Instant messaging applications (IM apps) are among the most frequent sources of signalling storms in mobile networks. Poorly designed IM apps can be exploited by adversaries to initiate stealthy signalling storm attacks (SSAs). Recent advancements in the 5G Radio Resource Control (RRC) protocol have introduced a novel attack vector, where malicious actors manipulate the 5G inactivity timer to generate excessive signalling messages. Due to the inherent complexity of the working mechanism of the IM app and the diversity of user behavior, distinguishing between legitimate and malicious IM signalling traffic is a challenge. In this study, a comprehensive source traffic model (STM) is presented that captures the operational dynamics of modern IM applications alongside a wide spectrum of user interaction patterns. This model enables a deeper understanding of normal IM traffic and its corresponding signalling load. Building upon this foundation, we introduce a threat model that demonstrates how attackers can emulate normal user behavior and exploit IM app functionalities to launch inactivity timer-based SSAs in 5G networks. To detect such attacks, two novel traffic analysis features: Burst Correlation Index (BCI) and Kolmogorov–Smirnov Distance Contrast (KSDC) are proposed. These metrics effectively highlight subtle differences between benign and malicious traffic patterns, enhancing classification accuracy significantly.

This paper presents an analytical framework for modeling line-of-sight (LoS) availability in Reconfigurable Intelligent Surface (RIS)-assisted satellite communication systems under urban blockages. A correlated LoS probability model is proposed using a bivariate Bernoulli framework that captures spatial dependencies between satellite-to-user and satellite-to-RIS links. We derive closed-form expressions for the system-level success probability, incorporating elevation angle, blockage statistics, and RIS deployment geometry. A key result is a closed-form expression for the optimal RIS-user separation distance that maximizes system reliability. Monte Carlo simulations across diverse urban environments validate the accuracy of the proposed model. Numerical results reveal that denser urban settings require shorter RIS-user distances for optimal performance. The framework enables tractable performance analysis and guides efficient RIS deployment in urban SatCom scenarios.

Satellite and terrestrial communication systems often operate in congested and sometimes overlapping frequency bands due to the growing demand for spectrum. Existing and future systems might head towards intelligent spectrum access to address the limited spectrum problem. In this paper, we present a non-cooperative semantic segmentation spectrum sensing (SSSS) agent to tackle the first task of intelligent spectrum access. The proposed agent utilizes an encoder-decoder network architecture coupled with supervised machine learning to classify pixels in a spectrogram image as occupied or background. Furthermore, the proposed agent was trained to detect satellite and terrestrial signals under low and high signal-to-noise ratio (SNR) fading environments. The proposed agent removed underutilized convolution filters through pruning to improve its memory footprint and inference timing. We conducted an extensive performance evaluation under different simulated shared spectrum scenarios with varying channels. We validated the performance of the proposed agent through statistical analysis that was applied to the softmax output of the network.

This paper considers a MIMO Integrated Sensing and Communication (ISAC) system, where a base station simultaneously serves a MIMO communication user and a remote MIMO sensing receiver, without channel state information (CSI) at the transmitter. Existing MIMO ISAC literature often prioritizes communication rate or detection probability, typically under constant false-alarm rate (CFAR) assumptions, without jointly analyzing detection reliability and communication constraints. To address this gap, we adopt an eigenvalue-based detector for robust sensing and use a performance metric—the total detection error—that jointly captures false-alarm and missed-detection probabilities. We derive novel closed-form expressions for both probabilities under the eigenvalue detector, enabling rigorous sensing analysis. Using these expressions, we formulate and solve a —joint power allocation and threshold optimization— problem that minimizes total detection error while meeting a minimum communication rate requirement. Simulation results demonstrate that the proposed joint design substantially outperforms conventional CFAR-based schemes, highlighting the benefits of power-and threshold-aware optimization in MIMO ISAC systems.

Radio frequency interference (RFI) in synthetic aperture radar (SAR) is a daunting challenge, affecting both sensing reliability and image quality. To ensure that SAR remains a powerful tool for Earth observation, this letter presents a 2-D variable attenuation space (azimuth)-frequency filtration (VASFF) method. This framework leverages the time-frequency characteristics of Level-0 SAR data, the RFI power profile, estimated RFI signal parameters, and the SAR antenna pattern to design a novel variable filter. Signal power localization estimates the interference source’s relative position, facilitating filter application. Simulated results, obtained using our open-source emulator, SEMUS, to generate both clean and interference-contaminated raw SAR data, demonstrate that the proposed filter achieves a 2 dB improvement over traditional notch filtering. The framework is further tested on real-life interference events on TerraSAR-X revealing previously obscured image details, validating the framework’s effectiveness.

To keep up with the continuously growing coverage demands and attain true global coverage, the deployment of multi-layered low Earth orbit satellite constellations is necessary. Next-generation mega satellite constellations are expected to rely on inter-satellite links to relay information, which will enable fast and reliable communications between the different satellite nodes in free-space and facilitate the utilization of coverage diversity modes that can further enhance the quality-of-service provided in the network. However, materializing these high performing systems is challenging due to the complexity of the network architecture which may require long and complex simulation processes during design. In this article, we develop theoretical modeling for the probability of coverage for various diversity modes in mega satellite constellations by leveraging tools from stochastic geometry. We first develop analytical models for conventional single-shell networks and then extend these models to incorporate multi-shell networks. The analysis is validated using Monte-Carlo simulations which show a close fit to the analytical models derived. Moreover, the analytical models provide a performance baseline that is comparable to practical networks that rely on regular network architectures such as SpaceX’s Starlink. This allows network operators to devise expansion strategies to cater for expanding demands and gain insights into the performance of the network as more shells are introduced into the network.

This research paper delves into interference mitigation within Low Earth Orbit (LEO) satellite constellations, particularly when operating under constraints of limited radio environment information. Leveraging cognitive capabilities facilitated by the Radio Environment Map (REM), we explore strategies to mitigate the impact of both intentional and unintentional interference using planar antenna array (PAA) beamforming techniques. We address the complexities encountered in the design of beamforming weights, a challenge exacerbated by the array size and the increasing number of directions of interest and avoidance. Furthermore, we conduct an extensive analysis of beamforming performance from various perspectives associated with limited REM information: static versus dynamic, partial versus full, and perfect versus imperfect. To substantiate our findings, we provide simulation results and offer conclusions based on the outcomes of our investigation.

Mega satellite networks recently emerged to com-plement the current terrestrial infrastructure to attain global coverage and faster communication links. However, with in-creased congestion in the radio spectrum, migrating satellite communication to high frequencies can provide higher data rates. Nevertheless, signal fading due to weather conditions and atmospheric losses becomes significant at such high frequencies. In this paper, we leverage tools from stochastic geometry to provide an analytical framework that captures the effect of high frequency fading for satellite constellations in the downlink. The analytical framework presented exploits the spatial diversity provided by the satellite network and examines the performance of the dominant satellite (satellite providing the best quality of service). Results show a close fit to the performance of practical networks and can thus provide network designers with an analytical benchmark to assist with network tuning.

With the exponential growth of the Internet of Things (IoT) landscape and the resulting spectrum congestion, innovative techniques for spectrum monitoring are crucial. This paper presents an approach to spectrum monitoring harnessing the power of spiking neural networks (SNNs) with a focus on image segmentation using the UNet architecture. Traditional methods, including energy detection, have been widely used but are not without challenges, especially in environments with varying signal-to-noise ratios. In contrast, the presented SNN approach in this paper demonstrates through simulations performance metrics that significantly surpass energy detection methods and closely align with conventional convolutional neural network techniques while also exhibiting favorable energy efficiency. Future explorations will delve into enhancing the framework using machine learning techniques for advanced feature extraction and multiclass segmentation.

Transmitter authentication is critical for secured Internet of Things (IoT) applications. Recently, there has been growing interest in utilizing the physical layer authentication technique, radio frequency (RF) fingerprinting, to introduce extra security measurements without adding additional components. This work presents a novel fingerprint exploitation modality, Density Trace Plot (DTP), to leverage RF fingerprints originating from symbol transition trajectories for transmitter authentication. With a particular focus on IQ imbalance as the source impairment for RF fingerprints, we investigate the feasibility of three types of DTPs based on constellation, eye, and phase traces. The potential fingerprints presented in the DTP modalities are then used in training three deep learning classifiers: 2D-convolutional neural network (CNN), 2D-CNN+bi-directional long short-term memory (biLSTM), and 3D-CNN for transmitter authentication. The feasibility of the proposed approach in both wired and wireless conditions is validated using an experimental setup built using ADALM-PLUTO software-defined radios (SDRs). Experimental results demonstrate the best authentication accuracy of 96.7% is achieved across signals of various modulation complexities.

With the expansive deployment of ground base stations, low Earth orbit (LEO) satellites, and aerial platforms such as unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), the concept of space-air-ground integrated network (SAGIN) has emerged as a promising architecture for future 6G wireless systems. In general, SAGIN aims to amalgamate terrestrial nodes, aerial platforms, and satellites to enhance global coverage and ensure seamless connectivity. Moreover, beyond mere communication functionality, computing capability is increasingly recognized as a critical attribute of sixth generation (6G) networks. To address this, integrated communication and computing have recently been advocated as a viable approach. Additionally, to overcome the technical challenges of complicated systems such as high mobility, unbalanced traffics, limited resources, and various demands in communication and computing among different network segments, various solutions have been introduced recently. Consequently, this paper offers a comprehensive survey of the technological advances in communication and computing within SAGIN for 6G, including system architecture, network characteristics, general communication, and computing technologies. Subsequently, we summarize the pivotal technologies of SAGIN-enabled 6G, including the physical layer, medium access control (MAC) layer, and network layer. Finally, we explore the technical challenges and future trends in this field.

Utilizing Low Earth Orbit (LEO) satellite networks equipped with Inter-Satellite Links (ISL) is envisioned to provide lower delay compared to traditional optical networks. However, LEO satellites have constrained energy resources as they rely on solar energy in their operations. Thus requiring special consideration when designing network topologies that do not only have low-delay link paths but also low-power consumption. In this paper, we study different satellite constellation types and network typologies and propose a novel power-efficient topology. As such, we compare three common satellite architectures, namely; (i) the theoretical random constellation, the widely deployed (ii) Walker-Delta, and (iii) Walker-Star constellations. The comparison is performed based on both the power efficiency and end - to-end delay. The results show that the proposed algorithm outperforms long-haul ISL paths in terms of energy efficiency with only a slight hit to delay performance relative to the conventional ISL topology.

Recent satellite constellations are being deployed to serve massive numbers of wireless devices, especially targeting those located in rural and offshore settings. Accordingly, business models relying on data reported via wireless Internet-of-Things (IoT) networks can now easily utilize satellite constellations to expand their offerings. In this paper, we present an analytic framework for modeling the uplink performance of massive IoT-over-Satellite networks. The framework utilizes tools from stochastic geometry to model the satellites and the users as two random point processes enabling the development of a tractable analytic model for the uplink outage probability. Furthermore, the paper derives the expected normalized throughput and compares the results to Monte-Carlo simulations for intractable constellations such as the Walker models adopted by current satellite deployments. Comparisons show that random constellations provide a tractable lower bound to the performance and average throughput compared to Walker constellations. The analytic model can provide the fast estimation of the uplink performance aiding in designing the IoT-over-Satellite system.

The LoRa modulation scheme is becoming one of the most adopted Internet of Things wireless physical layer due to its ability to transmit data over long distances with low power requirements. Typical demodulation techniques for LoRa utilize variants of non-coherent Frequency Shift Keying demodulation. This paper aims to capitalize on the robustness of deep learning techniques, specifically by using convolutional neural networks to demodulate LoRa symbols. We achieve this by building a dataset consisting of emulated time-domain LoRa symbols across a range of channel impairments; namely, we examine additive white Gaussian noise together with carrier frequency offset and time offset. The presented results show an improvement when utilizing deep learning over typical non-coherent detection while performing very close to the optimal matched filter.

Spectrum scarcity has surfaced as a prominent concern in wireless radio communications with the emergence of new technologies over the past few years. As a result, there is a growing need for better understanding of the spectrum occupancy with newly emerging access technologies supporting the Internet of Things. In this article, we present a framework to capture and model the traffic behavior of short-time spectrum occupancy for Internet-of-Things (IoT) applications in the shared bands to determine the existing interference. The proposed capturing method utilizes a software-defined radio to monitor the short bursts of IoT transmissions by capturing the time-series data which is converted to power spectral density to extract the observed occupancy. Furthermore, we propose the use of an unsupervised machine learning technique to enhance conventionally implemented energy detection methods. Our experimental results show that the temporal and frequency behavior of the spectrum can be well captured using the combination of two models, namely, semi-Markov chains and a Poisson-distribution arrival rate. We conduct an extensive measurement campaign in different urban environments and incorporate the spatial effect on the IoT shared spectrum.

In this paper, we lay an analytic framework for computing the downlink success probability of cellular networks, taking into account a frequency reuse distance as an interference mitigation scheme. We model the frequency reuse distance using tools from stochastic geometry, namely, we utilize the Matérn hard-core (MHC) point process to capture the effect of interference protection zones created around base stations. To model the overall cellular network, we introduce a new point process composed of N superimposed MHC processes, where each individual MHC process corresponds to a co-channel base station group; this new point process is called the union-MHC (UMHC) process. We further investigate the resulting performance of the UMHC process and present the link success probability in integral form. The success probability can be evaluated for an arbitrary fading model and an arbitrary number of orthogonal resource groups. We test the newly proposed model against the practical data sets from a network operator and observe a good match of the results.

Although the popularity of Software-Defined Networking (SDN) is increasing, it is also vulnerable to security attacks such as Denial of Service (DoS) attacks. Since in SDN, the control plane is isolated from the data plane, DoS attackers can easily target the control plane to impair the network infrastructure in addition to the data plane to degrade the user's Quality of Service (QoS). In our previous work, we introduced SECO, an SDN Secure Controller algorithm to detect and defend SDN against DoS attacks. Simulation results showed that SECO successfully defends SDN networks from DoS attacks. In this paper, we present SDN sEcure COntrol and Data Plane (SECOD), which is an improved version of SECO. Basically, SECOD introduces new triggers to detect and prevent DoS attacks in both control and data planes. Moreover, SECOD is implemented and tested using SDN-based hardware testbed, OpenFlow-based switch, and RYU controller to capture the dynamics of realistic hardware and software. The results show that SECOD successfully detects and effectively mitigates DoS attacks on SDN networks keeping data plane performance at 99.72% compared to a network not under attack.

As the use of automotive radar increases, performance limitations associated with radar-to-radar interference will become more significant. In this paper, we employ tools from stochastic geometry to characterize the statistics of radar interference. Specifically, using two different models for the spatial distributions of vehicles, namely, a Poisson point process and a Bernoulli lattice process, we calculate for each case the interference statistics and obtain analytical expressions for the probability of successful range estimation. This paper shows that the regularity of the geometrical model appears to have limited effect on the interference statistics, and so it is possible to obtain tractable tight bounds for the worst case performance. A technique is proposed for designing the duty cycle for the random spectrum access, which optimizes the total performance. This analytical framework is verified using Monte Carlo simulations.

This paper describes the implementation of a miniature open-source and cost-effective SLAM-robot, utilizing a novel occupancy-grid SLAM algorithm based on the concept of random-finite-sets (RFS). This robotic platform is remotely controlled to move and scan unknown environments using a differential drive system algorithm, sending instantaneous position feedback to the remote operator. The mobile robot utilizes a LIDAR-Lite 2 laser range finder to map the environment while simultaneously estimating its position and orientation within the map. Even though there are many mobile robots that implement this behavior, the main advantage in this proposed robotic platform is modeling of LIDAR measurements at each scan as a RFS. This model provides robustness against the random count of received returns, due to false and missed detections, allowing the use of an inexpensive LIDAR sensor and commercial off the shelf hardware.

Stepped frequency radar technology, where the transmit waveform consists of a sequence of tones, has long been suggested for cost-effective and high-resolution applications. One recent use of this technology is in automotive application where, in addition to cost-effectiveness, a random stepped frequency (RSF) waveform can significantly reduce the interference between vehicles. In this paper we provide a generic framework for the range and Doppler measurements for multiple targets. We further suggest two possible methods for reducing the computational complexity of RSF waveforms processing, which is important for future automotive applications.

This paper lays a mathematical framework for estimating the energy saving of a relay assisting a pair of wireless devices. We derive closed-form expressions for describing the geometrical zone where relaying is energy efficient. In addition, we obtain the probabilistic distribution of the energy saving introduced by relays that are randomly distributed according to a spatial Poisson point process. Furthermore, we present a comparison methodology for fairly evaluating the energy consumption of conventional cellular network from one side and relay-assisted device-to-device communication from another side. Results suggest that a significant energy saving can be achieved when relay-assisted device-to-device communication is adopted for distances below a certain threshold. In order to test the analytical framework, we perform Monte-Carlo simulations and compare the results with those obtained from the mathematical framework.

In this paper we introduce a novel analytic approach in clustering wireless nodes on ground fields through the assistance of an aerial base station, forming an aerial backhauled network. On the ground, some of the deployed nodes act as cluster heads, and aggregate and relay the terrestrial data towards the aerial platform. We present a novel cluster-head selection algorithm based on Matern hard-core point process, and then obtain the analytical formulation to optimize the spacing between cluster heads to minimize the overall energy consumption. The formulated problem utilizes stochastic geometry to capture the random nature of the locations of the deployed nodes, leading to a tractable analysis of the expected network metrics. Furthermore, we compare the performance of the proposed approach with other clustering algorithms, and show the improvement in energy efficiency.

The reliable prediction of coverage footprint resulting from an airborne wireless radio base station, is at utmost importance, when it comes to the new emerging applications of air-to-ground wireless services. These applications include the rapid recovery of damaged terrestrial wireless infrastructure due to a natural disaster, as well as the fulfillment of sudden wireless traffic overload in certain spots due to massive movement of crowds. In this paper, we propose a statistical propagation model for predicting the air-to-ground path loss between a low altitude platform and a terrestrial terminal. The prediction is based on the urban environment properties, and is dependent on the elevation angle between the terminal and the platform. The model shows that air-to-ground path loss is following two main propagation groups, characterized by two different path loss profiles. In this paper we illustrate the methodology of which the model was deduced, as well as we present the different path loss profiles including the occurrence probability of each.