Integrating Atmospheric Sensing and Communications for Resource Allocation in NTNs

TL;DR

This paper proposes a sensing-assisted communication framework for LEO satellite constellations that improves resource allocation and link reliability by estimating atmospheric conditions, leading to significant throughput and fairness gains.

Contribution

It introduces a novel integrated sensing and communications framework for resource allocation in LEO NTNs, enhancing link performance under atmospheric uncertainties.

Findings

Increased average throughput by 59%.

Enhanced fairness by 700%.

Feasibility of joint sensing and resource allocation.

Abstract

The integration of Non-Terrestrial Networks (NTNs) with Low Earth Orbit (LEO) satellite constellations into 5G and Beyond is essential to achieve truly global connectivity. A distinctive characteristic of LEO mega constellations is that they constitute a global infrastructure with predictable dynamics, which enables the pre-planned allocation of radio resources. However, the different bands that can be used for ground-to-satellite communication are affected differently by atmospheric conditions such as precipitation, which introduces uncertainty on the attenuation of the communication links at high frequencies. Based on this, we present a compelling case for applying integrated sensing and communications (ISAC) in heterogeneous and multi-layer LEO satellite constellations over wide areas. Specifically, we propose a sensing-assisted communications framework and frame structure that not only enables the accurate estimation of the atmospheric attenuation in the communication links through sensing but also leverages this information to determine the optimal serving satellites and allocate resources efficiently for downlink communication with users on the ground. The results show that, by dedicating an adequate amount of resources for sensing and solving the association and resource allocation problems jointly, it is feasible to increase the average throughput by 59% and the fairness by 700% when compared to solving these problems separately.