Unmanned aerial vehicles (UAVs) have attracted great interest in rapid deployment for both civil and military applications. UAV communication has its own distinctive channel characteristics compared to the widely used cellular or satellite systems. Accurate channel characterization is crucial for the performance optimization and design of efficient UAV communication. However, several challenges exist in UAV channel modeling. For example, the propagation characteristics of UAV channels are under explored for spatial and temporal variations in non stationary channels. Additionally, airframe shadowing has not yet been investigated for small size rotary UAVs. This paper provides an extensive survey of the measurement methods proposed for UAV channel modeling that use low altitude platforms and discusses various channel characterization efforts. We also review from a contemporary perspective of UAV channel modeling approaches, and outline future research challenges in this domain.

The use of unmanned aerial vehicle (UAV) as aerial base stations can provide wireless communication services in the form of UAV-based small cells (USCs). Thus, the major design challenge that needs to be addressed is the coverage maximization of such USCs in the presence of co-channel interference generated by multiple UAVs operating within a specific target area. Consequently, the efficient deployment strategy is imperative for USCs while optimizing the coverage area performance to compensate the impact of interference. To this end, this paper presents a coordinated multi-UAV strategy in two scenarios. In the first scenario, symmetric placement of UAVs is assumed at a common optimal altitude and transmit power. In the second scenario, asymmetric deployment of UAVs with different altitudes and transmit powers is assumed. Then, the coverage area performance is investigated as a function of separation distance between UAVs which are deployed in a certain geographical area to satisfy a target signal-to-interference-plus-noise ratio (SINR) at the cell boundary. Finally, the system-level performance of a boundary user is studied in terms of the coverage probability. Numerical results unveils that the SINR threshold, the separation distance, and the number of UAVs and their formations should be carefully selected to achieve the maximum coverage area inside and to reduce the unnecessary expansion outside the target area. Thus, this paper provides important design guidelines for the deployment of multiple UAVs in presence of co-channel interference.

Future wireless networks will be required to provide more wireless services at higher data rates and with global coverage. However, existing homogeneous wireless networks, such as cellular and satellite networks, may not be able to meet such requirements individually, especially in remote terrain, including seas and mountains. One possible solution is to use diversified wireless networks that can exploit the inter-connectivity between satellites, aerial base stations (BSs), and terrestrial BSs over inter-connected space, ground, and aerial networks. Hence, enabling wireless communication in one integrated network has attracted both the industry and the research fraternities. In this work, we provide a comprehensive survey of the most recent work on hybrid satellite–terrestrial networks (HSTNs), focusing on system architecture, performance analysis, design optimization, and secure communication schemes for different cooperative and cognitive HSTN network architectures. Different key technologies are compared. Based on this comparison, several open issues for future research are discussed.

Many wireless networks operate in a mobile environment with randomly moving user terminals. This letter analytically characterizes the impact of ground user mobility, propagation environment and channel fading on the outage performance of unmanned aerial vehicle (UAV) communications. Closed-form expressions for the outage probability using the random waypoint model for ground user mobility, UAV channel models for different propagation environments and the Nakagami-m model for fading channels are derived. Furthermore, the outage analysis takes into account the effect of co-channel interference by both the stationary and mobile users. Numerical results are presented to demonstrate the interplay between the communication performance and the system parameters.

Caching content in small-cell networks can reduce the traffic congestion in backhaul. In this article, we develop a hybrid caching network comprising of unmanned aerial vehicles (UAVs) and ground small-cell base stations (SBSs), where UAVs are preferred because of their flexibility and elevated platform for line of sight. First, we derive the association probability for the ground user affiliated with a UAV and ground SBS. Then, we derive the successful content delivery probability by considering both the intercell and intracell interference. We also analyze the energy efficiency of the hybrid network and compare it with the separate UAV and ground networks. We further propose the caching scheme to improve the successful content delivery by managing the content popularity, where the part of the caching capacity in each UAV and ground SBS is reserved to store the most popular content (MPC), while the remaining stores less popular contents. Numerical results unveil that the proposed caching scheme has an improvement of 26.6% in content delivery performance over the MPC caching, which overlooks the impact of content diversity during caching.

Integrated sensing and communication enhances the spectral efficiency by using shared resources, eliminating the need for separate bandwidth allocations. Unmanned aerial vehicle (UAVs) play a key role in this, offering mobility, flexibility, and extended coverage for serving multiple users, especially in scenarios like disaster response and environmental monitoring. This paper explores multiple UAVs with integrated sensing and communication capabilities, using the Age of Information (AoI) metric to optimize resource allocation for timely data transmission. We propose two algorithms, Variable Particle Swarm Optimization (VPSO) and Twin Variable Neighborhood Particle Swarm Optimization (TVPSO), to jointly optimize power, bandwidth, and UAV trajectories to minimize AoI. Numerical results show the effects of the sensing and communication power ratio, the number of UAVs and the number of users on AoI and energy consumption. Furthermore, TVPSO is shown to outperform other PSO variants and the Deep Q Networks (DQN)-based approach, offering faster convergence and superior performance.