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As next-generation communication services and satellite systems expand across diverse frequency bands, the escalating utilization poses heightened interference risks to passive sensors crucial for environmental and atmospheric sensing. Consequently, there is a pressing need for efficient methodologies to detect, characterize, and mitigate the harmful impact of unwanted anthropogenic signals known as radio frequency interference (RFI) at microwave radiometers. One effective strategy to reduce such interference is to facilitate the coexistence of active and passive sensing systems. Such approach would greatly benefit from a testbed along with a dataset encompassing a diverse array of scenarios under controlled environment. This study presents a physical environmentally controlled testbed including a passive fully calibrated L-band radiometer with a digital back-end capable of collecting raw in-phase/quadrature (IQ) samples and an active fifth-generation (5G) wireless communication system with the capability of transmitting waveforms with advanced modulations. Various RFI scenarios such as in-band, transition-band, and out-of-band transmission effects are quantified in terms of calibrated brightness temperature. Raw radiometer and 5G communication samples along with preprocessed time-frequency representations and true brightness temperature data are organized and made publicly available. A detailed procedure and publicly accessible dataset are provided to help test the impact of wireless communication on passive sensing, enabling the scientific community to facilitate coexistence research and quantify interference effects on radiometers. 

The need for continuous coverage, as well as low-latency, and ultrareliable communication in 5G and beyond cellular networks encouraged the deployment of high-altitude platforms and low-altitude drones as flying base stations (FBSs) to provide last-mile communication where high cost or geographical restrictions hinder the installation of terrestrial base stations (BSs) or during the disasters where the BSs are damaged. The performance of unmanned aerial vehicle (UAV)-assisted cellular systems in terms of coverage and quality of service offered for terrestrial users depends on the number of deployed FBSs, their 3-D location as well as trajectory. While several recent works have studied the 3-D positioning in UAV-assisted 5G networks, the problem of jointly addressing coverage and user data rate has not been addressed yet. In this article, we propose a solution for joint 3-D positioning and trajectory planning of FBSs with the objectives of the total distance between users and FBSs and minimizing the sum of FBSs flight distance by developing a fuzzy candidate points selection method. 

In this paper, we delve into the domain of heterogeneous drone-enabled aerial base stations, each equipped with varying transmit powers, serving as downlink wireless providers for ground users. A central challenge lies in strategically selecting and deploying a subset from the available drone base stations (DBSs) to meet the downlink data rate requirements while minimizing the overall power consumption. To tackle this, we formulate an optimization problem to identify the optimal subset of DBSs, ensuring wireless coverage with an acceptable transmission rate in the downlink path. Moreover, we determine their 3D positions for power consumption optimization. Assuming DBSs operate within the same frequency band, we introduce an innovative, computationally efficient beamforming method to mitigate intercell interference in the downlink. We propose a Kalai–Smorodinsky bargaining solution to establish the optimal beamforming strategy, compensating for interference-related impairments. Our simulation results underscore the efficacy of our solution and offer valuable insights into the performance intricacies of heterogeneous drone-based small-cell networks.