Autors: Ivanov, A. S., Tonchev, K., Poulkov, V. K., Manolova, A. H.
Title: Deep Learning for Modulation Classification: Signal Features in Performance Analysis
Keywords: cognitive radio; deep learning; feature analysis; Modulation classification

Abstract: An increasing trend towards making the modulation classification (MC) algorithms better-suited for real world implementation in Cognitive Radio (CR) equipment, can be seen as recent works adopt novel agile deep learning models and datasets which include different kinds of signal impairments. Considering the large range of studies in the field, a unifying examination of the common state-of-The-Art methods will benefit the future developments by providing a starting point for comparison of their efficiency for recognition of various communication signals. The purpose of this study is to provide a comparative analysis which gives clarity on the ways in which the dataset's type and contents (statistical features of the signals as well as the presence of noise) influences the classification performance. Thus, a more systematic approach to choosing the appropriate input data and a suitable classifier model can be employed in future developments.

References

    Issue

    in Proceedings of Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON, 11-14 March 2020, pp. 162-167, 2020, Thailand, DOI 10.1109/ECTIDAMTNCON48261.2020.9090764

    Copyright IEEE

    Цитирания (Citation/s):
    1. B Jdid, K Hassan, I Dayoub, WH Lim, M Mokayef, Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey - 2021 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus