On the Capacity of Cloud Radio Access Networks with Oblivious Relaying

TL;DR

This paper characterizes the capacity of cloud radio access networks with oblivious relays, showing that certain relaying strategies are optimal and providing bounds for general channels, including Gaussian cases.

Contribution

It provides a single-letter capacity characterization for a class of relay networks with oblivious relays, connecting to CEO source coding and extending to Gaussian channels.

Findings

Both compress-and-forward and noisy network coding are optimal strategies.

Capacity region is characterized for Gaussian channels with time-sharing.

Separate decompression-decoding is suboptimal in this setting.

Abstract

We study the transmission over a network in which users send information to a remote destination through relay nodes that are connected to the destination via finite-capacity error-free links, i.e., a cloud radio access network. The relays are constrained to operate without knowledge of the users' codebooks, i.e., they perform oblivious processing. The destination, or central processor, however, is informed about the users' codebooks. We establish a single-letter characterization of the capacity region of this model for a class of discrete memoryless channels in which the outputs at the relay nodes are independent given the users' inputs. We show that both relaying \`a-la Cover-El Gamal, i.e., compress-and-forward with joint decompression and decoding, and "noisy network coding", are optimal. The proof of the converse part establishes, and utilizes, connections with the Chief Executive Officer (CEO) source coding problem under logarithmic loss distortion measure. Extensions to general discrete memoryless channels are also investigated. In this case, we establish inner and outer bounds on the capacity region. For memoryless Gaussian channels within the studied class of channels, we characterize the capacity region when the users are constrained to time-share among Gaussian codebooks. Furthermore, we also discuss the suboptimality of separate decompression-decoding and the role of time-sharing.