An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification

TL;DR

This study investigates why complex-valued neural networks outperform real-valued ones in RF device classification, revealing that CVNNs better utilize joint IQ data, leading to higher accuracy in wireless device identification.

Contribution

It provides a detailed analysis of the impact of input representation and network architecture on CVNN performance in RF classification tasks.

Findings

CVNNs outperform RVNNs across various input representations.

Joint IQ data is better exploited by CVNNs for classification.

Architectural ablations confirm the importance of complex components.

Abstract

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.