4 research outputs found
Distributed Quantization for Sparse Time Sequences
Analog signals processed in digital hardware are quantized into a discrete
bit-constrained representation. Quantization is typically carried out using
analog-to-digital converters (ADCs), operating in a serial scalar manner. In
some applications, a set of analog signals are acquired individually and
processed jointly. Such setups are referred to as distributed quantization. In
this work, we propose a distributed quantization scheme for representing a set
of sparse time sequences acquired using conventional scalar ADCs. Our approach
utilizes tools from secure group testing theory to exploit the sparse nature of
the acquired analog signals, obtaining a compact and accurate representation
while operating in a distributed fashion. We then show how our technique can be
implemented when the quantized signals are transmitted over a multi-hop
communication network providing a low-complexity network policy for routing and
signal recovery. Our numerical evaluations demonstrate that the proposed scheme
notably outperforms conventional methods based on the combination of
quantization and compressed sensing tools
Channel Optimized Distributed Multiple Description Coding
In this paper, channel optimized distributed multiple description vector
quantization (CDMD) schemes are presented for distributed source coding in
symmetric and asymmetric settings. The CDMD encoder is designed using a
deterministic annealing approach over noisy channels with packet loss. A
minimum mean squared error asymmetric CDMD decoder is proposed for effective
reconstruction of a source, utilizing the side information (SI) and its
corresponding received descriptions. The proposed iterative symmetric CDMD
decoder jointly reconstructs the symbols of multiple correlated sources. Two
types of symmetric CDMD decoders, namely the estimated-SI and the soft-SI
decoders, are presented which respectively exploit the reconstructed symbols
and a posteriori probabilities of other sources as SI in iterations. In a
multiple source CDMD setting, for reconstruction of a source, three methods are
proposed to select another source as its SI during the decoding. The methods
operate based on minimum physical distance (in a wireless sensor network
setting), maximum mutual information and minimum end-to-end distortion. The
performance of the proposed systems and algorithms are evaluated and compared
in detail.Comment: Submitted to IEEE Transaction on Signal Processin