Impact of estimated CSI quantization on secrecy rate loss in pilot-aided MIMO systems
Bibliographic record
Abstract
In this paper, we investigate the system performance from the physical layer security provision under imperfect channel state information (CSI). In a classical transmitter (Alice)-legitimate receiver (Bob)- eavesdropper (Eve) model, we introduce artificial noise (AN) to disturb Eve's reception, as Eve's CSI is unknown to Alice. For designing the transmit beamforming vector of the information signal and precoding matrix of AN, Bob feeds back the quantized CSI estimation to Alice. Due to the effects of imperfect CSI at Alice, the secrecy system performance is upper bounded at high SNRs. In order to overcome the problem, by utilizing our derived upper bound on the secrecy rate loss, we put forward a scaled feedback strategy for the secrecy system. By employing the proposed strategy, the secrecy rate increases with transmit power despite that Alice can only obtain imperfect Bob's CSI, and the secrecy rate loss between perfect CSI and imperfect CSI can be controlled within a certain gap. Computer simulations are provided to verify our derived results.
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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".