Linear Prediction based Data Detection of Convolutional Coded DQPSK in SIMO-OFDM

TL;DR

This paper introduces a linear prediction-based data detection method for convolutional coded DQPSK signals in SIMO-OFDM systems, reducing complexity and decoding delay while maintaining error correction and channel estimation capabilities.

Contribution

The paper proposes a novel predictive Viterbi algorithm with reduced state complexity for data detection in SIMO-OFDM, improving decoding delay over existing methods.

Findings

Reduces supertrellis states using isometry concept

Achieves lower decoding delay compared to BIC and turbo coded OFDM

Maintains error correction and channel estimation capabilities

Abstract

Data detection of convolutional coded differential quaternary phase shift keyed (DQPSK) signals using a predictive Viterbi algorithm (VA) based receiver, is presented for single input, multiple output - orthogonal frequency division multiplexed (OFDM) systems. The receiver has both error correcting capability and also the ability to perform channel estimation (prediction). The predictive VA operates on a supertrellis with just $S_{\mathrm{ST}}=S_{\mathrm{E}}\times 2^{P-1}$ states instead of $S_{\mathrm{ST}}=S_{\mathrm{E}}\times 2^{P}$ states, where the complexity reduction is achieved by using the concept of isometry (here $S_{\mathrm{E}}$ denotes the number of states in the encoder trellis and $P$ denotes the prediction order). Though the linear prediction based data detection in turbo coded OFDM and the bit interleaved coded (BIC) OFDM systems perform better than the proposed approach in terms of bit error rate (BER) for a given signal to noise ratio (SNR), the decoding delay of the proposed approach is significantly lower than that of the BIC and the turbo coded OFDM systems.