Learning-Based Compress-and-Forward Schemes for the Relay Channel

TL;DR

This paper introduces a neural network-based compress-and-forward scheme for relay channels that learns task-aware compression, mimics optimal strategies, and operates near theoretical rate limits, demonstrating practical advantages in cooperative communications.

Contribution

It presents the first neural network-based, task-aware compress-and-forward scheme that learns to perform distributed compression mimicking optimal strategies without explicit source structure knowledge.

Findings

Neural compressor recovers binning of quantized indices.

Operates close to the rate of Gaussian codebook schemes.

Demonstrates advantages of correlated signal exploitation in neural CF architectures.

Abstract

The relay channel, consisting of a source-destination pair along with 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. In CF, the relay forwards a quantized version of its received signal to the destination. 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. Leveraging recent advances in neural network-based distributed compression, we revisit the relay channel problem and integrate a learned task-aware Wyner--Ziv compressor into a primitive relay channel with a finite-capacity out-of-band relay-to-destination link. The resulting neural CF scheme demonstrates that our 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. The proposed neural CF, employing finite order modulation, operates closely to the rate achievable in a primitive relay channel with a Gaussian codebook. We showcase the advantages of exploiting the correlated destination signal for relay compression through various neural CF architectures that involve end-to-end training of the compressor and the demodulator components. Our learned task-oriented compressors provide the first proof-of-concept work toward interpretable and practical neural CF relaying schemes.