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Blind Digital Modulation Detector for MIMO Systems over High-Speed Railway Channels

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Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 7865))

Abstract

Nowadays, several wireless communication networks are widely deployed along railway lines, and future ones are already candidates for Long-Term Evolution (LTE) wireless data communications standard deployment. In fact, an efficient interoperability between these systems is crucial to increase safety, reduce maintenance costs and offer new services to passengers. Cognitive Radios (CRs) have been selected as promised alternative to answer all previous requirements. The main issue in CR is radio environment awareness, which is enhanced by modulation and waveform identification. In this paper we aim to shed light on the problem of blind digital modulation identification for Multiple-Input Multiple-Output (MIMO) technology used in high-speed railway communication systems which are associated to fast-fading channels. The intention is to distinguish among different M-ary Phase-Shift Keying (M-PSK), Amplitude-Shift Keying (M-ASK) and Quadrature Amplitude Modulation (M-QAM) modulation schemes without signal knowledge and Channel State Information (CSI). The detection process employs a Blind Source Separation (BSS) technique to sightlessly perceive the modulation type. The proposed detector proves a high M-PSK modulation identification ratio under a high velocity for LTE standard frequency band and bandwidth.

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© 2013 Springer-Verlag Berlin Heidelberg

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Kharbech, S., Dayoub, I., Simon, E., Zwingelstein-Colin, M. (2013). Blind Digital Modulation Detector for MIMO Systems over High-Speed Railway Channels. In: Berbineau, M., et al. Communication Technologies for Vehicles. Nets4Cars/Nets4Trains 2013. Lecture Notes in Computer Science, vol 7865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37974-1_19

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  • DOI: https://doi.org/10.1007/978-3-642-37974-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37973-4

  • Online ISBN: 978-3-642-37974-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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