Modulation Mode Detection & Classification for in-Vivo Nano-Scale Communication Systems Operating in Terahertz Band

TL;DR

This paper explores the design of an intelligent nano receiver operating in the Terahertz band, focusing on modulation mode detection and classification using statistical models and optimization techniques.

Contribution

It introduces a novel approach for modulation detection and classification in nano-communication systems, including closed-form error probability expressions and GMM-based classification methods.

Findings

Existence of a Pareto-optimal point minimizing error probabilities.

Higher SNR improves accuracy for higher-order modulation schemes.

The proposed methods enable intelligent tasks like modulation prediction in nano receivers.

Abstract

This work initiates the efforts to design an intelligent/cognitive nano receiver operating in Terahertz (THz) 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, $M$-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 work 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 etc.).