Maximum Likelihood Decoding of Convolutionally Coded Noncoherent ASK Signals in AWGN Channels

TL;DR

This paper introduces a maximum likelihood detection algorithm for noncoherent ASK signals in AWGN channels, analyzing its performance with convolutional coding and Viterbi decoding, supported by tight upper bounds and simulations.

Contribution

It presents a novel ML detection algorithm for NCASK systems and derives tight performance bounds, enhancing understanding of noncoherent communication in noisy channels.

Findings

Upper bounds are within 0.1 dB of simulation results

The ML detection algorithm improves system performance

Convolutional coding with Viterbi decoding is effective in this setup

Abstract

In this work we develop the maximum likelihood detection (MLD) algorithm for noncoherent amplitude shift keying (NCASK) systems in additive white Gaussian noise (AWGN) channels. The developed algorithm was used to investigate the performance of the NCASK system with convolutional coding and soft-decision Viterbi decoding. Tight and simple upper bounds have been derived to describe the system performance; simulation results have shown that the derived upper bounds are within 0.1 dB of the simulated points.