Instantaneous Feedback-based Opportunistic Symbol Length Adaptation for Reliable Communication

Abstract

It is well known that although feedback cannot increase the channel capacity of memoryless channels, it can enhance reliability or shorten codeword length. This work is based on an early result by Viterbi in 1965 that utilizes instantaneous feedback for reliable communications. We build on this work by incorporating (tail-biting) convolutional codes and designing a system where the decoder interacts with the transmitter by sending feedback during the decoding process. The proposed system is called Opportunistic Symbol Length Adaptation (OSLA), in which the symbol length opportunistically adapts to noise realization of each symbol to ensure that the target reliability is achieved. It is shown that, combined with tail-biting convolutional codes, the proposed scheme outperforms state-of-the-art non-feedback codes, as well as a recently proposed deep learning-based feedback scheme with up to 1.5 dB gain in noise-less and noisy feedback channels.