On Channel Sensitivity to Partially Known Two-sided State Information

In some situations of channel coding with state information (CCSI), the encoder and/or the decoder may not have perfect knowledge of the state information. In these situations, the state information may be viewed as the sum of a dominant (nominal) state information and a relatively weak perturbation. We consider the general case of channel with arbitrary pair of independent and identically distributed (i.i.d), possibly correlated, state information (S1, S2) available at the transmitter and at the receiver, respectively. We first analyze the decrease in capacity, or channel sensitivity to this perturbing noise. Both lower and upper bounds on this channel sensitivity are provided, using Fisher Information. The lower bound turns to be relatively tight, at low Signal-to-Noise-Ratio (SNR), in the Gaussian case, for which we provide a closed form expression of channel capacity degradation. Next, we show that these results can be used so as to increase system immunity to noise, by adapting the encoder to the channel uncertainty. Also, we straightforwardly extend these results to the more practical case where the state information is known only causally at the transmitter. Finally, for illustration purposes, two possible applications in the non-causal and the causal case, respectively, are discussed.

[1]  G. Wornell,et al.  Information Embedding and Related Problems : Recent Results and Applications , 2001 .

[2]  Mung Chiang,et al.  Duality between channel capacity and rate distortion with two-sided state information , 2002, IEEE Trans. Inf. Theory.

[3]  Aaron D. Wyner,et al.  Channels with Side Information at the Transmitter , 1993 .

[4]  Vincent K. N. Lau,et al.  Optimal partial feedback design for MIMO block fading channels with causal noiseless feedback , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[5]  Vincent K. N. Lau,et al.  Optimal partial feedback design for MIMO block fading channels with feedback capacity constraint , 2003, IEEE International Symposium on Information Theory, 2003. Proceedings..

[6]  Max H. M. Costa,et al.  Writing on dirty paper , 1983, IEEE Trans. Inf. Theory.

[7]  Mikael Skoglund,et al.  Combining beamforming and orthogonal space-time block coding , 2002, IEEE Trans. Inf. Theory.

[8]  P. Duhamel,et al.  On Coding With a Partial Knowledge of the State Information , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[9]  Shlomo Shamai,et al.  On channels with partial channel state information at the transmitter , 2005, IEEE Transactions on Information Theory.

[10]  Abbas El Gamal,et al.  On the capacity of computer memory with defects , 1983, IEEE Trans. Inf. Theory.

[11]  Pierre Duhamel,et al.  Audio watermarking under desynchronization and additive noise attacks , 2006, IEEE Transactions on Signal Processing.

[12]  Dinh-Tuan Pham,et al.  Entropy of a variable slightly contaminated with another , 2005, IEEE Signal Processing Letters.

[13]  Stark C. Draper,et al.  Side information aware coding strategies for sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[14]  S. Verdu,et al.  Sensitivity of Gaussian channel capacity and rate-distortion function to nonGaussian contamination , 1995, Proceedings of 1995 IEEE International Symposium on Information Theory.

[15]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[16]  Babak Hassibi,et al.  On the capacity of MIMO broadcast channels with partial side information , 2005, IEEE Transactions on Information Theory.