Channel capacity and state estimation for state-dependent Gaussian channels
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 parallel to S-n - S-n parallel to for a state-dependent additive Gaussian channel Y-n = X-n + S-n + Z(n) with an independent and identically distributed (i.i.d.) Gaussian state sequence S-n = (S1,..., S-n) known at the transmitter and an unknown i.i.d. additive Gaussian noise Z(n). We show that a simple technique of direct state amplification (i.e., X-n = alpha S-n), 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.
state estimation state amplification additive gaussian noise channels channels with state-information joint source-channel coding