Sensing the channel: sensor networks with shared sensing and communications
Proceedings of the 5th international conference on Information processing in sensor networks
Channel Coding in the Presence of Side Information
Foundations and Trends in Communications and Information Theory
Special non-linear filter and extension to Shannon's channel capacity
Digital Signal Processing
Cognitive radio: an information-theoretic perspective
IEEE Transactions on Information Theory
Coding and common reconstruction
IEEE Transactions on Information Theory
ACM Transactions on Sensor Networks (TOSN)
Wyner-Ziv coding over broadcast channels: digital schemes
IEEE Transactions on Information Theory
Joint source channel coding with side information using hybrid digital analog codes
IEEE Transactions on Information Theory
On network interference management
IEEE Transactions on Information Theory
New hybrid digital/analog schemes for transmission of a Gaussian source over a Gaussian channel
IEEE Transactions on Information Theory
Risk bounds of learning processes for Lévy processes
The Journal of Machine Learning Research
| Hi-index | 755.20 |
We formulate a problem of state information transmission over a state-dependent channel with states known at the transmitter. In particular, we solve a problem of minimizing the mean-squared channel state estimation error E||Sn - Sˆn|| for a state-dependent additive Gaussian channel Yn = Xn + Sn + Zn with an independent and identically distributed (i.i.d.) Gaussian state sequence Sn = (S1, ..., Sn) known at the transmitter and an unknown i.i.d. additive Gaussian noise Zn. We show that a simple technique of direct state amplification (i.e., Xn = αSn), where the transmitter uses its entire power budget to amplify the channel state, yields the minimum mean-squared state estimation error. This same channel can also be used to send additional independent information at the expense of a higher channel state estimation error. We characterize the optimal tradeoff between the rate R of the independent information that can be reliably transmitted and the mean-squared state estimation error D. We show that any optimal (R, D) tradeoff pair can be achieved via a simple power-sharing technique, whereby the transmitter power is appropriately allocated between pure information transmission and state amplification.