Optimal Distortion-Power Tradeoffs in Gaussian Sensor Networks

Computer Science – Information Theory

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

5 pages, 0 figure, To appear in ISIT 2006

Scientific paper

We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements from an underlying random process, code and transmit those measurement samples to a collector node in a cooperative multiple access channel with imperfect feedback, and reconstruct the entire random process at the collector node. We provide lower and upper bounds for the minimum achievable expected distortion when the underlying random process is Gaussian. In the case where the random process satisfies some general conditions, we evaluate the lower and upper bounds explicitly and show that they are of the same order for a wide range of sum power constraints. Thus, for these random processes, under these sum power constraints, we determine the achievability scheme that is order-optimal, and express the minimum achievable expected distortion as a function of the sum power constraint.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Optimal Distortion-Power Tradeoffs in Gaussian Sensor Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Optimal Distortion-Power Tradeoffs in Gaussian Sensor Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Optimal Distortion-Power Tradeoffs in Gaussian Sensor Networks will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-119164

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.