On Antijamming in General CDMA Systems-Part II: Antijamming Performance of Coded Multicarrier Frequency-Hopping Spread Spectrum Systems
Bibliographic record
Abstract
In the first part of the paper, the capacity of a general multiuser code division multiple access (CDMA) jamming channel was analyzed for noncooperative and cooperative users in uplink and downlink static and Nakagami fading channels when the receiver has or lacks jammer state information. The results were applied to a unified channel model encompassing a variety of multiuser spread spectrum systems contaminated by jamming. It was found that the jammer should spread its energy evenly over all degrees of freedom in order to minimize the average capacity. In the second part of the paper, using a standard orthogonal frequency division multiplexing (OFDM) model, the performance of a coded version of a multicarrier frequency-hopping (MC-FH) CDMA system in static and Rayleigh fading jamming uplink channels is analyzed. The MC-FH system under study is a hybrid of the OFDM and frequency hopping concepts, and is also a practical example of the model developed in the first part. It is demonstrated that in the cases where the receiver knows the jammer state and the receiver lacks the jammer state, spreading and contracting the jamming power over the system bandwidth, respectively, will give rise to the worst performance for the communicators. Optimal decorrelator weights for the receiver soft outputs in different channels, valuable for practical system design, are also obtained.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".