EE Seminar: Cloud Radio Access Networks, Distributed Information Bottleneck, and more: A Unified Information Theoretic View
(The talk will be given in English)
Speaker: Prof. Shlomo Shamai
Distinguished Professor, Chair in Communications, Technion – Israel Institute of Technology
The Viterbi Faculty of Electrical Engineering, Technion-Israel Institute of Technology
Tuesday, July 3rd, 2018
14:00 - 15:00
Hall 020 (between Wolfson & Labs Builds.), Faculty of Engineering
Cloud Radio Access Networks, Distributed Information Bottleneck, and more: A Unified Information Theoretic View
Abstract
We consider transmission over a cloud radio access network (CRAN) focusing on the framework of oblivious processing at the relay nodes (radio units), i.e., the relays are not cognizant of the users' codebooks.
This approach is motivated by future wireless communications (5G and beyond) and the theoretical results connect to a variety of different information theoretic models and problems.
First it is shown that relaying a-la Cover-El Gamal, i.e., compress-and-forward with joint decompression and decoding, which reflects 'noisy network coding,' is optimal.
The penalty of obliviousness is also demonstrated to be at most a constant gap, when compared to cut-set bounds.
Naturally, due to the oblivious (nomadic) constraint the CRAN problem intimately comments to Chief Executive Officer (CEO) source(s) coding under a logarithmic loss distortion measure.
Furthermore, we identify and elaborate on some interesting connections with the distributed information bottleneck model for which we characterize optimal tradeoffs between rates (i.e., complexity) and information (i.e., accuracy) in the discrete and vector Gaussian frameworks.
Further connections to 'information combining' and 'common reconstruction' are also pointed out. In the concluding outlook, some interesting problems are mentioned such as the characterization of the optimal input distributions under users' power limitations and rate-constrained compression at the relay nodes,
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Joint work with: I.E. Aguerri (Paris Research Center, Huawei France) A. Zaidi (Universite Paris-Est, Paris) and G. Caire (USC-LA and TUB, Berlin)
The research is supported by the European Union's Horizon 2020 Research And Innovation Programme: no. 694630.
