Antijamming Capacity and Performance Analysis of Multiple Access Spread Spectrum Systems in AWGN and Fading Environments
Bibliographic record
Abstract
A unified multiple access channel model based on the time-bandwidth dimensionality is considered for the user capacity analysis of a wide variety of multiuser spread spectrum systems contaminated by smart jamming. Different users are distinguished by signatures of either direct sequence or hopping type. It is found that the jammer should spread its energy evenly over all degrees of freedom in order to minimize the average capacity or maximize the outage probability. Also, for the communicator to best make use of the channel resources, it is necessary to avoid hopping type signatures. As a practical realization, we consider the performance analysis of a synchronous frequently cited multicarrier frequency hopping CDMA system in AWGN and Rayleigh fading jammed uplink channels. We show that the best counter-jamming performance is attained when the number of subcarriers is maximal. It is demonstrated that when the receiver does not know the jammer state, concentrating instead of spreading the jamming power over the channel degrees of freedom will give rise to the worst performance for the communicators. Optimal weights for the receiver soft outputs in different channels are also obtained, for practical purposes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".