Neural Compress-and-Forward for the Relay Channel

TL;DR

This paper introduces a neural network-based compress-and-forward scheme for relay channels, demonstrating near-capacity performance and mimicking optimal strategies without explicit source structure knowledge.

Contribution

It presents the first neural network-based compress-and-forward scheme that learns to perform distributed compression similar to theoretical optimal strategies.

Findings

Neural CF mimics optimal binning of relay indices.

Operates close to the theoretical capacity of Gaussian relay channels.

First proof-of-concept for practical neural relay schemes.

Abstract

The relay channel, consisting of a source-destination pair and a relay, is a fundamental component of cooperative communications. While the capacity of a general relay channel remains unknown, various relaying strategies, including compress-and-forward (CF), have been proposed. For CF, given the correlated signals at the relay and destination, distributed compression techniques, such as Wyner-Ziv coding, can be harnessed to utilize the relay-to-destination link more efficiently. In light of the recent advancements in neural network-based distributed compression, we revisit the relay channel problem, where we integrate a learned one-shot Wyner--Ziv compressor into a primitive relay channel with a finite-capacity and orthogonal (or out-of-band) relay-to-destination link. The resulting neural CF scheme demonstrates that our task-oriented compressor recovers "binning" of the quantized indices at the relay, mimicking the optimal asymptotic CF strategy, although no structure exploiting the knowledge of source statistics was imposed into the design. We show that the proposed neural CF scheme, employing finite order modulation, operates closely to the capacity of a primitive relay channel that assumes a Gaussian codebook. Our learned compressor provides the first proof-of-concept work toward a practical neural CF relaying scheme.