Secrecy Performance in Ultra-Dense Networks with Multiple Associations
Bibliographic record
Abstract
Network densification is a promising approach to support the ever-increasing required capacity in cellular networks. Besides, securing the gigantic amount of sensitive data transferred within the network has become a critical issue. Ultra-Dense Networks (UDNs) with a massive number of deployed Small Cells (SCs) constitute the new trait of network densification. Exploiting this massive number of small cells and their proximity to served users, we can enhance the achievable secrecy rate in the network. In this paper, we study Physical Layer Security (PLS) in a multiple-association scenario where each user is served simultaneously by a group of the M closest SCs. This approach helps to mitigate the limitations in the backhaul link capacities of the SCs. Besides, it provides spatial diversity for the users when their data traffic is split into different paths which enhances secrecy performance. Using tools from stochastic geometry, we provide a lower bound analytical expression for the average secrecy rate per user. The obtained results via both analysis and system-level simulations show the significant gain in secrecy performance that can be attained from the proposed multiple associations scheme.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".