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Entropy 2019, 21(3), 273;

MIMO Gaussian State-Dependent Channels with a State-Cognitive Helper

Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel
Samsung Semiconductor Inc., San Jose, CA 95134, USA
Department of ECE, The Ohio State University, Columbus, OH 43210, USA
Authors to whom correspondence should be addressed.
Received: 2 January 2019 / Revised: 3 March 2019 / Accepted: 5 March 2019 / Published: 12 March 2019
(This article belongs to the Special Issue Multiuser Information Theory II)
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We consider the problem of channel coding over multiterminal state-dependent channels in which neither transmitters nor receivers but only a helper node has a non-causal knowledge of the state. Such channel models arise in many emerging communication schemes. We start by investigating the parallel state-dependent channel with the same but differently scaled state corrupting the receivers. A cognitive helper knows the state in a non-causal manner and wishes to mitigate the interference that impacts the transmission between two transmit–receive pairs. Outer and inner bounds are derived. In our analysis, the channel parameters are partitioned into various cases, and segments on the capacity region boundary are characterized for each case. Furthermore, we show that for a particular set of channel parameters, the capacity region is entirely characterized. In the second part of this work, we address a similar scenario, but now each channel is corrupted by an independent state. We derive an inner bound using a coding scheme that integrates single-bin Gel’fand–Pinsker coding and Marton’s coding for the broadcast channel. We also derive an outer bound and further partition the channel parameters into several cases for which parts of the capacity region boundary are characterized. View Full-Text
Keywords: dirty paper coding; Gel’fand–Pinsker scheme; non-causal channel state information; network information theory dirty paper coding; Gel’fand–Pinsker scheme; non-causal channel state information; network information theory

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Dikshtein, M.; Duan, R.; Liang, Y.; Shamai (Shitz), S. MIMO Gaussian State-Dependent Channels with a State-Cognitive Helper. Entropy 2019, 21, 273.

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