Joint Satellite Power Consumption and Handover Optimization for LEO Constellations

TL;DR

This paper tackles the challenge of maintaining continuous coverage in LEO satellite constellations by explicitly accounting for handover-related signaling and power costs. It introduces a slot-by-slot joint optimization of user association and downlink power, incorporating a handover penalty $\alpha$ and formulating the problem as a mixed-integer concave program solvable with standard solvers. Through detailed modeling of visibility, channel, and path loss (including Rician fading and atmospheric effects), the authors demonstrate that optimizing both association and power can significantly improve per-user throughput (about 40%) compared to a closest-visible baseline, albeit with more frequent handovers. The results highlight a practical trade-off between higher throughput and handover frequency, suggesting future work on horizon-based optimization or decomposition methods to balance performance and signaling overhead in large-scale NTN deployments.

Abstract

In satellite constellation-based communication systems, continuous user coverage requires frequent handoffs due to the dynamic topology induced by the Low Earth Orbit (LEO) satellites. Each handoff between a satellite and ground users introduces additional signaling and power consumption, which can become a significant burden as the size of the constellation continues to increase. This work focuses on the optimization of the total transmission rate in a LEO-to-user system, by jointly considering the total transmitted power, user-satellite associations, and power consumption, the latter being handled through a penalty on handoff events. We consider a system where LEO satellites serve users located in remote areas with no terrestrial connectivity, and formulate the power allocation problem as a mixed-integer concave linear program (MICP) subject to power and association constraints. Our approach can be solved with off-the-shelf solvers and is benchmarked against a naive baseline where users associate to their closest visible satellite. Extensive Monte Carlo simulations demonstrate the effectiveness of the proposed method in controlling the handoff frequency while maintaining high user throughput. These performance gains highlight the effectiveness of our handover-aware optimization strategy, which ensures that user rates improve significantly, by about 40%, without incurring a disproportionate rise in the handoff frequency.

Figures (5)

• Figure 1: User handovers in a LEO satellite network: moving satellites periodically trigger user reallocation to maintain connectivity.
• Figure 2: Illustration of the four possible cases for the product $I_{u,s_{1},b}^{t-1} I_{u,s_{1},b}^t$. The binary product is used to detect handovers.
• Figure 3: Total throughput of all users over $20$ time slots for both approaches.
• Figure 4: Monte Carlo simulation results over $1000$ runs: user rates for both approaches.
• Figure 5: Comparison of total number of handovers for the proposed approach and the minimum distance policy.