## 2 Related Work  
UAVs are widely utilized as low-altitude platforms owing to their effortless flexibility, inexpensive price, and simplicity of deployment [9]. Current academic research and industrial associations propose UAVs as aerial base stations and evaluate their application characteristics in various systems. For example, the authors of [10] consider UAVs to provide downlink services for network coverage in rural areas. The authors of [11] construct a UAV network that collects data from IoT devices and starts transmission whenever the IoT devices are within the coverage area of the UAV. In [12; 13], radio power transmission from UAVs to end devices was proposed to improve battery life or to enable local computing. In [14], a deep reinforcement learning-based algorithm was proposed to solve the UAV trajectory planning, mission scheduling, and deployment in complex regional scenarios. To facilitate the modeling and analysis of wireless geometric networks, PPP is widely used because of its ease of handling. In most of the research on stochastic FlyMesh, PPP is usually used to model the spatial distribution of UAVs. In [15], a 3D wireless network of UAVs was modeled as a multilayer network with the location of UAVs at each layer following the PPP model, which was derived analytically in addition to the 3D UAVs switching probability. Reference [16] derived the coverage performance of the UAV-assisted millimeter-wave cellular network using the PPP model and analyzed the optimal key parameters to maximize the coverage probability. Although the PPP model provides many useful theoretical results for our UAV network analysis, it has certain drawbacks due to the independence of the described UAVs' location distribution. Therefore, a better point process must be considered to capture the deployment of UAVs in real-world environments. \(\beta\)-GPP had received much attention as describing a point process with repulsive power, where \(0<\beta<1\), describing the strength of repulsion between points. In contrast, GPP belongs to a class of deterministic point processes (DPP) [17]. \(\beta\)-GPP falls between GPP and PPP, and it is more regular than PPP and treats PPP as a particular case. Therefore, \(\beta\)-GPP was a soft-core model with a scaling function according to the variation of the repulsive parameter \(\beta\). In [18], an ambient radio frequency energy harvesting wireless sensor network was considered, where the location of the ambient radio frequency source was modeled as a \(\beta\)-GPP and the average value of energy, outage probability, and transmission interruption probability were harvested by the Laplace transform of \(\beta\)-GPP. In [19], the performance of a specific clustered network with a wireless node distribution location modeled as \(\beta\)-GPP was investigated and its approximate expressions were obtained by approximating the simplified palm distribution of \(\beta\)-GPP as an inhomogeneous Poisson point process (IPPP). On this basis, the approximate expressions for the message outage probability, the overall outage probability, and the transmission capacity were obtained by further neglecting the small-scale fading of the interfering links. The \(\beta\)-GPP was applied and generalized in [20], where the mean and variance of the interference were derived using the Palm measurement and the simplified second moment method, and the correlation of the model with the cellular system was further verified. [PERSON] et al. [21; 22] analyzed the system performance of single-hop and multi-hop relaying under \(\beta\)-GPP interference fields and obtained the outage probability and ergodic capacity. The above study analyzed the network performance of mobile nodes in wireless self-organizing networks based on GPP distribution, but did not consider the self interference factors of wireless networks. The antenna is a major factor affecting self-interference in self-organized networks.