Performance of Multiple-Access Frequency-Hopped Systems in the Presence of Spurious Tones
Bibliographic record
Abstract
Frequency hopping (FH) can be used to combat jamming. In multiple access applications such as for satellite communications, multiple FH signals are combined by frequency division multiple access (FDMA). The frequency synthesizers in such an application have requirements for fast switching times, a large number of hop frequencies, wide hopping bands, and very small resolution. The need to meet all these requirements simultaneously makes the synthesizer implementation challenging. Furthermore, the need for hopping makes it more difficult to control spurious tones compared to non-hopped systems. It is useful to be able to determine the effects of spurs on performance in order that the synthesizer specification not be unnecessarily stringent and expensive. In this paper, an approach for analyzing the effects of spurious signal levels is provided. Both the signal tone, such as used in various forms of FSK and PSK, and the spurious tone are added together with some random phase difference. This combined tone results in an equivalent signal power with a corresponding SNR, which is then used for computing the new error rate. The effective error rate is then computed by averaging the error over all the possible phase differences. The loss in effective SNR due to spurs is then computed. As a detailed example, this approach is applied to non-coherent FSK modulation for both fast (one or more hops/symbol) and slow (more than one symbol per hop) hopping. The application of this approach to other modulations is summarized
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".