Precoding Design for Key Generation in Extremely Large-Scale MIMO Near-Field Multi-User Systems
Bibliographic record
Abstract
This paper develops a physical layer key generation (PLKG) scheme that utilizes artificial randomness in extremely large-scale multiple-input multiple-output (XL-MIMO) near-field multi-user communications to produce shared secret keys for legitimate users. Unlike traditional PLKG schemes, which rely on the variation of wireless channels, this approach introduces noise power via the precoding vectors to create dynamic fluctuations in the line-of-sight (LoS) channels, emulating the rapid changes typically observed in fast-fading channels. This artificial randomness ensures that the user equipment (UEs) can generate secret keys while effectively preventing potential eavesdropping from malicious eavesdroppers. In particular, a novel channel probing protocol is designed, enabling multiple UEs to simultaneously agree on secret keys with the base station (BS) using non-orthogonal pilots, which exploits the difference in the distances and spatial angles of UEs in near-field communications. Secondly, to maximize the secret key rate, an alternating optimization algorithm is proposed, solving two sub-optimization problems. The first sub-problem employs the singular value decomposition (SVD) method to identify the legitimate space and its orthogonal subspace for generating secret keys and preventing eavesdropping attacks, respectively. Subsequently, a Dinkelbach method-based power allocation algorithm is developed to allocate noise power to these two spaces. The second sub-problem uses a water-filling algorithm to implement power allocation among multiple UEs. Finally, to address the issue of precoding noise not being considered in the alternating optimization problem, a deep learning-based method is introduced, which further improves the performance of the scheme. Simulations demonstrate the efficiency of the proposed PLKG scheme over existing schemes.
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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 | 0.001 |
| 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".