Combining coded signals with arbitrary modulations in orthogonal relay channels

TL;DR

This paper analyzes a frequency division relay channel with decode-and-forward relays, deriving optimal combining strategies for arbitrary modulations and convolutional encoding to improve decoding performance.

Contribution

It introduces the first comprehensive derivation of ML and other combiners for orthogonal relay channels with arbitrary modulations and convolutional codes.

Findings

Derived the optimal ML combiner and branch metrics.

Compared ML, MRC, C-MRC, and MMSE combiners in this context.

Provided guidelines for implementing these combiners in practical systems.

Abstract

We consider a relay channel for which the following assumptions are made. (1) The source-destination and relay-destination channels are orthogonal (frequency division relay channel). (2) The relay implements the decode-and-forward protocol. (3) The source and relay implement the same channel encoder, namely, a onvolutional encoder. (4) They can use arbitrary and possibly different modulations. In this framework, we derive the best combiner in the sense of the maximum likelihood (ML) at the destination and the branch metrics of the trellis associated with its channel decoder for the ML combiner and also for the maximum ratio combiner (MRC), cooperative-MRC (C-MRC), and the minimum mean-square error (MMSE) combiner.