Antonios Argyriou

Rapid advances in optical imaging have accelerated the emergence of holographic applications. To enable a genuine holographic telepresence experience, the challenge of transmitting massive volumes of holographic data over bandwidth-limited networks must be addressed. This article presents a hologram streaming framework that combines complex-domain rescaling and bitrate allocation tailored to the complex-valued nature of holograms. First, we build a complex-valued rescaling neural network to reduce the required transmission bandwidth. It consists of a downscaling model for reducing hologram volume and a compression-robust upscaling model for recovering high-resolution holograms. The two models are jointly trained to optimize hologram reconstruction quality and are then deployed separately on the server and client. Second, we develop a distortion model that characterizes the different contributions of the real and imaginary (R/I) components to the amplitude distortion of reconstructed holograms. The model is used in a rate-distortion optimization framework to guide optimal R/I bitrate allocation subject to a bandwidth constraint. Experiments on immersive-video datasets show that the proposed framework substantially improves rate-distortion performance; under the BD-rate metric, the baselines require 49.83–222.71% more bitrate to achieve comparable reconstruction quality.

During deep space missions, maintaining reliable communication between a spacecraft and Earth becomes particularly challenging during superior solar conjunctions, when the spacecraft passes behind the Sun relative to Earth. The highly ionized solar corona and solar wind create plasma inhomogeneities that severely affect radio signals, leading to phase scintillation, amplitude fading, and signal loss. These effects intensify as the Sun‐Earth‐Probe (SEP) angle decreases, resulting in frequent disruption of phase‐locking techniques crucial for successful signal demodulation. This paper comprehensively analyzes the communication subsystem to ensure reliable tracking, telemetry, and command (TT&C) links during superior solar conjunctions. By analyzing the effects of solar plasma on radio links, the paper proposes new operational methods and subsystem architectures that enhance the robustness of RF communication even in very harsh conditions. The solutions include modulation, coding, non‐coherent demodulation methods, and alternative channel models to mitigate scintillation effects and maintain stable communication links, particularly at low SEP angles. These strategies offer promising ways to improve data transmission reliability and maximize mission success during challenging solar conjunction phases.

Searching for transient wireless signals like fast radio bursts (FRBs) in radio interferometer data can be accomplished with beamforming algorithms. Radio-frequency interference (RFI) can significantly hamper the search since it causes a loss of bandwidth that can reach 40% for a telescope like the Canadian Hydrogen Intensity Mapping Experiment. In this work, we propose a new beamforming algorithm that combines the discrete Fourier transform and the high-resolution adaptive minimum variance distortionless response beamformers for RFI-free and RFI-inflicted channels, respectively. Our method operates on the baseband data after the transient has been detected. We deploy the beamformer first to produce cleaner dynamic spectra of the FRBs, and second to localize them spatially under high-power RFI. Our simulation results show that even when a fraction of 20% of the channels are contaminated with RFI, we have an increase in localization accuracy, especially in the low signal-to-noise ratio regime.

This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise and inference mitigation, while the subsequent Hankel-DMD stage enables reliable parameter extraction even for overlapping sginals or strong interference conditions. Numerical simulations demonstrate the robustness of MAHankDMD, showing its ability to precisely estimate both DOA and frequency under challenging conditions involving RFI and Gaussian white noise interference. The proposed algorithm thus provides an effective solution for channel parameter estimation in complex noisy environments.

With the imminent development of sixth-generation (6G) networks, there will be a demand for high-accuracy, computationally-efficient, and low-inference time automatic modulation classification (AMC) algorithms. To address this need, we propose a new deep-learning based model for AMC that is called the threshold denoise recurrent neural network (TDRNN). The TDRNN combines an adaptive threshold denoising (TD) algorithm and a recurrent neural network (RNN) that together achieve high accuracy and fast inference. The TD module adaptively reduces the noise level of the received signal, while the RNN module performs the modulation classification on the denoised result. The two subsystems are jointly optimized to reach the optimal architecture. The proposed TDRNN is evaluated for various modulation schemes and signal-to-noise ratios (SNR). The experimental results demonstrate that the TDRNN outperforms existing methods in terms of accuracy, speed, and computational complexity making it an ideal solution for 6G wireless communication systems.

This paper presents a replay attack that can compromise the functionality of joint radar communication (JRC) systems that use orthogonal frequency division multiplexing (OFDM) by creating ghost target signatures. The attack is particularly dangerous in connected autonomous vehicles that deploy JRC. When the attacker detects the OFDM JRC transmission it retransmits the same frame (preamble and data) but with a frequency and phase shift that varies across subcarriers and successive OFDM symbols of the preamble. This technique exploits the operation of the OFDM RADAR receiver algorithm and generates additional ghost targets in the Range-Doppler response at the JRC system. To evaluate the impact of this attack we calculate the Range-Doppler response for different system configurations of an 802.11-based OFDM JRC system and different spoofing strategies in a representative vehicular system. Our results show that JRC based on OFDM can easily be spoofed and it may lead to inability of a autonomous vehicular system to operate safely.

In this paper we propose a method for defending against an eavesdropper that uses a Deep Neural Network (DNN) for learning the modulation of wireless communication signals. Our method is based on manipulating the emitted waveform with the aid of a continuous time frequency-modulated (FM) obfuscating signal that is mixed with the modulated data. The resulting waveform allows a legitimate receiver (LRx) to demodulate the data but it increases the test error of a pre-trained or adversarially-trained DNN classifier at the eavesdropper. The scheme works for analog modulation and digital single carrier and multi carrier orthogonal frequency division multiplexing (OFDM) waveforms, while it can implemented in frame-based wireless protocols. The results indicate that careful selection of the parameters of the obfuscating waveform can drop classification performance at the eavesdropper to less than 10% in AWGN and fading channels with no performance loss at the LRx.

In this paper we propose a method for defending against an eavesdropper that uses a Deep Neural Network (DNN) for learning the modulation of wireless communication signals. Our method is based on manipulating the emitted waveform with the aid of a continuous time frequency-modulated (FM) obfuscating signal that is mixed with the modulated data. The resulting waveform allows a legitimate receiver (LRx) to demodulate the data but it increases the test error of a pre-trained or adversarially-trained DNN classifier at the eavesdropper. The scheme works for analog modulation and digital single carrier and multi carrier orthogonal frequency division multiplexing (OFDM) waveforms, while it can implemented in frame-based wireless protocols. The results indicate that careful selection of the parameters of the obfuscating waveform can drop classification performance at the eavesdropper to less than 10% in AWGN and fading channels with no performance loss at the LRx.