Energy planning for progressive estimation in multihop sensor networks
IEEE Transactions on Signal Processing
Functional forwarding of channel state information
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Analogue transmission over a two-hop Gaussian cascade network
IEEE Communications Letters
Communicating correlated Gaussian sources over Gaussian Z interference channels
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Sending a bivariate Gaussian over a Gaussian MAC
IEEE Transactions on Information Theory
A little feedback can simplify sensor network cooperation
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
Universal distributed estimation over multiple access channels with constant modulus signaling
IEEE Transactions on Signal Processing
On the optimal performance in asymmetric gaussian wireless sensor networks with fading
IEEE Transactions on Signal Processing
EURASIP Journal on Wireless Communications and Networking - Special issue on signal processing-assisted protocols and algorithms for cooperating objects and wireless sensor networks
Transmitting multiple correlated gaussian sources over a Gaussian MAC using delay-free mappings
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
On optimal policies for control and estimation over a Gaussian relay channel
Automatica (Journal of IFAC)
| Hi-index | 754.91 |
A single memoryless Gaussian source is observed by many terminals, subject to independent Gaussian observation noises. The terminals are linked to a fusion center via a standard Gaussian multiple-access channel. The fusion center needs to recover the underlying Gaussian source with respect to mean-squared error. In this correspondence, a theorem of Witsenhausen is shown to imply that an optimal communication strategy is uncoded transmission, i.e., each terminal's channel input is merely a scaled version of its noisy observation.