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.

This concept paper proposes an architecture that integrates agentic AI models with digital twin representations for each subsystem component in an RF transceiver. The digital twins, constructed using machine learning techniques such as deep learning and reinforcement learning, are paired with specialized, autonomous AI agent(s) capable of independent reasoning, planning, and adaptive action. Agentic AI models go beyond traditional rule-based/symbolic AI to autonomously select optimal strategies, synthesize data from diverse sources, and interact with both virtual and real-world environments. Ideas for simulations and experimental validations are proposed for future work.

The real-time adaptive nulling of a digital beamformer is crucial for receiving weak signals in a high RFI or RF denied environment. The Howells-Applebaum and MVDR adaptive beamformers use the Weiner optimum solution to ensure that RFI sources are blanked out in an array processor. In this paper, we create a custom 32-channel fully-digital array operating at 5.7-5.8 GHz, with real-time FPGA processing for both adaptation and subsequent beamforming with RFI suppression. Real-time measurements with up to three RFI sources show correct real-time digital beamforming in the FPGA processor. The developed microwave and digital systems along with hardware accelerated Howells-Applebaum array-null adaptation architecture will be used as a building block to create RF identification, location, spectrum sensing, and wireless communications that are resilient to jammers and RFI sources in the environment.

We address the detection of material defects, which are inside a layered material structure using compressive sensing-based multiple-input and multiple-output (MIMO) wireless radar. Here, strong clutter due to the reflection of the layered structure’s surface often makes the detection of the defects challenging. Thus, sophisticated signal separation methods are required for improved defect detection. In many scenarios, the number of defects that we are interested in is limited, and the signaling response of the layered structure can be modeled as a low-rank structure. Therefore, we propose joint rank and sparsity minimization for defect detection. In particular, we propose a non-convex approach based on the iteratively reweighted nuclear and ℓ1-norm (a double-reweighted approach) to obtain a higher accuracy compared to the conventional nuclear norm and ℓ1-norm minimization. To this end, an iterative algorithm is designed to estimate the low-rank and sparse contributions. Further, we propose deep learning-based parameter tuning of the algorithm (i.e., algorithm unfolding) to improve the accuracy and the speed of convergence of the algorithm. Our numerical results show that the proposed approach outperforms the conventional approaches in terms of mean squared errors of the recovered low-rank and sparse components and the speed of convergence.

We address the compensation of distortions in terahertz time-domain spectroscopy (THz-TDS) systems. In this context, the main sources of possible distortions are misalignment of the hardware and the sample during the scan, vibration of the measurement system and time delay variations due to the unevenness of the material surface. This heavily affects the achievable accuracy of material characterization by the THz-TDS system. Therefore, it is important to develop approaches to mitigate these effects. In this work, we profit from the low-rankness of the distortion profile. To this end, we formulate an iterative algorithm based on rank minimization to estimate the material parameters in the presence of the aforementioned impairments. In contrast to the standard convex relaxation of the rank, we propose an iteratively reweighted nuclear norm for improved estimation performance. Further, we compare our approach with …

Wireless signals-based non-destructive material characterization and object identification is a vital research area that has many potential day-to-day and industrial applications. Here, one of the main challenges is suppression of the unwanted reflections coming from the surroundings that are not in our interest, ie, clutter. The clutter echoes are much stronger than the reflection that comes from the desired object. Also, in many scenarios, the clutter source and the desired objects are in close proximity. Therefore, clutter may mask the reflections coming from the desired objects. This dissertation intensively discusses clutter suppression for the identification of objects/defects. In this regard, we utilize advances in supervised learning and nonconvex optimization for clutter suppression. In this context, we profit from the low-rankness and the sparseness of the responses of the clutter and the objects. Instead of the convex relaxation of rank and sparsity commonly applied in state-ofthe-art, we propose a non-convex iteratively reweighted nuclear and ℓ1− norm-based iterative algorithm to obtain higher accuracy. Moreover, in contrast to the standard parameter tuning of the iterative algorithm, we propose supervised deep learning to tune the parameters, which is referred to as algorithm unrolling. Our numerical results show that deep learning-based parameter tuning significantly improves the accuracies of the recovered low-rank and sparse components and the speed of the convergence of the iterative algorithm. Additionally, we investigated the robustness of our approach by introducing artificial errors into the measurement matrices. Our findings show that …

This paper discusses advances related to the integration of future mobile electronic THz systems. Without claiming to provide a comprehensive review of this surging research area, the authors gathered research on selected topics that are expected to be of relevance for the future exploration of components for practical mobile THz imaging and sensing applications. First, a brief technology review of integrated mobile THz components is given. Advances in III-V technology, silicon technology, and resonant-tunneling diodes (RTD) are discussed. Based on an RTD source and a SiGe-HBT direct detector, low-cost and compact computed tomography is presented for volumetric continuous-wave imaging at around 300 GHz. Moreover, aspects of system integration of mobile THz MIMO radars are discussed. Thereby, a novel phase-locked loop concept utilizing a high-stability yttrium-iron-garnet-tuned oscillator to …

Estimating parameters and properties of various materials without causing damage to the material under test (MUT) is important in many applications. Thus, in this letter, we address MUT's parameter estimation by wireless sensing. Here, the precision of the estimation depends on the accurate estimation of the properties of the reflected signal from the MUT (e.g., number of reflections, their amplitudes, and time delays). For a layered MUT, there are multiple reflections, and due to the limited bandwidth at the receiver, these reflections superimpose with each other. Since the number of reflections coming from the MUT is limited, we utilize sparse signal processing (SSP) to decompose the reflected signal. In SSP, a so called dictionary is required to obtain a sparse representation of the signal. Here, instead of a fixed dictionary, an iterative dictionary-update technique is proposed to improve the estimation of the reflected …

We present a compressive sensing based defect detection by multiple input multiple output (MIMO) wireless radar. Here, defects are inside a layered material structure, therefore, due to reflections from the surface of the layered material structure the defect detection is challenging. By utilizing a low-rank nature of the reflections of the layered material structure and sparse nature of the defects, we propose a method based on rank minimization and sparse recovery. To improve the accuracy in the recovery of low-rank and sparse components, we propose a non-convex approach based on the iteratively reweighted nuclear norm and iteratively reweighted norm algorithm. Our numerical results show that the proposed method is able to demix and recover the signalling responses of the defects and layered structure successfully from substantially reduced number of observations. Further, the proposed approach outperforms the state-of-the-art clutter reduction approaches

We address the problem of identification of spherical objects, which are inside a layered structure by wireless sensing. Here, the backscatter response is used to identify the objects. It is a task, which is improved as the resolution gets higher. Therefore, to have a better resolution we consider the THz band. However, in the THz surface scattering expands the backscatter response of the layered structure in time. Thus, the backscatter response of the objects and the layered structure superimpose each other. This becomes even more severe due to the limited bandwidth at the receiver. In consequence, the exploitation of sophisticated signal separation methods for improved object identification is required. Therefore, we propose morphological component analysis (MCA), semi-blind MCA (MCA-SB) and singular value decomposition (SVD) methods. The MCA and MCA-SB rely on the sparse representation of signals …

Dielectric heaters are mainly used in industry for various applications. Due to generation of near field energy from stray electromagnetic fields, these machines were selected for analysis as a source for RF energy harvesting. In this research, the energy harvester was designed using a ferrite core loop antenna, rectifying and smoothing circuit and an ultra-low power management system in order to provide energy to a temperature and humidity sensor node. This application was tested within the footwear manufacturing industry due to availability of dielectric heaters (RF Welding Machine) and temperature and humidity are continuously measured during the production process.

Low Noise Pre-amplifiers (LNP) increase the quality of weak signals by amplifying them without introducing excessive noise generated inside the amplifier. The advantage of using LNP for weak TV signal reception is presented by extensive analysis of signal to noise ratio. A design of a LNP that operates satisfactorily over the 50-750 MHz frequency band is presented in this paper. Cost effectiveness and superior performance are the salient features of this design.