Joint ADS-B in B5G for Hierarchical AAV Networks: Performance Analysis and MEC-Based Optimization

Autonomous aerial vehicles (AAVs) play significant roles in multiple fields, which brings great challenges for the airspace safety. In order to achieve efficient surveillance and break the limitation of application scenarios caused by single communication, we propose the collaborative surveillance model for hierarchical AAVs based on the cooperation of automatic dependent surveillance-broadcast (ADS-B) and 5G. Specifically, AAVs are hierarchical deployed, with the low-altitude central AAV equipped with the 5G module, and the high-altitude central AAV with ADS-B, which helps automatically broadcast the flight information to surrounding aircraft and ground stations. First, we build the framework, derive the analytic expression, and analyze the channel performance of both air-to-ground (A2G) and air-to-air (A2A). Then, since the redundancy or information loss during transmission aggravates the monitoring performance, the mobile edge computing (MEC) based on-board processing algorithm is proposed. Finally, the performances of the proposed model and algorithm are verified through both simulations and experiments. In detail, the redundant data filtered out by the proposed algorithm accounts for 53.48%, and the supplementary data accounts for 16.42% of the optimized data. This work designs a AAV monitoring framework and proposes an algorithm to enhance the observability of trajectory surveillance, which helps improve the airspace safety and enhance the air traffic flow management.