Applications of deep-learning at digital communication receiver

Abstract

Modulation and demodulation are fundamental modules for communication systems. The modulation techniques — Offset QPSK (OQPSK), p/2 BPSK, p/4 QPSK and GMSK — are frequently applied in the power-constrained wireless communication links (e.g., the terminal transmission links of several 2G, 3G and 4G terrestrial and satellite air-interface standards). However, their detailed numerical comparison of the performance and functional characteristics are currently lacking in the literature. The prior studies have focused on a comparison of at the most two of these four schemes (typically OQPSK versus GMSK). One of the objectives of this thesis is to bridge this gap. We provide a detailed comparison of (i) the spectral regrowth and (ii) probability of bit error Perrb versus Eb/N0 performance of these four modulation schemes in the presence ofAM/AMandAM/PM non-linearities with varying backoff (BO). We believe that our results with key observations will be beneficial in selecting an appropriate modulation technique when designing practical communication systems. Another crucial component of communication and signal processing systems is the estimation of channel parameters. In the practical communication systems, the varying channel conditions and non-linear channel impairments make the task of estimation more challenging. We propose a Deep Learning (DL) application at digital communication receiver to estimate the channel impairments that are difficult to describe with a rigid mathematical tractable model. Another objective of our research work is to develop a learned parameter estimator that effectively captures the non-linear functional mappings and produces accurate estimations. The results for Phase Offset (PO) impairment estimations obtained with our proposed approach give competitive accuracy concerning its baseline equivalent. Lastly, we demonstrate the learning-based modulation classifier that potentially solves the misclassification problem presented in an earlier study.