Petros S. Bithas

This study presents the design, fabrication, and experimental validation of the Hermes reusable model rocket platform integrating additive manufacturing, onboard sensing, and telemetry capabilities for low-cost aerospace experimentation. The rocket was manufactured using modular Polyethylene Terephthalate Glycol (PETG) components produced through fused filament fabrication to achieve a lightweight and structurally robust configuration suitable for repeated flight operations. A custom flight computer based on a Raspberry Pi Zero 2W was developed to acquire in-flight data from an inertial measurement unit, barometric pressure sensor, and Global Positioning System module, while an onboard camera enabled post-flight trajectory assessment. Aerodynamic performance and stability were evaluated using OpenRocket simulations, and propulsion was provided by a cluster of Klima D9-5 solid rocket motors. Four experimental flights were conducted to evaluate the integrated system architecture, assess telemetry and sensor performance, and compare experimental flight data with simulation predictions. The recorded measurements successfully captured the primary flight phases, including launch, ascent, apogee, descent, and recovery. The experimental results showed qualitative agreement with the simulated flight profiles; however, deviations in apogee altitude, acceleration, and flight duration were observed due to aerodynamic drag, environmental disturbances, motor-performance variability, and implementation-related limitations. The flight campaigns additionally identified practical challenges associated with wireless telemetry reliability, GPS signal acquisition, electronic protection, and parachute deployment, leading to iterative system improvements. From a sensing perspective, the flight campaigns demonstrate the operation and limitations of a low-cost embedded acquisition architecture under dynamic conditions, including the effects of sampling rate, sensor calibration, synchronization, wireless-link interruption, and local data preservation on the quality of the recorded flight measurements. The presented platform demonstrates the feasibility of combining low-cost additive manufacturing techniques with commercially available embedded electronics for reusable aerospace testing and educational applications. The proposed system further provides a flexible experimental framework for flight-data acquisition, simulation validation, and iterative development in academic and amateur rocketry research.

Unmanned aerial vehicle (UAV) corridor-assisted communication networks are expected to expand significantly in the upcoming years driven by several technological, regulatory, and societal trends. In this new type of networks, accurate and realistic channel models are essential for designing reliable, efficient, and secure communication systems. In this paper, an analytical framework is presented that is based on one-dimensional (1D) finite point processes, namely the binomial point process (BPP) and the finite homogeneous Poisson point process (HPPP), to model the spatial locations of UAV-Base Stations (UAV-BSs). To this end, the shadowing conditions experienced in the UAV-BS-to-ground users links are accurately considered in a realistic maximum power-based user association policy. Subsequently, coverage probability analysis under the two spatial models is conducted, and exact-form expressions are derived. In an attempt to reduce the analytical complexity of the derived expressions, a dominant interferer-based approach is also investigated. Finally, the main outcomes of this paper are extensively validated by empirical data collected in an air-to-ground measurement campaign. The results reveal that: i) increasing the corridor length mitigates interference and diminishes the impact of shadowing and ii) according to conducted measurements, the UAV’s altitude can be modeled by the Normal distribution, effectively capturing the random variations in the UAV’s flight height.

This paper investigates a near-field covert communication system enhanced by an absorptive reconfigurable intelligent surface (ARIS) and a fluid antenna system (FAS), enabling covert transmission to arbitrary receiver locations. By generating near-field spherical waves via large-scale antenna arrays at Alice and ARIS, covert transmission to Bob is enabled while evading detection by Willie. We jointly optimize Alice’s hybrid precoding, ARIS reflection coefficients, and Bob’s active port selection to maximize the worst-case covert transmission rate. We begin by evaluating ARIS’s suitability versus conventional RIS. We demonstrate the asymptotic orthogonality of near-field beam-focusing vectors in the 3D domain for uniform planar arrays, and characterize the beam-focusing behavior in cascaded ARIS-enabled covert transmissions. Additionally, we reveal the channel gain improvement owing to FAS over traditional antenna systems. To solve the coupled non-convex problem, we propose a low-complexity block coordinate descent algorithm. It incorporates Fibonacci search for hybrid precoding, three complexity-performance trade-off strategies for reflection coefficients optimization, and both exhaustive search and linear conic relaxation for active port selection. Finally, we recover precoding via an alternating minimization scheme. Numerical results show that (i) significant improvement of covert transmission is achieved only with both ARIS and FAS, when Bob and Willie are co-located; (ii) the proposed algorithm outperforms near-field and far-field beam alignment schemes without ARIS, as well as beam focusing of full-map zeroing with ARIS, when Bob and Willie share the same reception direction.

Low-altitude (LA) intelligent network is a key component of integrated space-air-ground networks, where understanding radio propagation channels is crucial for the design and optimization of reliable communication links. In this paper, we present a high efficient and low-interference LA channel measurement scheme for multiple nodes. To avoid self-interferences and cancel cross-interference, a series-parallel switching method is developed, where sounding signals are specially designed via a genetic algorithm (GA). It can effectively reduce the multi-node interference and improve the efficiency. In order to reduce the data size of multi-link channels and release the data transmission and postprocessing burden, a real-time data reduction method is proposed, which can extract the valid multi-path components (MPCs) and small-scale fading (SSF) characteristics. On this basis, a prototype of four-node LA channel measurement system is implemented. The prototype is applied to perform LA channel measurements in a campus scenario, and measured channel characteristics for air-to-air (A2A), air-to-ground (A2G), and ground-to-ground (G2G) links are presented. Then, the deep neural network (DNN) and the variational autoencoder (VAE) are utilized to characterize the LA channels, which can effectively predict and generate new channel characteristics for real applications.

This study investigates the design and optimization of a free-space optical (FSO) wireless communication network employing high-altitude platforms (HAPs). The objective is to explore the parameters that affect the quality and viability of such a network and to develop a method for minimizing installation costs while maximizing performance. The methodology includes clustering ground nodes using the k-means algorithm and adjusting the emission solid angles for each HAP. Furthermore, to more closely reflect real-world conditions, our analytical investigations also consider the effects of atmospheric turbulence. The network’s performance is evaluated under both daytime and nighttime operational scenarios, taking into account background noise and the layered effects of atmospheric turbulence. These considerations ensure that the results presented in this paper more accurately reflect real-world conditions. The results demonstrate significant performance gains through appropriate parameter selection. Additionally, deploying multiple HAPs enhances network flexibility and resilience. It was shown that in certain scenarios specific combinations of per-HAP configurations offer more than a 70% increase in throughput with a small increase in the cost. The paper’s insights fill an important gap between theoretical FSO network models and the practical design considerations needed for real deployments.

Integrated sensing and communication (ISAC) is a promising technology for next-generation wireless networks, enabling applications that require enhanced communication and precise sensing capabilities. Notable examples include smart environments, augmented and virtual reality, and the internet of things, where the functionalities of intelligent sensing and broadband communications are paramount. Consequently, ISAC has attracted significant research interest from academia and industry, resulting in numerous investigations conducted over the past decade. The literature encompasses a diverse range of articles, including system models, performance evaluations, and optimization studies of various ISAC designs. Stochastic geometry (SG) is the study of random spatial patterns. As such, SG tools have been used to evaluate the performance of wireless networks with different types of nodes. In this paper, we present a comprehensive survey of current research on the performance evaluation of ISAC systems that employ SG tools. The survey covers terrestrial, aerial, and vehicular networks, addressing the random spatial location of network elements, propagation scatterers, and blockages through various point processes. The paper begins with an overview of ISAC technology, SG tools, and performance evaluation metrics for communication and sensing. Next, we elaborate on the technical components of the system models employed in the surveyed literature. Then, we present pertinent literature findings across diverse network types using three levels of integration: sensing-assisted communication, communication-assisted sensing, and collaborative sensing and communication. Lastly, we discuss the challenges and potential directions of forthcoming research.

Unmanned aerial vehicle (UAV) corridor-assisted communication networks are expected to expand significantly in the upcoming years driven by several technological, regulatory, and societal trends. In this new type of networks, accurate and realistic channel models are essential for designing reliable, efficient, and secure communication systems. In this paper, an analytical framework is presented that is based on one-dimensional (1D) finite point processes, namely the binomial point process (BPP) and the finite homogeneous Poisson point process (HPPP), to model the spatial locations of UAV-Base Stations (UAV-BSs). To this end, the shadowing conditions experienced in the UAV-BS-to-ground users links are accurately considered in a realistic maximum power-based user association policy. Subsequently, coverage probability analysis under the two spatial models is conducted, and exact-form expressions are derived. In an attempt to reduce the analytical complexity of the derived expressions, a dominant interferer-based approach is also investigated. Finally, the main outcomes of this paper are extensively validated by empirical data collected in an air-to-ground measurement campaign. To the best of the authors' knowledge, this is the first work to experimentally verify a generic spatial model by jointly considering the random spatial and shadowing characteristics of a UAV-assisted air-to-ground network.

Joint communication and sensing (JCAS) technology allows the coexistence of sensing and communication capabilities within the same frequency band without causing mutual interference. Aerial-toground wireless communication networks offer additional flexibility for communication users and sensing targets through the dynamic positioning of unmanned aerial vehicles (UAVs), resulting in an extra degree of freedom to alleviate the challenges imposed by the dynamic characteristics of the wireless propagation channel. In this paper, a sensing-assisted aerial communication network is analytically investigated in scenarios with realistic assumptions for the channel and system model. Indeed, the presented analysis considers independent but non-identically distributed shadowing effects, non-isotropic antennas, and a generic statistical distribution for the radar cross section of sensing targets. Analytical expressions are derived for the statistics of the received signal-to-interference plus noise ratio (SINR), for both sensing and communication functionalities, while simpler expressions for special cases and asymptotic results are also obtained. Based on the analytical derivations, communication and sensing performance have been evaluated using, respectively, the outage and coverage probabilities and the ergodic radar estimation rate and detection probability. Numerical and simulation results demonstrate the accuracy of the proposed analysis and reveal how factors like non-identical distributed statistics of shadowing, small scale fading, and interference influence the performance of the system.