Robust and Efficient Distributed Compression for Cloud Radio Access Networks
Seok-Hwan Park, Osvaldo Simeone, Onur Sahin, Shlomo Shamai (Shitz)

TL;DR
This paper proposes a robust, distributed compression scheme for uplink cloud radio access networks that accounts for imperfect statistical information and includes a joint base station selection method to improve energy efficiency.
Contribution
It introduces a robust compression method based on worst-case optimization and a joint BS selection algorithm for energy-efficient network operation.
Findings
Robust compression compensates for performance loss due to imperfect covariance information.
Proposed BS selection performs close to exhaustive search.
The algorithms converge to locally optimal solutions.
Abstract
This work studies distributed compression for the uplink of a cloud radio access network where multiple multi-antenna base stations (BSs) are connected to a central unit, also referred to as cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, distributed source coding strategies are potentially beneficial, and can be implemented via sequential source coding with side information. For the problem of compression with side information, available compression strategies based on the criteria of maximizing the achievable rate or minimizing the mean square error are reviewed first. It is observed that, in either case, each BS requires information about a specific covariance matrix in order to realize the advantage of distributed source coding. Since this covariance matrix depends on the channel realizations corresponding to other…
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Taxonomy
TopicsWireless Communication Security Techniques · Advanced MIMO Systems Optimization · Cooperative Communication and Network Coding
