State-dependent channel models with encoder channel state information (CSI) are important in a wide variety of applications including among others watermarking, cellular downlink precoding, and cognitive radio. These models involve a communications channel whose input-output relationship depends upon a time-varying random parameter called the channel state, and have been studied extensively for scenarios with a single encoder and decoder. In this talk, we review coding schemes such as dirty paper coding (DPC) and fundamental performance limits from information theory, focusing on geometric intuition, and present recent results for scenarios involving multiple encoders operating over multiaccess channel (MAC) models.

For the asymmetric scenario in which only some encoders have CSI, we derive inner and outer bounds for the capacity region in the discrete memoryless case, specializing to an example binary model, and in the Gaussian case. The informed encoders in the binary and Gaussian cases use generalized DPC that allows arbitrary correlation between the codeword and the known CSI, with negative correlation being viewed as partial state cancellation. We observe that generalized DPC obtains a larger achievable rate region than that obtained using DPC alone because negative correlation in generalized DPC assists the uninformed encoders. For the Gaussian MAC with some informed encoders, it appears that generalized DPC cannot completely eliminate the effect of the channel state, in contrast to the case of all encoders' being informed. Finally, for the case in which the messages are degraded so that the informed encoder encodes both message, we obtain the capacity region.

Joint work with Shivaprasad Kotagiri.

Host: Ashu Sabharwal

Biography of J Nicholas Laneman:
J. Nicholas Laneman is an Assistant Professor of Electrical Engineering at the University of Notre Dame. He earned a Ph.D. in Electrical Engineering from the Massachusetts Institute of Technology, Cambridge, MA, in 2002. His research interests are in wireless communications and networking, information theory, and detection and estimation. He received the NSF CAREER Award in 2006, the ORAU Ralph E. Powe Junior Faculty Enhancement Award in 2003, and the MIT EECS Harold L. Hazen Teaching Award in 2001. He is a member of IEEE, ASEE, and Sigma Xi.