Backend
On the Alignment Between Spectral Efficiency and Positioning Accuracy in Multi-LEO Clusters
arXiv cs.NI0 views
arXiv:2610.13526v1 Announce Type: new Abstract: Low Earth Orbit (LEO) satellite constellations are expected to play a key role in future 6G Non-Terrestrial Networks (NTNs), where dense deployments enable cooperative multi-satellite processing for communication and positioning. While existing works have addressed communication and localization objectives separately, the correlation and alignment between these objectives remain largely unexplored. In this paper, we investigate the correlation between Spectral Efficiency (SE) and positioning accuracy in cooperative multi-LEO satellite clusters. We consider an uplink direct-to-device scenario in which multiple satellites jointly process user signals and evaluate cluster performance using SE and Position Error Bound (PEB) metrics. We derive Time-of-Arrival (TOA)-based and Doppler-based PEBs, while communication performance is quantified through the achievable SE under joint signal processing. Using realistic LEO constellation simulations, we demonstrate that for clusters of seven satellites, SE exhibits a strong negative correlation with Doppler-based PEB (correlation coefficient of $-0.72$). In contrast, its correlation with TOA-based PEB is notably weaker, ranging between $-0.55$ and $-0.37$. These findings suggest that communication-oriented LEO clusters are largely compatible with Doppler-based positioning, but not necessarily with TOA-based positioning.
Source:https://arxiv.org/abs/2610.13526PreviousManeuver Coordination Using V2I to Improve Traffic Efficiency at IntersectionsNextRAPID: Proactive Rate Control for Enabling High Data Rate and Low Latency in 5G Edge Networks
Related posts
I used to dream about coding on the train. Now I'm managing agents from my phone. 🚉🍁🍂
0dev.to Productivity
Streamlining GitHub Activity: Why Lost Filters Impact Software Engineering Measurement
0dev.to Productivity

The Roofline Model for LLM Inference
0dev.to Machine Learning
Deduplicate Your RAG Corpus Without Losing Its Citations
0dev.to Machine Learning

scm2cpp-learn: 44 scikit-learn estimators, same results, up to ~1000x faster
0dev.to Machine Learning
Marvell is buying Lumentum for $115B: what the optical bet means for AI cluster power, and what a fixed 4.5x exchange ratio really locks in
0dev.to Machine Learning