Secure Content Delivery in Two-Tier Cache-Enabled mmWave Heterogeneous Networks
Bibliographic record
Abstract
In this paper, we investigate secure content delivery in a two-tier cache-enabled millimeter wave (mmWave) heterogeneous network composed of a macro base station (MBS) and K small base stations (SBSs) with caching capabilities. We allocate finite cache units at the SBSs and MBS to pre-store files with high popularities, where the SBSs store the most popular files, and the MBS stores the less popular ones. To deliver the file requested by a legitimate user securely, two secure transmission schemes, namely, distributed beamforming and direct transmission, are employed at the SBSs and MBS, respectively. Moreover, artificial noise (AN) is combined with the above two transmission schemes to further improve transmission security. The connection outage probability, secrecy outage probability, and secrecy throughput for the proposed mmWave transmission schemes are obtained. Based on these results, we jointly design the transmission rates and the cache resource allocation between the SBSs and MBS to maximize the overall secrecy throughput. We also provide insights into how the overall secrecy throughput is influenced by various parameters, including transmission rates, power allocation ratio of the AN scheme, and cache allocation factor. Numerical results are eventually presented to validate our theoretical analysis and demonstrate the effectiveness of the proposed transmission schemes and cache resource allocation strategy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".