Integrating Atmospheric Sensing and Communications for Resource Allocation in NTNs

TL;DR

The paper tackles reliable global connectivity in non-terrestrial networks by addressing rainfall-induced attenuation in high-frequency satellite links through an integrated sensing and communications (ISAC) framework. It introduces a sensing-assisted frame structure and a joint satellite-to-cell matching with resource allocation, solved via a convex-relaxation and augmented-Lagrangian approach, to achieve proportional fairness under realistic delays and handover costs. The framework demonstrates up to $59%$ average throughput improvements and over $700%$ fairness gains compared with separated designs, while remaining real-time feasible for frame lengths in the $10$–$30$ s range. This work enables continent-scale NTN deployments with improved resilience and efficiency, and suggests avenues for distributed optimization and atmospheric digital-twin extensions.

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.

Figures (11)

• Figure 1: Toy example of the scenario at hand and the algorithmic depiction of the integration of sensing and communication.
• Figure 2: Map of central Europe illustrating (a) the active users per-cell as in Sec. \ref{['sec:sat-cells']} (derived from the per-cell population CIESIN) and (b) the rain rate with the clustered rain model of Sec. \ref{['sec:rain']}.
• Figure 3: The rain rate at each user cell is the sum of rates of the active rain cells within range. The on-off process for the rain cells is a two-state .
• Figure 4: Visualization of a generic $k$-th frame for a $(s,c)$ pair, comprising communication, sensing sub-frame, and process, with $T_F = 10 T$, $N_S = 3$ and $N_\mathrm{HO} = 2$. The handover on frame $k+1$-th is missing to show a situation where the $(s,c)$ pair does not change from $k$ to $k+1$.
• Figure 5: Exemplary time diagram for the sensing sub-frame with $C_s=3$ anchor nodes and $N_B=2$.

Theorems & Definitions (3)

• Definition 1
• Proposition 1
• proof