Broadcast Approach for the Information Bottleneck Channel

This work considers a layered coding approach for efficient transmission of data over a wireless block fading channel, connected to a limited capacity reliable link, known as the bottleneck channel. Two main approaches are considered, the first is an oblivious approach, where the sampled noisy observations are compressed and transmitted over the bottleneck channel without having any knowledge of the original information codebook. This is compared to a decode-forward (non-oblivious) approach where the sampled noisy data is decoded, and whatever is successfully decoded is reliably transmitted over the bottleneck channel. In both settings it is possible to analytically describe in closed form expressions, the optimal continuous layering power distribution which maximizes the average achievable rate. Numerical results demonstrate the achievable broadcasting rate in the limit of continuous layering.

[1]  Gerald Matz,et al.  The rate-information trade-off for Gaussian vector channels , 2014, 2014 IEEE International Symposium on Information Theory.

[2]  Shlomo Shamai,et al.  On the capacity of cloud radio access networks with oblivious relaying , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).

[3]  Erna Viterbi,et al.  Broadcast Approach under Information Bottleneck Capacity Uncertainty 1 , 2020 .

[4]  Muhammad Ali Imran,et al.  5G Backhaul Challenges and Emerging Research Directions: A Survey , 2016, IEEE Access.

[5]  Shlomo Shamai,et al.  Broadcast Approach for the Information Bottleneck Channel , 2021, IEEE Transactions on Communications.

[6]  Shlomo Shamai,et al.  Communication via decentralized processing , 2005, ISIT.

[7]  Suhas N. Diggavi,et al.  Successive Refinement Via Broadcast: Optimizing Expected Distortion of a Gaussian Source Over a Gaussian Fading Channel , 2008, IEEE Transactions on Information Theory.

[8]  Toby Berger,et al.  All sources are nearly successively refinable , 2001, IEEE Trans. Inf. Theory.

[9]  Amir K. Khandani,et al.  Broadcast Approaches to the Diamond Channel , 2012, IEEE Transactions on Information Theory.

[10]  William Equitz,et al.  Successive refinement of information , 1991, IEEE Trans. Inf. Theory.

[11]  A. Sridharan Broadcast Channels , 2022 .

[12]  Shlomo Shamai,et al.  Robust uplink communications over fading channels with variable backhaul connectivity , 2013, ISIT.

[13]  Xinping Yi,et al.  Opportunistic Treating Interference as Noise , 2018, IEEE Transactions on Information Theory.

[14]  Bixio Rimoldi,et al.  Successive refinement of information: characterization of the achievable rates , 1994, IEEE Trans. Inf. Theory.

[15]  S. Brendle,et al.  Calculus of Variations , 1927, Nature.

[16]  Xiupu Zhang,et al.  High Capacity Mode Division Multiplexing Based MIMO Enabled All-Optical Analog Millimeter-Wave Over Fiber Fronthaul Architecture for 5G and Beyond , 2019, IEEE Access.

[17]  Shlomo Shamai,et al.  A broadcast approach for a single-user slowly fading MIMO channel , 2003, IEEE Trans. Inf. Theory.

[18]  Shlomo Shamai,et al.  A broadcast strategy for the Gaussian slowly fading channel , 1997, Proceedings of IEEE International Symposium on Information Theory.

[19]  Shlomo Shamai,et al.  Variable-Rate Channel Capacity , 2010, IEEE Transactions on Information Theory.

[20]  Naftali Tishby,et al.  The information bottleneck method , 2000, ArXiv.

[21]  Gal Chechik,et al.  Information Bottleneck for Gaussian Variables , 2003, J. Mach. Learn. Res..

[22]  Ali Tajer,et al.  Multiaccess Communication via a Broadcast Approach Adapted to the Multiuser Channel , 2018, IEEE Transactions on Communications.

[23]  Rüdiger L. Urbanke,et al.  A New Coding Paradigm for the Primitive Relay Channel , 2019, Algorithms.

[24]  Shlomo Shamai,et al.  New Upper Bounds on the Capacity of Primitive Diamond Relay Channels , 2019, 2019 IEEE Information Theory Workshop (ITW).

[25]  H. Vincent Poor,et al.  Unified Overview of Matrix-Monotonic Optimization for MIMO Transceivers , 2018, ArXiv.

[26]  Shlomo Shamai,et al.  On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Views , 2020, Entropy.