Lattice Quantization with Side Information:
Codes, Asymptotics, and Applications in Sensor Networks.
Author
S. D. Servetto.
Status
IEEE Transactions on Information Theory; 53(2):714-731, 2007.
Abstract
We consider the problem of rate/distortion with side information
available only at the decoder. For the case of jointly-Gaussian
source X and side information Y, and mean-squared error distortion,
Wyner proved in 1976 that the rate/distortion function for this problem
is identical to the conditional rate/distortion function R_{X|Y},
assuming the side information Y is available at the encoder. In
this paper we construct a structured class of asymptotically optimal
quantizers for this problem: under the assumption of high correlation
between source X and side information Y, we show there exist
quantizers within our class whose performance comes arbitrarily close
to Wyner's bound. As an application illustrating the relevance of
the high-correlation asymptotics, we also explore the use of these
quantizers in the context of a problem of data compression for sensor
networks, in a setup involving a large number of devices collecting
highly correlated measurements within a confined area. An important
feature of our formulation is that, although the per-node throughput
of the network tends to zero as network size increases, so does the
amount of information generated by each transmitter. This is a
situation likely to be encountered often in practice, which allows
us to cast under new---and more ``optimistic''---light some negative
results on the transport capacity of large-scale wireless networks.
S. D. Servetto. Lattice
Quantization with Side Information. In the Proceedings
of the IEEE Data Compression Conference (DCC), Snowbird, UT, March 2000.
S. D. Servetto.
On the Feasibility of Large-Scale Wireless Sensor Networks.
In the Proceedings of the 40th Annual Allerton Conference on Communication,
Control, and Computing, Urbana, IL, October 2002.