This work compares the application of Reinforcement Learning (RL) and Swarm Intelligence (SI) based methods for resolving the problem of coordinating multiple High Altitude Platform Stations (HAPS) for communications area coverage. Swarm coordination techniques are essential for developing autonomous capabilities for multiple HAPS/UAS control and management. This paper examines the performance of artificial intelligence (AI) capabilities of RL and SI for autonomous swarm coordination. In this work, it was observed that the RL approach showed superior overall peak user coverage with unpredictable coverage dips; while the SI based approach demonstrated lower coverage peaks but better coverage stability and faster convergence rates.

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The impact and requirements for implementing stratospheric or high altitude vehicles for communications coverage may vary from one geographical location to another. These variations may impose significant constraints on energy and various key parameters of the vehicle’s operation and performance.This paper therefore, examines the potential for autonomous fixed-wing unmanned solar-powered High Altitude Platform Station or Pseudo-Satellite (HAPS) to provide persistent communications coverage. As a solar dependent platform, the potential for harnessing green energy and long platform endurance makes it an attractive communications coverage option. However, the variation of latitude and seasons across the globe presents an implementation constraint and challenges power availability and coverage capability. This paper investigates how the services of a typical solar-powered HAPS are affected by latitude and season. It shows that the degree of insolation directly affects the unmanned aircraft’s altitude, hence, its footprint diameter and power available to the communications payload. The paper highlights effective energy management algorithms as key to successful implementation of solar-powered unmanned HAPS especially at challenging latitudes and seasons

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