State Amplification

• Published in: IEEE transactions on information theory Vol. 54; no. 5; pp. 1850 - 1859
• Main Authors: Young-Han Kim, Sutivong, A., Cover, T.M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Amplification; Applied sciences; Capacity; causal state information; Channels; channels with state information; Coding, codes; Cognitive radio; Communication channels; Conveying; Data transmission; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Fading; Information processing; Information systems; Information theory; Information, signal and communications theory; joint source-channel coding; Mutual information; noncausal state information; Optimization; Radio transmitters; Receivers; Reduction; Signal and communications theory; Signal, noise; state amplification; state uncertainty reduction; Statistics; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transmission; Transmission and modulation (techniques and equipments); Uncertainty; Writing; writing on dirty paper; joint source-channel coding; Parameter estimation; channels with state information; Information rate; state amplification; Joint source channel coding; Information transmission; noncausal state information; causal state information; writing on dirty paper; Capacity; Channel estimation; Masking; Transmission rate; Causality; Mutual information; state uncertainty reduction
• ISSN: 0018-9448; 1557-9654
• DOI: 10.1109/TIT.2008.920242

We consider the problem of transmitting data at rate over a state-dependent channel with state information available at the sender and at the same time conveying the information about the channel state itself to the receiver. The amount of state information that can be learned at the receiver is captured by the mutual information between the state sequence and the channel output . The optimal tradeoff is characterized between the information transmission rate and the state uncertainty reduction rate , when the state information is either causally or noncausally available at the sender. In particular, when state transmission is the only goal, the maximum uncertainty reduction rate is given by . This result is closely related and in a sense dual to a recent study by Merhav and Shamai, which solves the problem of masking the state information from the receiver rather than conveying it.