551 research outputs found
Graded quantization for multiple description coding of compressive measurements
Compressed sensing (CS) is an emerging paradigm for acquisition of compressed
representations of a sparse signal. Its low complexity is appealing for
resource-constrained scenarios like sensor networks. However, such scenarios
are often coupled with unreliable communication channels and providing robust
transmission of the acquired data to a receiver is an issue. Multiple
description coding (MDC) effectively combats channel losses for systems without
feedback, thus raising the interest in developing MDC methods explicitly
designed for the CS framework, and exploiting its properties. We propose a
method called Graded Quantization (CS-GQ) that leverages the democratic
property of compressive measurements to effectively implement MDC, and we
provide methods to optimize its performance. A novel decoding algorithm based
on the alternating directions method of multipliers is derived to reconstruct
signals from a limited number of received descriptions. Simulations are
performed to assess the performance of CS-GQ against other methods in presence
of packet losses. The proposed method is successful at providing robust coding
of CS measurements and outperforms other schemes for the considered test
metrics
Source-Channel Diversity for Parallel Channels
We consider transmitting a source across a pair of independent, non-ergodic
channels with random states (e.g., slow fading channels) so as to minimize the
average distortion. The general problem is unsolved. Hence, we focus on
comparing two commonly used source and channel encoding systems which
correspond to exploiting diversity either at the physical layer through
parallel channel coding or at the application layer through multiple
description source coding.
For on-off channel models, source coding diversity offers better performance.
For channels with a continuous range of reception quality, we show the reverse
is true. Specifically, we introduce a new figure of merit called the distortion
exponent which measures how fast the average distortion decays with SNR. For
continuous-state models such as additive white Gaussian noise channels with
multiplicative Rayleigh fading, optimal channel coding diversity at the
physical layer is more efficient than source coding diversity at the
application layer in that the former achieves a better distortion exponent.
Finally, we consider a third decoding architecture: multiple description
encoding with a joint source-channel decoding. We show that this architecture
achieves the same distortion exponent as systems with optimal channel coding
diversity for continuous-state channels, and maintains the the advantages of
multiple description systems for on-off channels. Thus, the multiple
description system with joint decoding achieves the best performance, from
among the three architectures considered, on both continuous-state and on-off
channels.Comment: 48 pages, 14 figure
Optimal Filter Banks for Multiple Description Coding: Analysis and Synthesis
Multiple description (MD) coding is a source coding technique for information transmission over unreliable networks. In MD coding, the coder generates several different descriptions of the same signal and the decoder can produce a useful reconstruction of the source with any received subset of these descriptions. In this paper, we study the problem of MD coding of stationary Gaussian sources with memory. First, we compute an approximate MD rate distortion region for these sources, which we prove to be asymptotically tight at high rates. This region generalizes the MD rate distortion region of El Gamal and Cover (1982), and Ozarow (1980) for memoryless Gaussian sources. Then, we develop an algorithm for the design of optimal two-channel biorthogonal filter banks for MD coding of Gaussian sources. We show that optimal filters are obtained by allocating the redundancy over frequency with a reverse "water-filling" strategy. Finally, we present experimental results which show the effectiveness of our filter banks in the low complexity, low rate regim
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Source and Channel Coding Strategies for Wireless Sensor Networks
In this dissertation, I focus on source coding techniques as well as channel coding techniques. I addressed the challenges in WSN by developing (1) a new source coding strategy for erasure channels that has better distortion performance compared to MDC; (2) a new cooperative channel coding strategy for multiple access channels that has better channel outage performances compared to MIMO; (3) a new source-channel cooperation strategy to accomplish source-to-fusion center communication that reduces system distortion and improves outage performance. First, I draw a parallel between the 2x2 MDC scheme and the Alamouti's space time block coding (STBC) scheme and observe the commonality in their mathematical models. This commonality allows us to observe the duality between the two diversity techniques. Making use of this duality, I develop an MDC scheme with pairwise complex correlating transform. Theoretically, I show that MDC scheme results in: 1) complete elimination of the estimation error when only one descriptor is received; 2) greater efficiency in recovering the stronger descriptor (with larger variance) from the weaker descriptor; and 3) improved performance in terms of minimized distortion as the quantization error gets reduced. Experiments are also performed on real images to demonstrate these benefits. Second, I present a two-phase cooperative communication strategy and an optimal power allocation strategy to transmit sensor observations to a fusion center in a large-scale sensor network. Outage probability is used to evaluate the performance of the proposed system. Simulation results demonstrate that: 1) when signal-to-noise ratio is low, the performance of the proposed system is better than that of the MIMO system over uncorrelated slow fading Rayleigh channels; 2) given the transmission rate and the total transmission SNR, there exists an optimal power allocation that minimizes the outage probability; 3) on correlated slow fading Rayleigh channels, channel correlation will degrade the system performance in linear proportion to the correlation level. Third, I combine the statistical ranking of sensor observations with cooperative communication strategy in a cluster-based wireless sensor network. This strategy involves two steps: 1) ranking the sensor observations based on their test statistics; 2) building a two-phase cooperative communication model with an optimal power allocation strategy. The result is an optimal system performance that considers both sources and channels. I optimize the proposed model through analyses of the system distortion, and show that the cooperating nodes achieve maximum channel capacity. I also simulate the system distortion and outage to show the benefits of the proposed strategies
State of the art in 2D content representation and compression
Livrable D1.3 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D3.1 du projet
Graded quantization: democracy for multiple descriptions in compressed sensing
The compressed sensing paradigm allows to efficiently represent sparse
signals by means of their linear measurements. However, the problem of
transmitting these measurements to a receiver over a channel potentially prone
to packet losses has received little attention so far. In this paper, we
propose novel methods to generate multiple descriptions from compressed sensing
measurements to increase the robustness over unreliable channels. In
particular, we exploit the democracy property of compressive measurements to
generate descriptions in a simple manner by partitioning the measurement vector
and properly allocating bit-rate, outperforming classical methods like the
multiple description scalar quantizer. In addition, we propose a modified
version of the Basis Pursuit Denoising recovery procedure that is specifically
tailored to the proposed methods. Experimental results show significant
performance gains with respect to existing methods
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