Time-Correlated RF Performance Analysis for Direct-to-Cell (D2C) Networks

[Thilina_Uswatta_Liya]

Hi all,

I’ve been looking at how we should interpret classical LTE KPIs (RSRP, RSRQ, SINR) when a UE is connected to a Direct-to-Cell LEO satellite.

The key point is simple:

A stationary UE does not mean a stationary radio condition.

Because the satellite (the “cell”) is moving, RF condition, serving-cell state (PCI/TAC), and protocol state can all change with time even if the phone never moves.

I put together a short practical note on this topic. Sharing the full content as images below (4 pages).

Main points covered:
• Why D2C measurements need a time-correlated view (RF + cell state + protocol + application)
• Protocol-aware failure classification (Cell Acquisition → RACH → RRC → NAS → User Plane → Application)
• A practical 5-stage field procedure: Stabilize → Observe & Log → Trigger → Correlate → Classify
• How to interpret PCI/EARFCN changes and RF variation together

Happy to hear feedback from any

one already testing Starlink Direct-to-Cell or similar systems.

[hicelltek]

One extra check for the common timeline: how old is each RF sample when it gets logged? With Android API-based logging, I’d keep the CellInfo timestamp alongside the app’s logging time and align the clock references explicitly. Otherwise, an older RF value can appear to coincide with a protocol failure. Are your measurements coming from Android APIs, modem diagnostic logs, or both?

[Thilina_Uswatta_Liya]

The measurements are mainly from modem/drive test logs rather than Android API logging. We use a common time reference to align RF measurements, serving cell information, protocol events, and application events. We also consider timestamp differences and logging delays when correlating the data. This helps ensure that an RF measurement is correctly associated with the corresponding protocol or application event.