On Modeling Coverage and Rate of Random Cellular Networks under Generic Channel Fading

In this paper we provide an analytic framework for computing the expected downlink coverage probability, and the associated data rate of cellular networks, where base stations are distributed in a random manner. The provided expressions are in computable integral forms that accommodate generic channel fading conditions. We develop these expressions by modelling the cellular interference using stochastic geometry analysis, then we employ them for comparing the coverage resulting from various channel fading conditions namely Rayleigh and Rician fading, in addition to the fading-less channel. Furthermore, we expand the work to accommodate the effects of random frequency reuse on the cellular coverage and rate. Monte-Carlo simulations are conducted to validate the theoretical analysis, where the results show a very close match

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

Hierarchical routing protocols for wireless sensor network: a compressive survey

Wireless Sensor Networks (WSNs) are one of the key enabling technologies for the Internet of Things (IoT). WSNs play a major role in data communications in applications such as home, health care, environmental monitoring, smart grids, and transportation. WSNs are used in IoT applications and should be secured and energy efficient in order to provide highly reliable data communications. Because of the constraints of energy, memory and computational power of the WSN nodes, clustering algorithms are considered as energy efficient approaches for resource-constrained WSNs. In this paper, we present a survey of the state-of-the-art routing techniques in WSNs. We first present the most relevant previous work in routing protocols surveys then highlight our contribution. Next, we outline the background, robustness criteria, and constraints of WSNs. This is followed by a survey of different WSN routing techniques. Routing techniques are generally classified as flat, hierarchical, and location-based routing. This survey focuses on the deep analysis of WSN hierarchical routing protocols. We further classify hierarchical protocols based on their routing techniques. We carefully choose the most relevant state-of-the-art protocols in order to compare and highlight the advantages, disadvantage and performance issues of each routing technique. Finally, we conclude this survey by presenting a comprehensive survey of the recent improvements of Low-Energy Adaptive Clustering Hierarchy (LEACH) routing protocols and a comparison of the different versions presented in the literature

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)

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

Diagnostic performance and clinical impact of Ga-68-PSMA-11 PET/CT imaging in early relapsed prostate cancer after radical therapy: a prospective multicenter study (IAEA-PSMA study)

Biochemical recurrence (BCR) is a clinical challenge in prostate cancer (PCa) patients, as recurrence localization guides subsequent therapies. The use of PET with prostate-specific membrane antigen (PSMA) provides better accuracy than conventional imaging practice. This prospective, multicenter, international study was performed to evaluate the diagnostic performance and clinical impact of PSMA PET/CT for evaluating BCR in PCa patients in a worldwide scenario. METHODS : Patients were recruited from 17 centers in 15 countries. Inclusion criteria were histopathologically proven prostate adenocarcinoma, previous primary treatment, clinically established BCR, and negative conventional imaging (CT plus bone scintigraphy) and MRI results for patients with PSA levels of 4-10 ng/mL. All patients underwent PET/CT scanning with 68Ga-PSMA-11. Images and data were centrally reviewed. Multivariate logistic regression analysis was applied to identify the independent predictors of PSMA-positive results. Variables were selected for this regression model on the basis of significant associations in the univariate analysis and previous clinical knowledge: Gleason score, the PSA level at the time of the PET scan, PSA doubling time, and primary treatment strategy. All patients were monitored for a minimum of 6 mo. RESULTS : From a total of 1,004 patients, 77.7% were treated initially with radical prostatectomy and 22.3% were treated with radiotherapy. Overall, 65.1% had positive PSMA PET/CT results. PSMA PET/CT positivity was correlated with the Gleason score, PSA level at the time of the PET scan, PSA doubling time, and radiotherapy as the primary treatment (P < 0.001). Treatment was modified on the basis of PSMA PET/CT results in 56.8% of patients. PSMA PET/CT positivity rates were consistent and not statistically different among countries with different incomes. CONCLUSION : This multicenter, international, prospective trial of PSMA PET/CT confirmed its capability for detecting local and metastatic recurrence in most PCa patients in the setting of BCR. PSMA PET/CT positivity was correlated with the Gleason score, PSA level at the time of the PET scan, PSA doubling time, and radiotherapy as the primary treatment. PSMA PET/CT results led to changes in therapeutic management in more than half of the cohort. The study demonstrated the reliability and worldwide feasibility of PSMA PET/CT in the workup of PCa patients with BCR.Partially funded by IAEA.https://jnm.snmjournals.orghj2023Nuclear Medicin