When to Reach for the Skies? A DRL-Based Routing Framework for Non-Terrestrial Networks

Abstract

Non-terrestrial networks are envisioned to be an integral component of the beyond-fifth-generation wireless communication networks, catering to both conventional and emerging communication applications. In particular, a plethora of use cases are emerging for ultra-reliable low-latency communication, which require dynamic and quality of service compliant frameworks. In this letter, we formulate a binary integer non-linear programming problem to route time-critical traffic through non-terrestrial nodes. As the problem is NP-hard, we propose the solution using a deep reinforcement learning framework, taking into account the interactions between the terrestrial and various non-terrestrial nodes with an end-to-end latency target while maximizing the coverage probability. We perform simulations for multiple latency deadlines and outage thresholds and the results corroborate the efficiency of the proposed framework. Furthermore, we benchmark the proposed framework and show an improvement of 96.31% in coverage while incurring only 3.2% latency violations compared to the state-of-the-art.