In unmanned aerial vehicle (UAV) corridor-assisted networks supported by joint communication and sensing (JCAS) is regarded as a key enabler for supporting reliable, efficient, and secure systems for mission critical sixth-generation (6 G) services. As an emerging antenna technology, a fluid antenna (FA) system enhances spatial diversity to improve both sensing and communication performance by shifting the active antennas among available ports. In this paper, a UAV corridor-assisted network is proposed to study the JCAS coverage performance of an arbitrary located FA-enabled terrestrial base station (BS). The BS is assumed to enable radar sensing capabilities to track a UAV-user equipment (UE)'s movement trajectory while the tracked UAV-UE is assumed to communicate with the terrestrial BS. By modeling the spatial locations of the UAV-UEs and the BS as a one-dimensional (1D) and two-dimensional (2D) binomial point process (BPP), respectively, an exact-form expression for the JCAS coverage probability is derived under communication and clutter interference. To this end, the Student's $t$ copula is exploited for the first time in JCAS performance to model the spatial correlation between the FA's ports. The results reveal insightful design guidelines for the JCAS performance of UAV corridor-assisted networks.

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

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

This paper introduces the multivariate inverse-gamma distribution arising from Gaussian random processes. Novel single-integral representations for the multivariate probability density function (PDF), cumulative distribution function (CDF), and the joint moments are obtained, under the assumption of non-identically distributed random variables. Although the presented model is a general shadowing model, two wireless communications scenarios have been selected to present its applicability. Both scenarios are related to unmanned-aerial-vehicle (UAV)-assisted communications. In the first one, the multivariate IG distribution is employed as a tractable correlated shadowing model in a communication system with multiple UAVs that are equipped with reconfigurable intelligent surfaces (RIS). The new distribution is used to model the shadowing correlation among the signals transmitted by different UAVs. In the second scenario, the IG distribution is used to model the correlation between the sensing and communication signals in a sensing-assisted UAV communication system. The outage and the coverage probability are used as performance evaluation metrics. It is shown that in RIS-UAV assisted communications, the impact of shadowing cannot be neglected, while, under certain conditions, shadowing correlation may affect the performance by more than 50% as compared to uncorrelated cases. Moreover, it is also shown that the large shadowing decorrelation distance can be exploited in sensing-assisted communications applied to aerial-to-ground communication scenarios.

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

The inverse-gamma (IG) distribution has recently emerged as a mathematically tractable and empirically supported model for large-scale shadowing in wireless communication systems. Although several IG-based fading and shadowing models have been investigated in terms of first-order statistics, their second-order temporal behavior has received limited attention. This paper develops an analytical framework for the level crossing rate (LCR) and average fade duration (AFD) of single- and double-IG shadowing processes. The joint probability density function (PDF) of the IG process and its time derivative is derived, leading to closed-form expressions for its LCR and AFD. The analysis is then extended to an independent double-IG product process, for which the joint PDF of the process and its time derivative is formulated, yielding an exact single-integral LCR expression and the corresponding AFD. The framework is further applied to a UAV-to-ground double-shadowing link, where the UAV-side and ground-station-side IG shadowing components may be statistically dependent. Under a conditional Gaussian derivative model, a closed-form PDF and expressions for the moments, together with exact integral expressions for the CDF, LCR, and AFD, are obtained for the received signal-to-noise ratio (SNR). Time-domain Monte Carlo simulations validate the single-IG second-order results, whereas conditional Monte Carlo evaluations of Rice’s formula confirm the numerical consistency of the correlated double-IG expressions under the adopted derivative model. The results quantify the effects of the IG parameters, shadowing dependence, and temporal variation rates on the frequency and duration of shadowing-induced outage events.

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

Investigating the statistics of the sum of random variables (RVs) is fundamental in wireless communication systems, since it can be used to estimate the outage probability and error rates. In this paper, exact closed-form expressions are derived for the probability density function (PDF) and cumulative distribution function (CDF) of the sum of inverse-gamma (IG) RVs. The IG distribution is especially suitable for performance analysis purposes because it provides a tractable yet accurate model of shadowing/local mean power variations. Unlike prior studies on sums of shadowing RVs, the resulting formulas are simple to evaluate and numerically stable. Their utility is demonstrated in two practical settings: unmanned aerial vehicle (UAVs)-assisted networks and high-speed railway communications. Across all scenarios, the analytical results agree with Monte Carlo simulations to within machine precision, while the asymptotic approximations closely track the exact results.

In this paper, rate splitting (RS) has been applied in a mixed dual-hop radio frequency/free space optical (RF/FSO) relay system. In particular, assuming fixed-gain amplify-and-forward (AF) and decode-and-forward (DF) relay protocols, the performance of both heterodyne detection and intensity-modulated direct detection (IM-DD) has been analytically evaluated. In this framework, it is assumed that the RF channel suffers from Rayleigh fading, while the FSO channel is affected by the atmospheric turbulence-induced fading with M & aacute;laga ( $\mathcal {M}$ ) distribution and pointing errors. For the considered system, we derive expressions for the outage probability, the average bit error rate (BER), and the average channel capacity. In addition, the asymptotic performance of the system at high signal-to-noise ratios regime is analyzed, special cases are discussed, and useful insights have been extracted. Finally, numerical results are presented to verify the accuracy of the analytical and asymptotic expressions.

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

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

This letter studies the joint energy and signal-to-interference-plus-noise (SINR)-based coverage probability in Unmanned Aerial Vehicle (UAV)-assisted radio frequency (RF)-powered Internet of Things (IoT) networks. The UAVs are spatially distributed in an aerial corridor that is modeled as a one-dimensional (1D) binomial point process (BPP). By accurately capturing the line-of-sight (LoS) probability of a UAV through large-scale fading: i) an exact form expression for the energy coverage probability is derived and ii) a tight approximation for the overall coverage performance is obtained. Among several key findings, numerical results reveal the optimal number of deployed UAV-BSs that maximizes the joint coverage probability, as well as the optimal length of the UAV corridors when designing such UAV-assisted IoT networks.

The application of joint sensing and communications (JSACs) technology in air–ground networks, which include unmanned aerial vehicles (UAVs), offers unique opportunities for improving both sensing and communication performances. However, this type of network is also sensitive to the peculiar characteristics of the aerial communications environment, which include shadowing and scattering caused by man-made structures. This paper investigates an aerial JSAC network and proposes a UAV-selection strategy that is shown to improve the communication performance. We first derive analytical expressions for the received signal-to-interference ratio for both communication and sensing functions. These expressions are then used to analyze the outage and coverage probability of the communication part, as well as the ergodic radar estimation information rate and the detection probability of the sensing part. Moreover, a performance trade-off is investigated under the assumption of a total bandwidth constraint. Various numerical evaluated results have been presented complemented by equivalent simulated ones. These results reveal the applicability of the proposed analysis, as well as the impact of shadowing and multipath fading severity, and interference on the system’s performance.

Maritime transportation is crucial for global trade and responsible for the majority of goods movement worldwide. The optimization of maritime operations is challenged by the complexity and heterogeneity of maritime nodes. This paper presents the emerging deployment of federated learning (FL) in maritime environments to address these challenges. FL enables decentralized machine learning model training, ensuring data privacy and security while overcoming issues associated with non-i.i.d. data. This paper explores various maritime use cases, including fuel consumption reduction, predictive maintenance, and just-in-time arrival. Experimental results using real datasets demonstrate the superiority of FL in predicting the fuel consumption of large cargo ships in terms of accuracy and spatiotemporal complexity over traditional collaborative machine learning approaches. The findings indicate that FL can significantly improve the performance of fuel consumption models in a collaborative way, while ensuring data privacy preservation and no data transmission during the learning process. Finally, this paper discusses open issues and future research directions necessary for the widespread adoption of FL in maritime transportation and settings.

The application of joint sensing and communications (JSACs) technology in air-ground networks, which include unmanned aerial vehicles (UAVs), offers unique opportunities for improving both sensing and communication performances. However, this type of networks are also sensitive to the peculiar characteristics of the aerial communications environment, which include shadowing and scattering caused by man-made structures. This paper investigates an aerial JSAC network and proposes a UAV-selection strategy that is shown to improve the communication performance. We first derive analytical expressions for the received signal-to-interference ratio for both communication and sensing functions. These expressions are then used to analyze the outage and coverage probability of the communication part, as well as the ergodic radar estimation information rate and the detection probability of the sensing part. Moreover, a performance trade-off is investigated under the assumption of a total bandwidth constraint. The presented results reveal the impact of shadowing severity and interference on the system’s performance.

We study and analyze the performance of a wireless communication system that is based on the use of airborne Reconfigurable Intelligent Surfaces, i.e., surfaces mounted on an Unmanned Aerial Vehicle (UAV). To this end, we perform a stochastic analysis that allows us to study a very wide variety of realistic channel conditions, including environments characterized by combined small-scale and large-scale fading and potential spatial correlation in shadowing. For this generic channel model, we derive closed-form expressions for the outage probability and the ergodic capacity, and also investigate the analytical calculation of the average energy efficiency. The presented results allow us, for the first time, to study the impact of different shadowing conditions, including correlation effects, in aerial communications systems that are also supported by reconfigurable intelligent surfaces. Finally, the performance of the considered system is also compared with that of decode-and-forward relaying, highlighting the advantages of combining reconfigurable intelligent surfaces with UAV-assisted communication technologies in composite fading environments.

In this paper, we study the performance of a cooperative underwater acoustic communication/free-space optical communication (UAC-FSO) transmission system supported by a fixed-gain amplify-and-forward (AF) relay, where an underwater source transmits signals to a satellite through the help of a buoy functioning as a relay located on the sea surface. In particular, K and κ - μ shadowed fading distribution models are used to characterize the UAC link, while the FSO channel follows a unified Málaga distribution existing pointing errors. Based on the above considerations, we calculate the cumulative distribution function (CDF) and probability density function (PDF) of the end-to-end (e2e) signal-to-noise ratio (SNR). To assess the system performance, expressions for the outage probability (OP) and average bit-error rate (BER) are further obtained in closed-form. Furthermore, driven by a desire for more explicit insights, the high SNR analyses of the OP and average BER, and upper and lower bounds on the average capacity are presented. Finally, through Monte Carlo simulations, the correctness of the theoretical analysis is verified.

We investigate Wireless Powered Multi-Relay Networks (WPRNs) equipped with multiple antennas both at the Source and the Relay and propose two different communication schemes. These schemes are based on the combination of Time Switching (TS) and Self-Energy Recycling (SER) and extend existing ones that have been developed for single-antenna sources. Following that, by adopting only the very generic assumption that the Energy Harvesting Model (EHM) is described by any non-decreasing function, we focus on the instantaneous rate maximization problem and design near-optimal beamforming and wireless power transfer-time determination algorithms for our schemes. A common characteristic of the presented algorithms is their low complexity and implementation simplicity. Given the generality of our EHM assumptions, our algorithms are applicable for all popular EHMs found in the literature, which are normally described using non-decreasing functions, without being specific to any of them. Various simulation results are presented that allow to evaluate the two schemes and compare them with existing benchmarks for different popular EHMs and relay availability scenarios. Finally, we bound the suboptimality of our solutions and verify their near-optimal performance for different EHMs.

In this work, a mixed underwater acoustic/optical wireless transmission system under both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols is proposed. Assuming pointing errors and both heterodyne detection (HD) as well as intensity modulation/direct detection (IM/DD) techniques in the underwater optical link, we deduce the exact analytical formulas for the outage probability (OP), average bit error rate (ABER), and average capacity of the system under consideration. Also, we further derive the corresponding asymptotic expressions to gain intuitive physical insights about the system and channel models under consideration. Additionally, we extend the analysis to a more general multi-sensor system. Finally, we check the analytical results by Monte Carlo simulations.

The exploitation of unmanned aerial vehicles (UAVs) in enhancing network performance in the context of beyond-fifth-generation (5G) communications has shown a variety of benefits compared to terrestrial counterparts. In addition, they have been largely conceived to play a central role in data dissemination to Internet of Things (IoT) devices. In the proposed work, a novel stochastic geometry unified framework is proposed to study the downlink performance in a UAV-assisted IoT network that integrates both UAV-base stations (UAV-BSs) and terrestrial IoT receiving devices. The framework builds upon the concept of the aerial UAV corridor, which is modeled as a finite line above the IoT network, and the one-dimensional (1D) binomial point process (BPP) is employed for modeling the spatial locations of the UAV-BSs in the aerial corridor. Subsequently, a comprehensive SNR-based performance analysis in terms of coverage probability, average rate, and energy efficiency is conducted under three association strategies, namely, the nth nearest-selection scheme, the random selection scheme, and the joint transmission coordinated multi-point (JT-CoMP) scheme. The numerical results reveal valuable system-level insights and trade-offs and provide a firm foundation for the design of UAV-assisted IoT networks.