  Recent deep neural network-based device classification studies show that
complex-valued neural networks (CVNNs) yield higher classification accuracy
than real-valued neural networks (RVNNs). Although this improvement is
(intuitively) attributed to the complex nature of the input RF data (i.e., IQ
symbols), no prior work has taken a closer look into analyzing such a trend in
the context of wireless device identification. Our study provides a deeper
understanding of this trend using real LoRa and WiFi RF datasets. We perform a
deep dive into understanding the impact of (i) the input representation/type
and (ii) the architectural layer of the neural network. For the input
representation, we considered the IQ as well as the polar coordinates both
partially and fully. For the architectural layer, we considered a series of
ablation experiments that eliminate parts of the CVNN components. Our results
show that CVNNs consistently outperform RVNNs counterpart in the various
scenarios mentioned above, indicating that CVNNs are able to make better use of
the joint information provided via the in-phase (I) and quadrature (Q)
components of the signal.
