Department Publications

Intelligent control of RF transceivers adapting to dynamic operational conditions is essential in the modern and future communication systems. We propose a multi-agent neurosymbolic AI system, where AI agents are assigned for circuit components. Agents have an internal model and a corresponding control algorithm as its constituents. Modeling of the IF amplifier shows promising results, where the same approach can be extended to all the components, thus creating a fully intelligent RF system.

Civilian GNSS receivers are vulnerable to spoofing due to open signal structures, while adaptive electronic-warfare platforms increasingly synthesize high-fidelity counterfeit signals. Existing detection methods depend on handcrafted features or supervised learning, limiting robustness under distribution shifts and novel attacks. This paper introduces NEMESIS, a self-supervised framework based on joint embedding predictive architectures (JEPA) in the wavelet time-frequency domain, which leverages unlabeled RF corpora via masked time-frequency prediction to learn spoofing-relevant embeddings directly from raw RF signals without labels or contrastive augmentation. Experiments demonstrate that wavelet-domain JEPA substantially outperforms time- and frequency-domain variants and achieves competitive accuracy relative to contrastive learning while significantly reducing labeling requirements.

The radio frequency (RF) cochlea, a biomimetic LCR transmission-line filter bank that performs O(N) broadband spectrum analysis, has been demonstrated in active CMOS and as passive microwave channelizers but has remained a one-dimensional (1-D) structure since its introduction two decades ago. We argue that the natural next step is to add a second dimension, and we explore three complementary aspects of this extension. First, we review existing 1-D RF cochlea architectures and present a 20-channel, 1 GHz constant-bandwidth passive channelizer as a representative implementation. Second, we analyze the two-dimensional (2-D) cochlear-duct geometry of Watts and Neely using finite-difference and Liouville–Green simulations: the second dimension introduces a cut-off mode whose interference with the traveling-wave mode produces an upper-skirt roll-off 1.6–1.7× steeper than the 1-D long-wave limit …

This paper presents a multi agent neurosymbolic artificial intelligence architecture to dynamically adapt operating parameters of radio frequency (RF) transceivers. The proposed dual branch architecture combines deep learning feature extraction with deterministic symbolic reasoning, leveraging sensed physical parameters such as signal power and spectrum occupancy, to ensure self-adapting and robust hardware safety. Component specific control agents are trained using digital twins and integrated with physical hardware via shared memory space and socket-based inter-process communication for low latency. Experimental results demonstrate accurate modeling and rapid parameter adaptation for error vector magnitude optimization, providing a scalable foundation for intelligent self-adapting RF transceivers.