In-Band Full-Duplex Discriminatory Channel Estimation Using MMSE
Bibliographic record
Abstract
This paper proposes full-duplex transmissions from legitimate nodes to achieve channel estimation performance deterioration at an eavesdropper as compared to the legitimate receiver. The proposed discriminatory channel estimation (DCE) technique comprises of two stages where, in the first stage, the self-interference channel is estimated by the respective legitimate nodes. Followed by in-band full-duplex transmission from both legitimate nodes for channel estimation at legitimate nodes, while providing equivocation at the eavesdropper due to the superposition of two signals. The discrimination of channel estimation performance provides secrecy against the passive eavesdropper while delivering information to the legitimate receiver. We provide the mean square error (MSE) to indicate the performance achieved by linear minimum mean square error (LMMSE) estimators. We have also provided bit error rate (BER), and secrecy capacity analysis to indicate the performance of secure communication achieved by securing the channel estimates from the eavesdropper. The BER analysis shows that for proposed DCE, BER at the eavesdropper is close to 0.1 while the legitimate node is able to robustly decode the information. Finally, simulation results show that the proposed DCE outperforms existing DCE techniques for the considered scenario.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".