Performance of a Frequency-Hopped Real-Time Remote Control System in a Multiple Access Scenario
Bibliographic record
Abstract
The ubiquitous presence of powerful low-power wireless systems on chip (SoC) able to operate in the Industrial, Scientific and Medical (ISM) band has brought a new enhanced operational choice for real-time Radio Control (RC) applications such as aircrafts and cars in the hobby grade category. Frequency Hopping Spread Spectrum (FHSS) has become the dominant transmission technique for the previously mentioned hardware platform. Even though, FHSS provides for resilience to noise and interference, partial-band type of interference could be specially harmful with regards to the overall system performance. This is critical in real-time RC applications as it could increase system latency. The present paper characterizes the performance of a single real-time RC application, which operates in a realistic multi-user ISM environment by means of two main metrics: System Lag Occurrence Probability (SLOP) and Probability of Losing a Packet (PoLP). Both Synchronous FHSS Multiple Access (SFHSS-MA) and Asynchronous FHSS Multiple Access (AFHSSMA) environments have been modeled. Simulation results show the level of impact on system performance of key engineering parameters such as clock drift, number of co-located users, and variable data packet duty cycle.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".