The emerging Internet-of-Things (IoT) network is expected to connect billions of devices, creating intelligent environments and transforming our daily lives. However, due to large-scale deployment, the broadcast nature of wireless channels, and the limited energy and computation capabilities of IoT devices, such networks are highly vulnerable to cyber-attacks and malware infiltration. In the presence of eavesdroppers, establishing a giant connection where devices can securely and privately communicate is particularly challenging. This motivates our study of a physical layer security (PLS) approach that introduces artificial noise (AN) into transmissions. Using tools from percolation theory, we analyze the probability of legitimate devices forming a giant component in both signal-to-noise ratio (SNR)- and signal-to-interference-plus-noise ratio (SINR)-based connectivity and intrinsically secure communication graphs. We show that by carefully designing the AN power fraction, legitimate devices can be protected from eavesdropping regardless of the eavesdroppers’ density and capabilities, leading to a significant reduction in the critical percolation density. We also prove the existence of an optimal AN fraction that minimizes this critical density, which remains consistent across both SNR and SINR graph models. Furthermore, we characterize the percolation behavior in SINR-based graphs and reveal that, while AN reduces the percolation threshold, efficient interference cancellation is necessary to counteract the reduced data transmission power and maintain robust connectivity.

Radio altimeter (RA) accuracy is crucial to aircraft safety in critical scenarios such as low visibility, close proximity to terrain, collision avoidance, and autoland (automatic landing) procedures. Nowadays, there is growing concern that possibly damaging interference from 5G communications systems in C-band may interfere with the operation of RA at 4.2 - 4.4 GHz. Some countries and international organisations have already taken corrective measures. In this work, we study a system where radio altimeter and 5G network exist in the same area. the system is composed of one airplane landing in a straight direction parallel to the runway and 5G base stations (5G-BSs) deployed on the ground. Our ultimate goal is to analyse the interference caused by 5G-BSs and ensure a safe operation environment for the radio altimeter. Using tools from stochastic geometry, we determine the CDF of the interference caused by the 5G-BSs. We use the probability of having an interference greater then a specific threshold as a metric. We want this metric to be as close as possible to zero. Thus, we propose different exclusion zones as a solution to mitigate interference. Then, we analyse the performance of the 5G network to evaluate the impact of deploying exclusion zones on it. We verify our findings using Monte-Carlo simulations and draw multiple useful insights.

Non-terrestrial networks (NTNs) are poised to play a critical role in next-generation mobile communications, offering enhanced flexibility, improved line-of-sight (LoS) conditions, and overcoming the limitations of terrestrial networks (TNs). Among NTN platforms, high altitude platform stations (HAPSs) have emerged as a promising solution to provide Internet connectivity to underserved regions, including rural areas, islands, and maritime zones, where traditional infrastructure deployment is costly and challenging to deploy. In this paper, we investigate the feasibility of large-scale HAPS deployment to connect island and maritime users, considering real-world shadowing effects on part of HAPSs caused by the presence of island building clusters. We first analyze the coverage performance of onshore (island) and offshore (remote sea) users, in which the channels between HAPSs and the user follow the shadowed Rician distributions and Rician distributions, respectively. Next, we introduce an evaluation method for nearshore users in a hybrid channel environment with HAPSs, and propose approximations that can reduce computational complexity. Based on the simulation results, we discuss how the distance from the island boundary (i.e. the relative remoteness of maritime users) affects coverage performance under different HAPS densities. We also emphasize the importance of choosing a balanced HAPS density or an advanced HAPS deployment scheme.

In this paper, we propose a novel network architecture where two types of aerial infrastructures together with a ground station provide connectivity to a remote area. A high altitude platform station (HAPS) is equipped with reconfigurable intelligent surface (RIS), called HAPS-RIS, to be exploited to assist the unmanned aerial vehicle (UAV)-based wireless networks. A key challenge in such networks is the restricted number of UAVs, which limits full coverage and leaves some users unsupported. To tackle this issue, we propose a hierarchical bilevel optimization framework including a leader and a follower problem. The users served by HAPS-RIS are in a zone called the HAPS-RIS zone and the users served by the UAVs are in another zone called the UAV zone. In the leader problem, the goal is to establish the zone boundary and practical RIS phase shift design that maximizes the number of users covered by HAPS-RIS while ensuring that users in this zone meet their rate requirements. This is achieved through our proposed practical relaxation method and the proposed dynamic radius-based zone association with RIS clustering (DyRaZARC) technique. The follower problem focuses on minimizing the number of UAVs required, ensuring that the rate requirements of the users in the UAV zone are met. This is addressed through our proposed novel geometry-informed machine learning method, called k-means adaptive dynamic UAV selection (KADUS) technique. Our study reveals that increasing the number of RIS elements significantly decreases the number of required UAVs.

In recent decades, minimizing data transmission delay has been a key metric for Ultra Reliable Low-Latency Communication (URLLC). However, low-delay transmission does not always ensure high-quality network service, particularly for real-time applications where the timeliness of data is crucial. This has led to the emergence of the Age of Information (Aol) as a key metric for tracking the freshness of transmitted updates. AoI is especially relevant in large-scale networks like the Internet of Things (IoT), where massive updates are frequently sent. IoT nodes in regions with poor terrestrial network coverage can benefit from Low Earth Orbit (LEO) satellites, which provide reliable connectivity. This work introduces a stochastic geometry-based model to study uplink AoI in fully loaded large-scale IoT networks, where both LEO satellites and ground nodes follow independent Poisson Point Processes (PPPs). In this model, each source node uses a non-preemptive transmission scheme, meaning it completes the current update transmission before handling new ones. Numerical results highlight the influence of varying satellite numbers and altitudes on AoI performance.

In the era of sixth-generation (6G) wireless communication, numerous applications are expected to be realized, including environmental monitoring, smart agriculture, remote education, security protection, and intelligent transportation systems. These scenarios require large-scale, continuous Internet services in forests, rivers, oceans, and road networks, to name a few, where optical cables are difficult to deploy. High-altitude platform stations (HAPSs) emerge as a promising solution, offering low-latency, high-capacity services while facilitating the establishment of vertical heterogeneous networks (vHetNets) in fiber-less areas. This paper investigates three HAPS-based solutions, where HAPSs can serve wireless devices directly or via gateway (GW) networks: the HAPS-to-device (H2D) scheme, the HAPS-to-GW-to-device (H2G2D) scheme, and the hybrid scheme. Leveraging percolation theory, we study the feasibility of large-scale continuous Internet coverage, where the key performance indicator (KPI) is the percolation probability. We discuss the subcritical and supercritical cases in different coverage schemes, and prove that the phase transition from zero to non-zero percolation probability appears when increasing the HAPS density or GW density. Numerical results verify that the curve of the critical condition of the phase transition exists between the derived lower bound and upper bound, which can help reduce the upfront cost of HAPS-based vHetNet solutions.

To achieve the Internet of Things (IoT) vision, Mobile Edge Computing (MEC) is a promising technology aimed at providing low-latency computing services to user equipment (UE). However, terrestrial MEC network struggles to provide service to UEs in remote and maritime region. Low Earth Orbit (LEO) satellite networks have the potential to overcome geographical restrictions and provide seamless global coverage for UEs. In this paper, we provide the first attempt to use stochastic geometry to investigate the performance of implementing space caching with LEO satellites (SATs) in the MEC network. We study a LEO satellite-assisted space caching MEC network, and LEO SATs can be equipped with servers to enable space caching, with the advantage of seamless coverage to assist terrestrial CSs for serving UEs in remote or maritime reigon. Using stochastic geometry and queuing theory, we establish an analytical framework for this MEC network. Meanwhile, we develop association strategies for UEs to connect with LEO SATs or CSs and utilize stochastic geometry to derive uplink and downlink coverage probabilities, considering the diversity of task and service types. On this basis, we employ the queuing theory to calculate the average delay to evaluate the system performance. Through Monte Carlo simulations and numerical results, the system performance is evaluated. The results show the potential of SAT spatial caching in improving the performance of the MEC network. Additionally, our results reveal useful insights such as the significant impact of the altitude and number of LEO SATs on the average delay of the network, providing helpful systemlevel recommendations for the design and configuration of the space-caching MEC network.

Understanding the interdependencies among communication performance metrics is crucial for designing efficient vehicle-assisted delay-tolerant networks (DTNs), as these metrics behave differently from those in traditional networks and directly affect data delivery reliability and timeliness. Motivated by this, and unlike most existing works that focus on protocol design, we analyze a vehicle-assisted DTN operated via TV white space (TVWS) from a communications perspective. Specifically, we consider a scenario where cities are connected to the cloud, while remote villages or IoT device clusters rely on vehicles traveling along highways to deliver data packages. Initially, we define and introduce three key performance metrics: uplink/ downlink transmission time, uplink peak age of information (PAoI), and request delay within this vehicle-assisted DTN. To comprehend the behaviors and correlations among these metrics, we first investigate a basic scenario considering only one road: while vehicles moving on a highway from one city to another, delivering data to the remote sites near the highway. Our results reveal correlations between uplink and downlink transmission times, the existence of an optimal transmitted data size per trip to minimize request delay and uplink PAoI, and that the request delay time can be approximated by a Gamma distribution when transmitting large data sizes. Subsequently, we extend the analysis to a more complex scenario involving multiple roads and information exchange between vehicles, with vehicle movement modeled as a 2D random walk. While the communication performance metrics exhibit similar trends to the basic scenario, we capture the effects of distance, vehicle arrival rate, and transmitted data size on data traveling time. Finally, we propose a special case that represents the upper bound of data traveling time within the DTN.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) systems are promising deployment paradigms for next-generation networks. They comprise a large number of base stations (BSs) and simultaneously serve all users over the same time and frequency resources. We analyze the performance of integrating aerial users, such as unmanned aerial vehicles (UAVs), into this novel system. Specifically, we consider a CF-mMIMO network containing both ground and aerial users and study the influence of system parameters on the signal-to-interference-plus-noise ratio (SINR) and rate coverage performance. Additionally, we use tools from stochastic geometry to capture the spatial randomness of users and BSs and compare the performance of the CF-mMIMO system to that of traditional small cell systems. Given the improvement of dedicated antennas, such as up-tilted/down-tilted antennas, on the performance of small cell systems, we include the up-tilted/down-tilted antenna model in this work and analyze the omnidirectional antenna model as a special case. We derive both the exact expressions and dominant-signal-based approximations for SINR and rate coverage. Our numerical results demonstrate that the CF-mMIMO system exhibits better performance at low values of SINR and rate thresholds and higher minimum achievable SINR compared to the small cell system. Furthermore, we observe that users benefit dramatically from increasing altitudes and establishing line-of-sight (LoS) channels with BSs.

Reconfigurable intelligent surfaces (RISs) have shown the potential to improve signal-to-interference-plus-noise ratio (SINR) related coverage, especially at high-frequency communications. However, assessing electromagnetic field exposure (EMFE) and establishing EMFE regulations in RIS-assisted large-scale networks remain open issues. This paper proposes a stochastic geometry (SG) based framework to characterize SINR and EMFE in such networks for downlink and uplink scenarios. Particularly, we carefully consider the association rule with the presence of RISs, accurate antenna pattern at base stations (BSs), fading model, and power control mechanism at mobile devices in the system model. Under the proposed framework, we derive the marginal and joint distributions of SINR and EMFE in downlink and uplink, respectively. The first moment of EMFE is also provided. Additionally, we design the compliance distance (CD) between a BS/RIS and a user to comply with the EMFE regulations. To facilitate efficient identification, we further provide approximate closed-form expressions for CDs. From numerical results of the marginal distributions, we find that in the downlink scenario, deploying RISs may not always be beneficial, as the improved SINR comes at the cost of increased EMFE. However, in the uplink scenario, RIS deployment is promising to enhance coverage while still maintaining EMFE compliance. By simultaneously evaluating coverage and compliance metrics through joint distributions, we demonstrate the feasibility of RISs in improving uplink and downlink performance. Insights from this framework can contribute to establishing EMFE guidelines and achieving a balance between coverage and compliance when deploying RISs.

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.

Deploying reconfigurable intelligent surfaces (RISs) offers the potential to improve coverage performance in mil-limeter wave (mmWave) communications. However, electric and magnetic field (EMF) exposure related to base station (BS) trans-mission and RIS reflection is still unclear. This paper provides an analytical framework to evaluate the EMF exposure in RIS-assisted mmWave cellular networks. The proposed framework provides insights to establish technical guidelines for ensuring EMF exposure within a safe limit. For example, by considering the compliance distance (CD) set for BSs, we explore the necessity of designing a similar CD for RISs.

This study presents a framework to analyze the performance of uplink localization with reconfigurable intelligent surfaces (RISs) in large-scale cellular networks. First, we propose a novel RIS-aided uplink localization algorithm, where the received signal strength (RSS) is observed at the base station (BS) for various pre-defined phase shift patterns of the RIS, i.e., a codebook of beams. We present a maximum likelihood estimator (MLE) and evaluate its performance by comparing it to the position error bound (PEB), defined as the square root of the Cramér-Rae lower bound (CRLB). Then, to analyze the localization performance on a large scale, we employ stochastic geometry tools, allowing the derivation of a tractable expression for the marginal PEB distribution. The obtained results demon-strate that the proposed algorithm converges to the CRLB for a narrow search grid, in a high SNR regime. Furthermore, higher BS density, number of RIS elements, and RIS element size are shown to enhance localization precision.

In this article, we analyze the Peak Age of Information (PAoI) in unmanned aerial vehicle (UAV)-assisted Internet of Thing (IoT) networks, in which the locations of IoT devices are modeled by a () and UAVs are deployed at the cluster centers to collect the status updates from the devices. Specifically, we consider that IoT devices can either monitor the same physical process or different physical processes and UAVs split their resources, time or bandwidth, to serve the devices to avoid intercluster interference. Using tools from stochastic geometry, we are able to compute the mean activity probability of IoT devices and the conditional success probability of an individual device. We then use tools from queuing theory to compute the PAoI under two load models and two scenarios for devices, respectively. Our numerical results show interesting system insights. We first show that for a low-data arrival rate, increasing the number of correlated devices can improve the PAoI for both load models. Next, we show that even though the time-splitting technique causes higher interference, it has a limited impact on the mean PAoI, and the mean PAoI benefits more from the time-splitting technique. This is because of the nature of UAV communication, especially at places where devices (users) are spatially clustered: shorter transmission distances and better communication channels, comparing the links established by the cluster UAV and serving devices (users) to links established by interferers.

This paper analyzes the uplink performance of IoT devices communicating with LEO satellites. We consider a scenario where IoT devices are deployed in hard-to-reach areas; hence, energy harvesting is the only sustainable solution. Due to the battery-free assumption of IoT devices, we consider a K-tier PB network to provide a sustainable energy supply for the IoT network. We particularly focus on two uplink communication scenarios: (i) direct communication from IoT devices to LEO satellites and (ii) indirect communication via GWs. The system’s performance is evaluated based on the two-stage ’harvest-then-transmit’ strategy, in which the IoT device harvests energy from all available energy sources during the first stage, referred to as the harvesting sub-slot. The harvested energy is then utilized to transmit information to its desired receiver in the second stage, known as the communication sub-slot. More precisely, we derive the joint uplink coverage probability if the following two conditions are satisfied: (i) the transmit power of the typical IoT device is sufficient, and (ii) the received SINR is above a predefined threshold. Furthermore, we investigate the effect of the proposed techniques on performance metrics such as the average number of successful data packet transmissions per day as well as the average amount of data uploaded per day. Finally, we explore the optimal network parameters to improve energy harvesting, communication reliability, and coverage for both direct and indirect scenarios.

The extensive deployment of low earth orbit (LEO) satellites introduces significant security challenges for communication security issues in Internet of Things (IoT) networks. With the rising number of satellites potentially acting as eavesdroppers, integrating physical-layer security (PLS) into satellite communications has become increasingly critical. However, these studies are facing challenges, such as dealing with dynamic topology difficulties, limitations in interference analysis, and the high complexity of performance evaluation. To address these challenges, for the first time, we investigate the PLS strategies in satellite communications using the stochastic geometry (SG) analytical framework. We consider the uplink communication scenario in an LEO-enabled IoT network, where the multitier satellites from different operators, respectively, serve as the legitimate receivers and eavesdroppers. In this scenario, we derive low-complexity analytical expressions for the security performance metrics, namely availability probability, successful communication probability, and secure communication probability. By introducing the power allocation parameters, we incorporate the artificial noise (AN) technique, which is an important PLS strategy, into this the analytical framework, and evaluate the gains it brings to secure transmission. In addition to the AN technique, we also analyse the impact of constellation configuration, physical-layer parameters, and network layer parameters on the aforementioned metrics.

In recent years, LEO satellite constellations have become envisioned as a core component of the next-generation wireless communication networks. The successive establishment of mega satellite constellations has triggered further demands for satellite communication advanced features: high reliability and low latency. In this article, we first establish a multi-objective optimization problem that simultaneously maximizes reliability and minimizes latency, then we solve it by two methods. According to the optimal solution, ideal upper bounds for reliability and latency performance of LEO satellite routing can be derived. Next, we design an algorithm for relay satellite subset selection, which can approach the ideal upper bounds in terms of performance. Furthermore, we derive analytical expressions for satellite availability, coverage probability, and latency under the stochastic geometry (SG) framework, and the accuracy is verified by Monte Carlo simulation. In the numerical results, we study the routing performance of three existing mega constellations and the impact of different constellation parameter configurations on performance. By comparing with existing routing strategies, we demonstrate the advantages of our proposed routing strategy and extend the scope of our research.

In this paper, we consider a novel cellular network for aerial users, which is composed of dedicated base stations (BSs), whose antennas are directed towards aerial users, and traditional terrestrial BSs (TBSs). Besides, the dedicated BSs are deployed on roadside furniture, such as lampposts and traffic lights, to achieve multiple features while occupying less space. Therefore, the locations of dedicated BSs and TBSs are modeled by a Poisson-line-Cox-process (PLCP) and Poisson point process (PPP), respectively. For the proposed network, we first compute the aerial coverage probability and show that the deployment of dedicated BSs improves the coverage probability in both high dense areas and rural areas. We then consider a cellular-connected UAV that has a flying mission and optimize its trajectory to maximize the minimal achievable signal-to-interference-plus-noise ratio (SINR) (Max-Min SINR). To obtain the Max-Min SINR and minimal time trajectory that satisfies the Max-Min SINR, we proposed two algorithms that are practical in large-scale networks. Finally, our results show that the optimal density of dedicated BSs which maximizes Max-Min SINR decreases with the increase of the road densities.

This article studies the reliability of multi-hop routing in a multi-tier hybrid satellite-terrestrial relay network (HSTRN). We evaluate the reliability of multi-hop routing by introducing interruption probability, which is the probability that no relay device (ground gateway or satellite) is available during a hop. The single-hop interruption probability is derived and extended to the multi-hop interruption probability using a stochastic geometry-based approach. Since the interruption probability in HSTRN highly depends on the priority of selecting communication devices at different tiers, we propose three priority strategies: (i) stationary optimal priority strategy, (ii) single-hop interruption probability inspired strategy, and (iii) density inspired strategy. Among them, the interruption probability under the stationary optimal priority strategy can approach the ideal lower bound. However, when analyzing an HSTRN with a large number of tiers, the stationary optimal priority strategy is computationally expensive. The single-hop interruption probability inspired strategy is expected to be a low-complexity but less reliable alternative to the stationary optimal priority strategy. In numerical results, we study the complementarity between terrestrial devices and satellites. Furthermore, analytical results for reliability are also applicable to the analysis of satellite availability, coverage probability, and ultra-reliable and low latency communications (URLLC) rate. Finally, we extend our original routing strategy into a multi-flow one with dynamic priority strategy.

Joint sensing and communication (JSAC) is regarded as a promising technology for future networks, which can reuse most devices of the systems in sensing and communication (S&C) and reduces the cost in terms of power and spectrum (P&S) critically. The current research considers the P&S allocation of S&C separately and then discusses the performance from different aspects. However, as an integrated system, the allocation strategy of P&S allocation affects the joint performance significantly. In this article, we use tools from stochastic geometry to study the coverage performance considering the trade-off of P&S allocation for JSAC with the principle requirements of small distance resolution (SDR) in sensing and high data rate (HDR) in communication. In particular, we model the locations of user equipment (UE) and base stations (BSs) as two different Poisson Point Processes and allocate P&S at BSs with two independent ratios. The sensing system will detect the surrounding environment and obtain UE positions. After that, an adaptive beamwidth for beamforming technology is applied in communication, which can save energy effectively. First, we introduce the distance resolution in sensing and special channel models in S&C with a high frequency. Then, considering the proposed system model, we separately model the interference in S&C. Further, the joint coverage probability (CP) of JSAC is derived as a function of densities of UE and BSs, required HDR and SDR, and allocation ratios of P&S. Finally, We draw multiple valuable system-level insights from the proposed analysis. For instance, we show that the SDR and HDR are the two main constraints to the maximum achievable CP with optimized allocations of P&S. Furthermore, we show that different densities of BSs should be considered in various scenarios. The revealed relationship between the densities of UE and BSs can be taken as a reference in practical applications.

Millimeter-wave (mmWave) integrated access and backhaul (IAB) has recently received considerable interest for its advantage in reducing the expenses related to the deployment of fiber optics, such as the Terragraph proposed by Meta’s Connectivity Lab. Terragraph networks aim to provide high-speed internet access to dense urban environments. However, due to the vulnerability to blockages and high path loss associated with mmWave frequencies, the proper deployment planning of mmWave networks is required to achieve the desired service quality. By obtaining a stable power supply through its tether connected to the ground, tethered unmanned aerial vehicle (UAV)-mounted base station (BS) can provide reliable communication service with the sacrifice of limited mobility. In this paper, we investigate the potential of incorporating tethered UAVs into Terragraph-like networks. To this end, we propose a novel deep reinforcement learning (DRL) framework that aims to minimize the overall deployment cost by optimizing the number of required UAVs and terrestrial BSs (TBSs), the hovering positions of deployed UAVs, and the multi-hop backhauling topology. Unlike the conventional DRL frameworks that focus on maximizing the expected cumulative or average reward, we formulate the proposed framework based on the max-Bellman optimality equation in order to maximize the maximum reward. Numerical results reveal that the proposed algorithm is able to yield significant reduction in terms of deployment cost. We also use case studies from cities in Asia, Europe, and North America to verify the practical applicability of the proposed framework.

Age of information (AoI) has introduced a new dimension into the design of real-time monitoring networks. However, how to achieve energy-efficient freshness-aware information transmission with energy-constrained Internet of Things (IoT) devices remains a challenge. To solve this challenge, in this paper, we consider a new metric called freshness-aware energy efficiency (FAEE), which is defined as the ratio of AoI improvement to transmission energy consumption. We study a freshness-aware integrated access and backhaul (IAB) network in which multiple IoT devices (IDs) that are accountable for generating and transmitting status updates frequently over time so as to maintain the freshness of information observed at the IAB donor. For this system setup, we first formulate three mixed discrete-continuous optimization problems in order to maximize the long-term average FAEE under different transmission scheduling schemes, including round-robin (RR), sub-channel allocation (SA), and time-resource allocation (TA). By constructing average-reward Markov decision processes (MDPs) with mixed discrete-continuous action space to model these optimization problems, we propose a novel proximal policy optimization (PPO) based deep reinforcement learning (DRL) framework, which is referred to as APO-CD, to learn sub-optimal policies for status update strategy and resource allocation. Extensive simulation results are provided to show the effectiveness of our proposed algorithm and to reveal several design insights of freshness-aware IAB networks.

The recent breakthroughs in electronic and photonic technologies enabled the design and implementation of intelligent reflective surfaces (IRSs) to manipulate electromagnetic waves and control the wireless environment. A promising application of IRSs is their integration with Terahertz (THz) communications. IRSs can cope with the blockage sensitivity of THz propagation by providing alternative line-of-sight (LoS) links to user equipment (UEs) which are initially blocked. However, deploying more IRSs may degrade the network performance as it leads to non-negligible interference levels. In this paper, we use tools from stochastic geometry to investigate the coverage probability of a downlink (DL) indoor THz network assisted by IRSs, which are added to a subset of the existing blockages. The numerical results reveal that there is an optimal density of IRSs that should be deployed to maximize the coverage of UEs in THz networks.

This work studies the signal-to-interference-plus-noise-ratio (SINR) meta distribution for the uplink transmission of a Poisson network with Rayleigh fading by using the dominant interferer-based approximation. The proposed approach relies on computing the mix of exact and mean-field analysis of interference. In particular, it requires the distance distribution of the nearest interferer and the conditional average of the rest of the interference. Using the widely studied fractional path-loss inversion power control and modeling the spatial locations of base stations (BSs) by a Poisson point process (PPP), we obtain the meta distribution based on the proposed method and compare it with the traditional beta approximation, as well as the exact results obtained via Monte-Carlo simulations. Our numerical results validate that the proposed method shows good matching and is time competitive.

Using drones for communications and transportation is drawing great attention in many practical scenarios, such as package delivery and providing additional wireless coverage. However, the increasing demand for unmanned aerial vehicles (UAVs) from industry and academia will cause aerial traffic conflicts in the future. This, in turn, motivates the idea of this article: multipurpose UAVs, acting as aerial wireless data relays and means of aerial transportation simultaneously, to deliver packages and data at the same time. This article aims to analyze the feasibility of using drones to collect and deliver data from the Internet of Things (IoT) devices to terrestrial base stations (TBSs) while delivering packages from warehouses to residential areas. We propose an algorithm to optimize the trajectory of UAVs to maximize the size of collected/delivered data while minimizing the total round trip time subject to the limited onboard battery of UAVs. Specifically, we use tools from stochastic geometry to model the locations of the IoT clusters and the TBSs and study the system performance with respect to energy efficiency, average size of collected/delivered data, and package delivery time. Our numerical results reveal that multifunctional UAVs have great potential to enhance the efficiency of both communication and transportation networks.

Underwater long-distance wireless communication (ULWC) is a critical challenge in many applications, such as marine environmental monitoring, underwater remote control, and underwater navigation, to name a few. However, very little literature focuses on ULWC, especially where the communication distance ups to thousands of kilometers, which is urgently required in the Underwater Internet of Things (UIoT) in the large-scale and deep sea. In this article, to improve the underwater communication capacity at a level of thousands of kilometers, we propose a three-hop underwater wireless acoustic communication (3H-UWAC) structure based on the sound fixing and ranging (SOFAR) channel. The proposed 3H-UWAC consists of transmitters, relay stations (RSs), and receivers. Different from the existing ULWC, 3H-ULWC can improve energy efficiency with a small vertical directivity angle (VDA). Due to the characteristics of UWAC, the straight-line communication link can be realized in the proposed three hops, and the communication distance can be increased to thousands of kilometers. Respecting the randomness of underwater devices, tools from stochastic geometry are used to model the spatial distributions of transmitters’, receivers’, and RSs’ locations. RSs are set on the SOFAR channel at a known depth. In the first hop, the transmitter sends information to the nearest first RS (NFRS) on the SOFAR plane. In the second hop, the NFRS sends information to the nearest RS, which is called the nearest second RS (NSRS), to the receiver on the SOFAR plane. In the third hop, SNFS sends information to the receiver. All three communication hops can be achieved with a narrow beam width, where the energy efficiency is improved critically. With given densities of transmitters, RSs, and receivers, the coverage probabilities (CPs) of the three hops (transmitter to first RS (FRS), FRS to second RS (SRS), and SRS to receiver) are analyzed, and the final CP from a transmitter to a receiver through the 3H link is derived. Insights about the effects of VDAs at the transmitters, FNRS, and SNRS, as well as the depths of transmitters and receivers, are revealed. A rapid optimization method is proposed based on the analytical results. The accuracy of the analysis is verified by Monte Carlo simulations.

This paper proposes a novel approach for computing the meta distribution of the signal-to-interference-plus-noise ratio (SINR) for the downlink transmission in a wireless network with Rayleigh fading. The novel approach relies on an approximation mix of exact and mean-field analysis of interference (dominant interferer-based approximation) to reduce the complexity of analysis and enhance tractability. In particular, the proposed approximation omits the need to compute the first or the second moment of the SINR that is used in the beta approximation typically adopted in the literature but requires of computing the joint distance distributions. We first derive the proposed approximation based on a Poisson point process (PPP) network with a standard path-loss and Rayleigh fading and then illustrate its accuracy and operability in another four widely used point processes: Poisson bipolar network, Matérn cluster process (MCP),$K$-tier PPP and Poisson line Cox process (PLCP). Specifically, we obtain the SINR meta distribution for PLCP networks for the first time. Even though the proposed approximation looks simple but it shows good matching in comparison to the popular beta approximation as well as the Monte-Carlo simulations, which opens the door to adopting this approximation in more advanced network architectures.

The meta distribution of the signal-to-interference-plus-noise ratio (SINR) provides fine-grained information about each link’s performance in a wireless system and the reliability of the whole network. While the UAV-enabled network has been studied extensively, most of the works focus on the spatial average performance, such as coverage probability, while SINR meta distribution has received less attention. In this paper, we use the SINR meta distribution to systematically analyze the improvement and the influence of deploying UAVs on the reliability of a wireless network. We first derive the$b$-th moments of the conditional success probability of the UAV-enabled network and give the approximated expressions derived by Gil-Pelaez theorem and the beta approximation of the meta distribution. Our numerical results show that deploying UAVs in wireless networks in most cases can greatly improve the system reliability, which denotes the fraction of users achieving cellular coverage, especially for the spatially-clustered users. In addition, establishing LoS links is not always beneficial since it also increases the interference. For instance, with the increase of the SINR threshold, the system reliability of a high LoS probability environment decreases dramatically and it is even lower than a low LoS probability environment. We also show that in highrise urban areas, UAVs can help in establishing extremely reliable (very high SINR) links.

Mobile edge computing (MEC) can dramatically promote the computation capability and prolong the lifetime of mobile users (MUs) by offloading computation-intensive tasks to edge cloud. In this paper, a spatial-random two-tier heterogeneous network (HetNet) is modelled to feature random node distribution, where the small-cell base stations (SBSs) and the macro base stations (MBSs) are cascaded with servers with different levels of computing and storage capacity. Only a certain type of application services and finite number of offloaded tasks can be cached and processed in the resource-limited edge server. For that setup, we investigate the performance of two offloading strategies corresponding to integrated access and backhaul (IAB)-enabled MEC networks and traditional cellular MEC networks. Using tools from stochastic geometry and queuing theory, we derive the average delay for the two different strategies, in order to better understand the influence of IAB on MEC networks. Simulations results are provided to verify the derived expressions and to reveal various system-level insights.

Installing more base stations (BSs) into the existing cellular infrastructure is an essential way to provide greater network capacity and higher data rates in the 5th-generation cellular networks (5G). However, a non-negligible amount of the population is concerned that such network densification will generate a notable increase in exposure to electric and magnetic fields (EMF) over the territory. In this paper, we analyze the downlink, uplink, and joint downlink&uplink exposure induced by the radiation from BSs and personal user equipment (UE), respectively, in terms of the received power density and exposure index. In our analysis, we consider the EMF restrictions set by the regulatory authorities such as the minimum distance between restricted areas (e.g., schools and hospitals) and BSs, and the maximum permitted exposure. Exploiting tools from stochastic geometry, mathematical expressions for the coverage probability and statistical EMF exposure are derived and validated. Tuning the system parameters such as the BS density and the minimum distance from a BS to restricted areas, we show a trade-off between reducing the population’s exposure to EMF and enhancing the network coverage performance. Then, we formulate optimization problems to maximize the performance of the EMF-aware cellular network while ensuring that the EMF exposure complies with the standard regulation limits with high probability. For instance, the exposure from BSs is two orders of magnitude less than the maximum permissible level when the density of BSs is less than$20 \text {BSs/km}^{2}$.

A new generation of aerial vehicles is hopeful to be the next frontier for the transportation of people and goods, becoming even as important as ground users in the communication systems. To enhance the coverage of aerial users, appropriate adjustments should be made to the existing cellular networks that mainly provide services for ground users by the down-tilted antennas of the terrestrial base stations (BSs). It is promising to up-tilt the antennas of a subset of BSs for serving aerial users through the mainlobe. With this motivation, in this work, we use tools from stochastic geometry to analyze the coverage performance of the adjusted cellular network (consisting of the up-tilted BSs and the down-tilted BSs). Correspondingly, we present exact and approximate expressions of the signal-to-interference ratio (SIR)-based coverage probabilities for users in the sky and on the ground, respectively. Numerical results verify the analysis accuracy and clarify the advantages of up-tilting BS antennas on the communication connectivity of aerial users without the potential adverse impact on the quality of service (QoS) of ground users. Moreover, it is unveiled that there exists an optimal value of the up-tilted/down-tilted BS density ratio for maximizing the coverage probability of the aerial or ground users.

Wireless communications over Terahertz (THz)-band frequencies are vital enablers of ultra-high rate applications and services in sixth-generation (6G) networks. However, THz communications suffer from poor coverage because of inherent THz features such as high penetration losses, significant molecular absorption, and severe path loss. To surmount these critical challenges and fully exploit the THz band, we explore a coexisting radio frequency (RF) and THz finite indoor network in which THz small cells are deployed to provide high data rates, and RF macrocells are deployed to satisfy coverage requirements. Using stochastic geometry tools, we assess the performance of coexisting RF and THz networks and derive tractable analytical expressions for the coverage probability and average achievable rate. The analytical results are validated with Monte-Carlo simulations. Several insights are devised for accurate tuning and optimization of THz system parameters, including the THz bias, and the fraction of THz access points (APs) to deploy. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz AP improves the rate significantly but may degrade the coverage performance. Furthermore, the location of a user in the finite area highly affects the fraction of THz APs that optimizes its quality of service.

The use of unmanned aerial vehicles (UAVs) is strongly advocated for sixth-generation (6G) networks, as the 6G standard will not be limited to improving broadband services, but will also target the extension of the geographical cellular coverage. In this context, the deployment of UAVs is considered a key solution for seamless connectivity and reliable coverage. That being said, it is important to underline that although UAVs are characterized by their high mobility and their ability to establish line-of-sight (LOS) links, their use is still impeded by several factors such as weather conditions, their limited computing power, and, most importantly, their limited energy. In this work, we are aiming for the novel technology that enables indefinite wireless power transfer for UAVs using laser beams. We propose a novel UAV deployment strategy, based on which we analyze the overall performance of the system in terms of wireless coverage. To this end, we use tractable tools from stochastic geometry to model the complex communication system. We analyze the user’s connectivity profile under different laser charging capabilities and in different type of environments. We show a decrease in the coverage probability by more than 12% in moderate-to-strong turbulence conditions compared to low turbulence conditions. We also show how the connection rate to the aerial network significantly decreases in favor of the terrestrial network for short laser charging ranges. We conclude that laser-powered drones are considered interesting alternatives when placed in LOS with users, in low-to-moderate optical turbulence, and at reasonable ranges from the charging stations.

This paper analyzes the energy efficiency of a novel system model where unmanned aerial vehicles (UAVs) are used to provide coverage for user hotspots (user clusters) and are deployed on charging pads to enhance the flight time. We introduce a new notion of “cluster pairs” to capture the dynamic nature of the users spatial distribution in order to exploit one of the top advantages of UAVs, which is the mobility and relocation flexibility. Using tools from stochastic geometry, we first derive a new distance distribution that is vital for the energy efficiency analysis. Next, we compute the coverage probability under two deployment strategies: (i) one UAV per cluster pair, and (ii) one UAV per cluster. Finally, we compute the energy efficiency for both strategies. Our numerical results reveal which of the two strategies is better for different system parameters. Our work investigates some new aspects of the UAV-enabled communication system such as the dynamic density of users and the advantages or disadvantages of one- or two-UAV deployment strategies per cluster pair. By considering the relationships between the densities of user cluster pairs and the charging pads, it is shown that an optimal cluster pair density exists to maximize the energy efficiency.

Using Unmanned Aerial Vehicles (UAVs) to enhance network coverage has proven a variety of benefits compared to terrestrial counterparts. One of the commonly used mathematical tools to model the locations of the UAVs is stochastic geometry (SG). However, in the existing studies, both users and UAVs are often modeled as homogeneous point processes. In this paper, we consider an inhomogeneous Poisson point process (PPP)-based model for the locations of the users that captures the degradation in the density of active users as we move away from the town center. In addition, we propose the deployment of aerial vehicles following the same inhomogeneity of the users to maximize the performance. In addition, a multi-tier network model is also considered to make better use of the rich space resources. Then, the analytical expressions of the coverage probability for a typical user and the total coverage probability are derived. Finally, we optimize the coverage probability with limitations of the total number of UAVs and the minimum local coverage probability. Finally we give the optimal UAV distribution parameters when the maximum overall coverage probability is reached.

Due to its potential to enable global connectivity in remote locations, such as rural areas and islands, Space-Air-Ground networks have become an ambitious solution for terrestrial communication in the sixth generation (6G) wireless communication network. In this paper, we propose a novel structure of Space-Air-Ground-Sea integrated networks (SAGSINs) to study and derive the coverage probability (CP) of users who are annotated as surface stations (SSs) on the far-reaching ocean surface that is far away from the coastline. By incorporating different types of relays such as onshore stations (OSs), tethered balloons (TBs), high altitude platforms (HAPs), and satellites (SATs), communication links between the terrestrial core connected base stations (CCBSs) and SSs are established via one of the four types of relay stations. Considering practical scenarios with a random distribution of SSs, we model the channel using the point-to-area model, which is recommended by ITU (for OSs to SS), the Rician model (for TBs or HAPs to SS), and the Shadowed-Rician model (for SATs to SS). When the SS’s distance from the coastline continues to increase from zero, since different channel models are considered, different relay stations will result in specific received signal strengths at SSs. The most powerful relay station will be chosen as the relay at one time. Hence, as we move away from the coastline, the respective strengths of the different types of relay stations vary, and hence, the association preference (among HAPs, OSs, TBs, and SATs) of the SSs changes leading to a CP value high enough even at locations far away from the coastline into the ocean. We analyze the CP using tools from stochastic geometry. Comparisons of CP between the integrated system with four types of relay stations and the single relay station system (only one type of relay station available) are represented. Numerical results verified by Monte-Carlo simulations reveal insights into the applicability of SAGSINs.

Underwater acoustic communication networks (UACNs) are considered a key enabler to the Underwater Internet of Things (UIoT). UACN is regarded as essential for various marine applications, such as monitoring, exploration, and trading. However, a large part of existing literature disregards the 3-D nature of the underwater communication system. In this article, we propose a$K$-tier UACN that acts as a gateway that connects the UIoT with the space–air–ground–sea integrated system (SAGSIS). The proposed network architecture consists of several tiers along the vertical direction with adjustable depths. On the horizontal dimension, the best coverage probability (CP) is computed and maximized by optimizing the densities of surface stations (SSs) in each tier. On the vertical dimension, the depth of each tier is also optimized to minimize intertier interference and maximize overall system performance. Using tools from stochastic geometry, the total CP of the proposed$K$-tier network is analyzed. For given spatial distribution of UIoT device’s depth, the best CP can be achieved by optimizing the depths of the transceivers connected to the SSs through a tether. We verify the accuracy of the analysis using Monte Carlo simulations. In addition, we draw multiple useful system-level insights that help optimize the design of underwater 3-D networks based on the given distribution of UIoT device’s depths.

This paper considers a dual-hop cooperative satellite-unmanned aerial vehicle (UAV) communication system including a satellite (S) and a group of cluster headers (CHs) which are respectively with a group of uniformly distributed UAVs. Specifically, the CHs serve as aerial decode-and-forward relays to forward the information transmitted by the S to the UAVs. Moreover, free-space optical (FSO) and radio frequency (RF) technologies are respectively adopted over S-CH and CH-UAV links to exploit the high directivity of FSO over long-distance transmission and the omnidirectional coverage ability of RF. The positions of the CHs in the three-dimensional space follow the Matérn hard-core point processes type-II, in which each CH cannot be closer to another by less than a predefined distance. Three different cases over CH-UAV links are considered during the performance modeling: interference-free, interference-dominated, and interference-and-noise. Then, the coverage performance of the S-CH link and the CH-UAV link in the aforementioned three cases is studied and the closed-form analytical expressions of the coverage probability (CP) over both two links are derived. Additionally, the asymptotic expressions for the CP over S-CH link and CH-UAV link in the interference-free case are derived. Finally, numerical results are provided to validate our proposed analytical models and thus some meaningful conclusions are achieved.

The upcoming Internet of Things (IoT) is foreseen to encompass massive numbers of connected devices, smart objects, and cyber-physical systems. Due to the large scale and massive deployment of devices, it is deemed infeasible to safeguard 100% of the devices with state-of-the-art security countermeasures. Hence, large-scale IoT has inevitable loopholes for network intrusion and malware infiltration. Even worse, exploiting the high density of devices and direct wireless connectivity, malware infection can stealthily propagate through susceptible (i.e., unsecured) devices and form an epidemic outbreak without being noticed to security administration. A malware outbreak enables adversaries to compromise a large population of devices, which can be exploited to launch versatile cyber and physical malicious attacks. In this context, we utilize spatial firewalls, to safeguard the IoT from malware outbreak. In particular, spatial firewalls are computationally capable devices equipped with state-of-the-art security and anti-malware programs that are spatially deployed across the network to filter the wireless traffic in order to detect and thwart malware propagation. Using tools from percolation theory, we prove that there exists a critical density of spatial firewalls beyond which malware outbreak is impossible. This, in turn, safeguards the IoT from malware epidemics regardless of the infection/treatment rates. To this end, a tractable upper bound for the critical density of spatial firewalls is obtained. Furthermore, we characterize the relative communications ranges of the spatial firewalls and IoT devices to ensure secure network connectivity. The percentage of devices secured by the firewalls is also characterized.

One of the promising technologies for the next generation wireless networks is the reconfigurable intelligent surfaces (RISs). This technology provides planar surfaces the capability to manipulate the reflected waves of impinging signals, which leads to a more controllable wireless environment. One potential use case of such technology is providing indirect line-of-sight (LoS) links between mobile users and base stations (BSs) which do not have direct LoS channels. Objects that act as blockages for the communication links, such as buildings or trees, can be equipped with RISs to enhance the coverage probability of the cellular network through providing extra indirect LoS-links. In this article, we use tools from stochastic geometry to study the effect of large-scale deployment of RISs on the performance of cellular networks. In particular, we model the blockages using the line Boolean model. For this setup, we study how equipping a subset of the blockages with RISs will enhance the performance of the cellular network. We first derive the ratio of the blind-spots to the total area. Next, we derive the probability that a typical mobile user associates with a BS using an RIS. Finally, we derive the probability distribution of the path-loss between the typical user and its associated BS. We draw multiple useful system-level insights from the proposed analysis. For instance, we show that deployment of RISs highly improves the coverage regions of the BSs. Furthermore, we show that to ensure that the ratio of blind-spots to the total area is below 10-5, the required density of RISs increases from just 6 RISs/km2when the density of the blockages is 300 blockage/km2to 490 RISs/km2when the density of the blockages is 700 blockage/km2.

Unmanned aerial vehicles (UAVs) have recently received a significant interest to assist terrestrial wireless networks thanks to their strong line-of-sight links and flexible/instant deployment. However, UAVs' assistance is limited by their battery lifetime and wireless backhaul link capacity. At the expense of limited mobility, tethered UAVs (T-UAVs) can be a viable alternative to provide seamless service over a cable that simultaneously supplies power and data from a ground station (GS). Accordingly, this paper presents a comparative performance analysis of T-UAV and regular/untethered UAV (U-UAV)-assisted cellular traffic offloading from a geographical area that undergoes heavy traffic conditions. By using stochastic geometry tools, we first derive joint distance distributions between the hot-spot users, the terrestrial base station (TBS), and the UAV. To maximize the end-to-end signal-to-noise ratio, a user association policy is developed, and corresponding association regions are analytically identified. Then, the overall coverage probability of the U-UAV/T-UAV-assisted system is derived for given locations of the TBS and the U-UAV/T-UAV. Moreover, we analytically prove that optimal UAV location falls within a partial surface of the spherical cone centered at the GS. Numerical results show that T-UAV outperforms U-UAV given that sufficient GS locations accessibility and tether length are provided.

One of the main challenges slowing the deployment of airborne base stations (BSs) using unmanned aerial vehicles (UAVs) is the limited on-board energy and flight time. One potential solution to such problem, is to provide the UAV with power supply through a tether that connects the UAV to the ground. In this paper, we study the optimal placement of tethered UAVs (TUAVs) to minimize the average path-loss between the TUAV and a receiver located on the ground. Given that the tether has a maximum length, and the launching point of the TUAV (the starting point of the tether) is placed on a rooftop, the TUAV is only allowed to hover within a specific hovering region. Beside the maximum tether length, this hovering region also depends on the heights of the buildings surrounding the rooftop, which requires the inclination angle of the tether not to be below a given minimum value, in order to avoid tangling and ensure safety. We first formulate the optimization problem for such setup and provide some useful insights on its solution. Next, we derive upper and lower bounds for the optimal values of the tether length and inclination angle. We also propose a suboptimal closed-form solution for the tether length and its inclination angle that is based on maximizing the line-of-sight probability. Finally, we derive the probability distribution of the minimum inclination angle of the tether length. We show that its mean value varies depending on the environment from 10° in suburban environments to 31° in high rise urban environments. Our numerical results show that the derived upper and lower bounds on the optimal values of the tether length and inclination angle lead to tight suboptimal values of the average path-loss that are only 0 - 3 dBs above the minimum value.

Owing to the ubiquitous availability of radio- frequency (RF) signals, RF energy harvesting is a promising candidate for powering IoT devices, some of which may be deployed at difficult-to-reach places thus making it inconvenient or even impossible to replace or recharge their batteries. In this paper, we model and analyze an IoT network which harvests RF energy and receives information from the same wireless network. In order to enable this operation, each time slot is partitioned into charging and information reception phases. For this setup, we characterize two performance metrics: (i) energy coverage, and (ii) joint signal-to-interference-plus-noise (SINR) and energy coverage. This analysis is performed using a spatial model that captures coupling between the locations of the IoT devices and the nodes of the wireless network (referred henceforth as the IoT gateways), which is usually ignored in the existing literature. In particular, we model the locations of the IoT devices using a general Poisson cluster process (PCP) and assume that the IoT gateways (GWs) are located at the cluster centers. Our results concretely demonstrate that both energy and joint coverage probabilities decrease as the size of the clusters increases. As expected, the performance converges to the case of modeling the locations of the IoT devices and the GWs as two independent PPPs when the cluster sizes go to infinity.

This paper studies the secrecy performance of a wireless network (primary network) overlaid with an ambient RF energy harvesting Internet of Things (IoT) network (secondary network). The nodes in the secondary network are assumed to be solely powered by ambient RF energy harvested from the transmissions of the primary network. We assume that the secondary nodes can eavesdrop on the primary transmissions due to which the primary network uses secrecy guard zones. The primary transmitter goes silent if any secondary receiver is detected within its guard zone. Using tools from stochastic geometry, we derive the probability of successful connection of the primary network as well as the probability of secure communication. Two conditions must be jointly satisfied in order to ensure successful connection: 1) the signal-to-interference-plus-noise ratio (SINR) at the primary receiver is above a predefined threshold, and 2) the primary transmitter is not silent. In order to ensure secure communication, the SINR value at each of the secondary nodes should be less than a predefined threshold. Clearly, when more secondary nodes are deployed, more primary transmitters will remain silent for a given guard zone radius, which will in turn impact the amount of energy harvested by the secondary network. Our results concretely show the existence of an optimal deployment density for the secondary network that maximizes the density of nodes that are able to harvest sufficient amount of energy. Furthermore, we show the dependence of this optimal deployment density on the guard zone radius of the primary network. In addition, we show that the optimal guard zone radius selected by the primary network is a function of the deployment density of the secondary network. This interesting coupling between the performance of the two networks is studied using tools from game theory. We propose an algorithm that can assist the two networks to converge to Nash equilibrium. The convergence of this algorithm is verified using simulations. Overall, this paper is one of the few concrete works that symbiotically merge tools from stochastic geometry and game theory.

This paper studies the secrecy performance in wireless networks (primary network) overlaid with an ambient RF energy harvesting network (secondary network). The nodes in the secondary network are assumed to be solely powered by ambient RF energy harvested from transmissions of the primary network. We assume that the secondary nodes can eavesdrop on the primary transmissions due to which the primary network uses secrecy guard zones. The primary transmitter goes silent if any secondary receiver is detected within its guard zone. Using tools from stochastic geometry, we first derive the optimal guard zone radius that minimizes the probability of going silent while ensuring a predefined minimum secure connection probability. Clearly, when more secondary nodes are deployed, more primary transmitters will remain silent for a given guard zone radius, thus impacting the amount of energy harvested by the secondary network. This introduces an interesting coupling between the performance of the two networks. We study this coupling using tools from game theory and propose an algorithm that can assist the two networks to converge to Nash equilibrium. Our results demonstrate the convergence of the proposed algorithm to the Nash equilibrium in finite number of iterations. Overall, this work forms one of the few concrete works that symbiotically merge tools from stochastic geometry and game theory.

This paper studies the downlink performance of a cellular-based internet-of-things (IoT) network where the receiving devices are solely powered by energy harvested from the ambient radio frequency (RF). Assuming that the cellular network is the only source of RF energy, we consider a time-division based approach for power and information transfer where each time slot is partitioned into two sub-lots: (i) charging sub-slot during which the base stations (BSs) act as chargers for the devices, and (ii) information sub-slot in which the devices receive information using energy harvested during the charging sub-slot. Modeling the BS locations as a Poisson Point Process (PPP), we study a new composite outage probability metric that considers the joint effect of outages due to insufficient energy harvested during the charging sub-slot and low signal quality in the information sub-slot. Using this metric, we concretely demonstrate the existence of an optimum downlink time slot division between charging and information transmission that maximizes the average downlink throughput for a given user.

Cognitive radios have attracted much attention from researchers lately due to their ability to increase spectrum utilization efficiency. Recently the idea of employing cognitive radios in wireless sensor networks was proposed resulting in what so called cognitive radio sensor networks (CRSNs). Many of the algorithms that used to be deployed successfully in WSNs may not work properly in the cognitive settings of CRSNs, many modifications should be introduced to these algorithms to make them eligible to work in CRSNs and show performance with comparable quality to that in the case of WSNs. One of these algorithms is data storage algorithms. In this paper we propose a framework and introduce the required modifications for these data storage algorithms to work properly in CRSNs, we apply our proposed modifications to 3 different data storage algorithms and show with simulation results how these modifications make the performance in CRSNs approaches the performance in normal WSNs.