Polar-Domain Multi-User Key Generation in Near-Field Communications
Bibliographic record
Abstract
Given the substantial increase in the number of antennas in extremely large-scale antenna array (ELAA) systems, polar-domain channel modeling has been introduced to capture both angular and distance information in near-field environments. The fine-grained polar-domain channel provides additional sources of randomness, making it well-suited for physical layer key generation (PLKG). To minimize the pilot overhead in multi-user key generation and leverage the randomness from the polar-domain channel paths, we implement a zero-forcing (ZF)-based precoding scheme to mitigate the inter-path and inter-user interference. Using ZF precoding, we derive an analytical expression for the sum secret key rate (SKR) as a function of the power allocation variables, and then optimize these variables in the presence of eavesdroppers. Since the ZF method may not fully eliminate interference with imperfect channel state information (CSI), there could be correlation between the measurements of polar-domain channel paths and users. We present a channel decorrelation and reciprocity compensation approach that leverages principal component analysis (PCA) and deep neural networks (DNNs) to mitigate channel correlation issues. Specifically, PCA is first applied at the base station (BS) to decorrelate the composite CSI vector that aggregates the CSI of all users. Following this preprocessing, a DNN is trained to learn the mapping from the decorrelated uplink CSI to the corresponding original downlink CSI. This trained DNN then reconstructs a new version of the downlink CSI, enhancing the cross-correlation between the BS and the users’ CSI, thereby improving uplink/downlink reciprocity. Our simulations evaluate the effectiveness of the DNN-based reciprocity compensation by assessing the normalized mean squared error (NMSE) and the correlation between uplink and downlink CSI, the bit disagreement ratio (BDR) and the randomness of secret keys after quantization.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".