUD) in tandem with a MMSE joint source decoder (JSD) for multiple sources to achieve a computational complexity that scales with the number of sources. The MUD and the JSD, then iteratively exchange extrinsic information to improve the interference cancellation. Experimental results show that, compared to a non-iterative decoder, the proposed iterative decoder is more robust against potential performance degradation due to correlated channel interference and offers better near far resistance.ii AcknowledgementsThere are a number of people I wish to thank for making my experience as a masters student an interesting and rewarding period. First of all, I would like to thank my advisor

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“…Surprisingly, very few researchers have so far addressed this important issue, e.g. [6,10,19,20]. However most of these approaches suffer from important drawbacks which will be explained in detail in section 3.…”

Section: Introductionmentioning

confidence: 99%

Error-resilient and complexity-constrained distributed coding for large scale sensor networks

Viswanatha¹,

Ramaswamy²,

Saxena³

et al. 2012

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)

0

View full text Add to dashboard Cite

There has been considerable interest in distributed source coding within the compression and sensor network research communities in recent years, primarily due to its potential contributions to low-power sensor networks. However, two major obstacles pose an existential threat on practical deployment of such techniques in real world sensor networks, namely, the exponential growth of decoding complexity with network size and coding rates, and the critical requirement for error-resilience given the severe channel conditions in many wireless sensor networks. Motivated by these challenges, this paper proposes a novel, unified approach for large scale, error-resilient distributed source coding, based on an optimally designed classifier-based decoding framework, where the design explicitly controls the decoding complexity. We also present a deterministic annealing (DA) based global optimization algorithm for the design due to the highly non-convex nature of the cost function, which further enhances the performance over basic greedy iterative descent technique. Simulation results on data, both synthetic and from real sensor networks, provide strong evidence that the approach opens the door to practical deployment of distributed coding in large sensor networks. It not only yields substantial gains in terms of overall distortion, compared to other state-of-the-art techniques, but also demonstrates how its decoder naturally scales to large networks while constraining the complexity, thereby enabling performance gains that increase with network size.

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

Cited by 6 publications

References 8 publications

Error-resilient and complexity-constrained distributed coding for large scale sensor networks

Error-resilient and complexity-constrained distributed coding for large scale sensor networks

On joint source-channel decoding and interference cancellation in CDMA-based large-scale wireless sensor networks

Error-resilient and complexity-constrained distributed coding for large scale sensor networks

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