Bluetooth Clock Recovery and Hop Sequence Synchronization Using Software Defined Radios
Bibliographic record
Abstract
Bluetooth communication is based on frequency hopping spread-spectrum and time division duplexing. Bluetooth devices must be properly synchronized so that they can hop together; the synchronization is done by using the same channel set as well as the same hopping sequence within that channel set along with the time synchronization. Frequency hopping sequences are derived from Bluetooth device addresses and clock values. During the inquiry procedure as well as in the page procedure, frequency hop synchronization (FHS) packets are exchanged which contain the device addresses and clock values for the derivation of the frequency hop sequences. This paper presents the different possibilities to intercept and demodulate the frequency hop synchronization packets exchanged during the inquiry or the page procedure. It also presents a complete SDR prototype solution to get the master's device address and its clock value, just listing for a short time on a fixed RF frequency out of the 79 Bluetooth channels, without capturing the FHS packet. The prototype system is build and interfaced with an Ettus's USRP mother board and RFX2400 daughter board using the GNU radio framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".