Modulation mode detection and classification for in-vivo nano-scale communication systems operating in terahertz band

Ozair Iqbal, M., Mahboob Ur Rahman, M., Imran, M. A. , Alomainy, A. and Abbasi, Q. H. (2019) Modulation mode detection and classification for in-vivo nano-scale communication systems operating in terahertz band. IEEE Transactions on NanoBioscience, 18(1), pp. 10-17. (doi:10.1109/TNB.2018.2882063)

Ozair Iqbal, M., Mahboob Ur Rahman, M., Imran, M. A. , Alomainy, A. and Abbasi, Q. H. (2019) Modulation mode detection and classification for in-vivo nano-scale communication systems operating in terahertz band. IEEE Transactions on NanoBioscience, 18(1), pp. 10-17. (doi:10.1109/TNB.2018.2882063)

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Abstract

This paper initiates the efforts to design an intelligent/cognitive nano receiver operating in terahertz band. Specifically, we investigate two essential ingredients of an intelligent nano receiver—modulation mode detection (to differentiate between pulse-based modulation and carrier-based modulation) and modulation classification (to identify the exact modulation scheme in use). To implement modulation mode detection, we construct a binary hypothesis test in nano-receiver’s passband and provide closed-form expressions for the two error probabilities. As for modulation classification, we aim to represent the received signal of interest by a Gaussian mixture model (GMM). This necessitates the explicit estimation of the THz channel impulse response and its subsequent compensation (via deconvolution). We then learn the GMM parameters via expectation–maximization algorithm. We then do Gaussian approximation of each mixture density to compute symmetric Kullback–Leibler divergence in order to differentiate between various modulation schemes (i.e., ${M}$ -ary phase shift keying and ${M}$ -ary quadrature amplitude modulation). The simulation results on mode detection indicate that there exists a unique Pareto-optimal point (for both SNR and the decision threshold), where both error probabilities are minimized. The main takeaway message by the simulation results on modulation classification is that for a pre-specified probability of correct classification, higher SNR is required to correctly identify a higher order modulation scheme. On a broader note, this paper should trigger the interest of the community in the design of intelligent/cognitive nano receivers (capable of performing various intelligent tasks, e.g., modulation prediction, and so on).

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and Abbasi, Dr Qammer H
Authors: Ozair Iqbal, M., Mahboob Ur Rahman, M., Imran, M. A., Alomainy, A., and Abbasi, Q. H.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on NanoBioscience
Publisher:IEEE
ISSN:1536-1241
ISSN (Online):1558-2639
Published Online:19 November 2018
Copyright Holders:Copyright © 2018 IEEE
First Published:First published in IEEE Transactions on NanoBioscience 18(1): 10-17
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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