arXiv subjects

Akram Al-Hourani

Publications and source records attributed to Akram Al-Hourani.

At least 19 recordsLinked to original sources

arXiv2026

Pseudo-Noise Superposition for Finite-Alphabet Physical Layer Security

Physical-layer security based on pseudo-noise (PN) superposition is a promising approach for mitigating eavesdropping in future wireless systems. However, under Shannon's capacity formulation with Gaussian signaling, achieving secrecy typically requires allocating substantial transmit power to PN, resulting in a significant reduction in achievable information rate and limiting practical applicability. This limitation is alleviated when finite-alphabet modulation schemes, such as M-ary Quadrature Amplitude Modulation ($M$-QAM), are employed, as expected in practical 6G transceivers. In this work, we analyze the information rate performance of PN-assisted systems under $M$-QAM signaling using mutual information and derive the corresponding achievable secrecy rate. The impact of PN power allocation on both the legitimate user and the eavesdropper is investigated across different modulation orders and channel conditions. Monte Carlo simulations are conducted to evaluate system behavior under varying user and eavesdropper channel conditions and to examine how PN power allocation influences secrecy performance. The results show that, at sufficiently high signal-to-noise ratio (SNR), the information rate becomes largely insensitive to PN power allocation, enabling near-perfect secrecy with $M$-QAM modulation-highlighting a key departure from Shannon-capacity-based secrecy analyses and underscoring the practicality of finite-alphabet security mechanisms for 6G wireless systems.

eess.SP↗

arXiv2025

Null-Shaping for Interference Mitigation in LEO Satellites Under Location Uncertainty

Radio frequency interference (RFI) poses a growing challenge to satellite communications, particularly in uplink channels of Low Earth Orbit (LEO) systems, due to increasing spectrum congestion and uncertainty in the location of terrestrial interferers. This paper addresses the impact of RFI source position uncertainty on beamforming-based interference mitigation. First, we analytically characterize how geographic uncertainty in RFI location translates into angular deviation as observed from the satellite. Building on this, we propose a robust null-shaping framework to increase resilience in the communication links by incorporating the probability density function (PDF) of the RFI location uncertainty into the beamforming design via stochastic optimization. This allows adaptive shaping of the antenna array's nulling pattern to enhance interference suppression under uncertainty. Extensive Monte Carlo simulations, incorporating realistic satellite orbital dynamics and various RFI scenarios, demonstrate that the proposed approach achieves significantly improved mitigation performance compared to conventional deterministic designs.

eess.SP↗

arXiv2024

Interference Mitigation in LEO Constellations with Limited Radio Environment Information

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.

eess.SP↗

arXiv2024

Deep Learning Methods for Device Identification Using Symbols Trace Plot

Devices authentication is one crucial aspect of any communication system. Recently, the physical layer approach radio frequency (RF) fingerprinting has gained increased interest as it provides an extra layer of security without requiring additional components. In this work, we propose an RF fingerprinting based transmitter authentication approach density trace plot (DTP) to exploit device-identifiable fingerprints. By considering IQ imbalance solely as the feature source, DTP can efficiently extract device-identifiable fingerprints from symbol transition trajectories and density center drifts. In total, three DTP modalities based on constellation, eye and phase traces are respectively generated and tested against three deep learning classifiers: the 2D-CNN, 2D-CNN+biLSTM and 3D-CNN. The feasibility of these DTP and classifier pairs is verified using a practical dataset collected from the ADALM-PLUTO software-defined radios (SDRs).

eess.SP↗

arXiv2023

Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks

Space communications, particularly massive satellite networks, re-emerged as an appealing candidate for next generation networks due to major advances in space launching, electronics, processing power, and miniaturization. However, massive satellite networks rely on numerous underlying and intertwined processes that cannot be truly captured using conventionally used models, due to their dynamic and unique features such as orbital speed, inter-satellite links, short pass time, and satellite footprint, among others. Hence, new approaches are needed to enable the network to proactively adjust to the rapidly varying conditions associated within the link. Artificial intelligence (AI) provides a pathway to capture these processes, analyze their behavior, and model their effect on the network. This article introduces the application of AI techniques for integrated terrestrial satellite networks, particularly massive satellite network communications. It details the unique features of massive satellite networks, and the overarching challenges concomitant with their integration into the current communication infrastructure. Moreover, this article provides insights into state-of-the-art AI techniques across various layers of the communication link. This entails applying AI for forecasting the highly dynamic radio channel, spectrum sensing and classification, signal detection and demodulation, inter-satellite and satellite access network optimization, and network security. Moreover, future paradigms and the mapping of these mechanisms onto practical networks are outlined.

cs.NI↗

arXiv2023

On Delay Performance in Mega Satellite Networks with Inter-Satellite Links

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.

cs.IT↗

arXiv2023

Spiking Neural Networks for Detecting Satellite-Based Internet-of-Things Signals

With the rapid growth of IoT networks, ubiquitous coverage is becoming increasingly necessary. Low Earth Orbit (LEO) satellite constellations for IoT have been proposed to provide coverage to regions where terrestrial systems cannot. However, LEO constellations for uplink communications are severely limited by the high density of user devices, which causes a high level of co-channel interference. This research presents a novel framework that utilizes spiking neural networks (SNNs) to detect IoT signals in the presence of uplink interference. The key advantage of SNNs is the extremely low power consumption relative to traditional deep learning (DL) networks. The performance of the spiking-based neural network detectors is compared against state-of-the-art DL networks and the conventional matched filter detector. Results indicate that both DL and SNN-based receivers surpass the matched filter detector in interference-heavy scenarios, owing to their capacity to effectively distinguish target signals amidst co-channel interference. Moreover, our work highlights the ultra-low power consumption of SNNs compared to other DL methods for signal detection. The strong detection performance and low power consumption of SNNs make them particularly suitable for onboard signal detection in IoT LEO satellites, especially in high interference conditions.

cs.IT↗

arXiv2022

HybNet: A Hybrid Deep Learning -- Matched Filter Approach for IoT Signal Detection

Random access schemes are widely used in IoT wireless access networks to accommodate simplicity and power consumption constraints. As a result, the interference arising from overlapping IoT transmissions is a significant issue in such networks. Traditional signal detection methods are based on the well-established matched filter using the complex conjugate of the signal, which is proven as the optimal filter under additive white Gaussian noise. However, with the colored interference arising from the overlapping IoT transmissions, deep learning approaches are being considered as a better alternative. In this paper, we present a hybrid framework, HybNet, that switches between deep learning and match filter pathways based on the detected interference level. This helps the detector work in a broader range of conditions, optimally leveraging the matched filter and deep learning robustness. We compare the performance of several possible data modalities and detection architectures concerning the interference-to-noise ratio, demonstrating that the proposed HybNet surpasses the complex conjugate matched filter performance under interference-limited scenarios.

cs.IT↗

arXiv2022

Improving IoT-over-Satellite Connectivity using Frame Repetition Technique

Through Non-Terrestrial Networks (NTN), a global coverage connecting areas with minimal or no terrestrial services is envisaged using satellite and air-borne platforms. However, one of the challenges to delivering NTN communications is the increased levels of path-loss, relative to typical terrestrial scenarios, due to the vast communication distance towards satellite platforms. Typically, cellular IoT technologies adopt frame repetitions where many redundant data transmissions are included to enhance its success rate. This can be also extended to satellite links to counter the effect of long distance communications on the link budget. In this paper, we put forth an analytic framework that captures the repetition behavior based on the probability of line-of-sight (LoS), as it heavily influences the propagation conditions. We analyze the coverage performance in terms of the frame success rate, which allows us to tune the repetitions behavior to suit a given satellite admittance region. Furthermore, we obtain an optimal global average success rate by considering the availability of satellites. The presented framework can facilitate coverage enhancements for IoT-over-Satellite networks.

cs.IT↗

arXiv2022

IoT Coverage Enhancement using Repetition in Energy Constrained Devices: an Analytic Approach

Novel Internet-of-Things (IoT) access technologies are emerging as part of the next generation cellular networks. These technologies are specifically oriented towards energy-limited IoT devices that are scattered far away from their serving base station. One of the key methods of achieving deep coverage is via repeated transmissions of data. However, repetition leads to higher energy consumption and reduces the device battery-lifetime. Thus, a trade-off between coverage and energy consumption exists and requires careful investigation. This paper evaluates the effects of transmission repetition on enhancing the probability of coverage and the cost of the incurred energy. Using an empirical repetition profile based on traffic load and device distance from the base station, we derive the coverage probability and energy profile models for IoT links that utilize two different diversity combining techniques. In particular, we focus on two common diversity combining that are; (i) Selection Combining (SC), and (ii) Maximal Ratio Combining (MRC). We utilize tools from stochastic geometry to formulate an analytic framework that compares these two combining methods. This framework can aid network designers in jointly maximizing the network coverage while minimizing the energy expenditure of devices.

cs.IT↗

arXiv2021

Optimal Beamwidth and Altitude for Maximal Uplink Coverage in Satellite Networks

Dense satellite constellations recently emerged as a prominent solution to complementing terrestrial networks in attaining true global coverage. As such, analytic optimization techniques can be adopted to rapidly maximize the benefits of such satellite networks. The paper presents a framework that relies on two primary tuning parameters to optimize the uplink performance; (i) the constellation altitude and (ii) the satellite antenna beamwidth. The framework leverages tools from stochastic geometry to derive analytical models that formulate a parametric uplink coverage problem which also includes user traffic demand as an input. This allows operators to devise uplink expansion strategies to cater for expanding user demand. The framework demonstrates that fine-tuning of these parameters can significantly enhance the network capacity. We show that the optimization of random constellations provides a close match to that of practical satellite constellations such as Walker-delta and Walker-star.

cs.IT↗

arXiv2021

Next Generation Mega Satellite Networks: Opportunities, Challenges, and Performance

Digital connectivity has become the foundation of prosperity and an essential need for functioning societies. Despite this dependence, limitation on Internet access remains a prevalent issue, largely hinged on socioeconomic and geographic factors. A promising solution to attain global access equality is based on integrated terrestrial-satellite networks that rely on low Earth orbit (LEO) mega constellations. While the benefits of LEO constellations complement the shortcomings of terrestrial networks, their incorporation impacts the network design, adding complexity and challenges. This article presents a systematic analysis of next generation LEO mega satellite constellations and outlines opportunities by virtue of the many benefits these constellations can provide and highlights the major challenges. Furthermore, it provides a synopsis of analytic models and underscores modern simulation approaches for next generation mega satellite constellations. This provides network designers with the necessary insights into satellite network performance.

cs.IT↗

arXiv2021

Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands

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 growing need for better understanding of the spectrum occupancy with newly emerging access technologies supporting the Internet of Things. In this paper, we present a framework to capture and model the traffic behavior of short-time spectrum occupancy for 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.

cs.LG↗

arXiv2021

On the Bound of Energy Consumption in Cellular IoT Networks

Billions of sensors are expected to be connected to the Internet through the emerging Internet of Things (IoT) technologies. Many of these sensors will primarily be connected using wireless technologies powered using batteries as their sole energy source which makes it paramount to optimize their energy consumption. In this paper, we provide an analytic framework of the energy-consumption profile and its lower bound for an IoT end device formulated based on Shannon capacity. We extend the study to model the average energy-consumption performance based on the random geometric distribution of IoT gateways by utilizing tools from stochastic geometry and real measurements of interference in the ISM-band. Experimental data, interference measurements and Monte-Carlo simulations are presented to validate the plausibility of the proposed analytic framework, where results demonstrate that the current network infrastructures performance is bounded between two extreme geometric models. This study considers interference seen by a gateway regardless of its source.

cs.IT↗

arXiv2021

Modeling Uplink Coverage Performance in Hybrid Satellite-Terrestrial Networks

Once deemed a far-fetched vision, emerging deployments of massive satellite constellations will soon offer true global coverage. When these constellations overlay the evolving terrestrial networks, a new hybrid continuum is formed which has the potential to provide uninterrupted coverage. In this paper, we provide an analytic framework for the uplink coverage probability in hybrid satellite-terrestrial networks. The framework extends well-developed terrestrial models that utilize tools from stochastic geometry by incorporating an additional layer that fits the emerging next generation satellite-terrestrial networks. The paper captures the impact of both (i) the constellation size, and (ii) the terrestrial base station density on the coverage of the uplink traffic which is dominant in applications relying on wireless sensor networks, such as the Internet of Things. This framework provides insights that guide the design of hybrid network infrastructures for a desired quality of service.

cs.IT↗

arXiv2016

Stochastic Geometry Methods for Modelling Automotive Radar Interference

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 vehicle spacial distributions, 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. Our study 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 worst case performance. A technique is proposed for designing the duty cycle for random spectrum access which optimizes the total performance. This analytical framework is verified using Monte-Carlo simulations.

cs.IT↗

arXiv2016

Nearest Neighbour Distance Distribution in Hard-Core Point Processes

In this paper we present an analytic framework for formulating the statistical distribution of the nearest neighbour distance in hard-core point processes. We apply this framework to Matérn hard-core point process (MHC) to derive the cumulative distribution function of the contact distance in three cases. The first case is between a point in an MHC process and its nearest neighbour from the same process. The second case is between a point in an independent Poisson point process and the nearest neighbour from an MHC process. The third case is between a point in the complement of an MHC process and its sibling MHC process. We test the analytic results against Monte-Carlo simulations to verify their consistency.

cs.IT↗

arXiv2016

Regularizing Random Points: Complementary Matérn Hard-Core Point Process

In this paper we present a tractable approach for regularizing randomly placed points, by splitting them into two subsets: the first is generated by means of the Matérn hard-core point process, while the remaining points constitute the complementary Matérn hard-core point process. We study the characteristics of these processes, deriving its pair-correlation functions, and the distribution of the distance to the nearest neighbour. The results have several applications in wireless communications, including the modeling of wireless sensor networks, where we investigate an example of regularizing such networks and illustrate its advantage in reducing the energy consumption of wireless nodes.

math.ST↗