  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.).
