A novel nonlinear secure transmission design for MU-MISO systems with limited feedback
Bibliographic record
Abstract
We study transceiver design for secure communication of multiuser multi-antenna systems with assistant of a cooperative helper. There is an external multiple-antenna eavesdropper trying to obtain the confidential messages. Due to the finite-rate constraint of feedback channels, only quantized channel state information of the legitimate users is available at the transmitter and the helper. A nonlinear precoding strategy using Tomlinson Harashima precoding at the legitimate transmitter and a null-space beamforming scheme at the helper are proposed based on the quantized channel state information. Assuming a genie-aided perfect successive interference cancelation at Eve, we obtain closed-form expressions of the performance bounds for the achievable ergodic rate of each legitimate user and the ergodic secrecy sum rate. Numerical results illustrate that our proposed nonlinear-precoded strategy outperforms linear precoding scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".