Secrecy Rate Performance of Cache-enabled Millimeter Wave Cellular Networks
Bibliographic record
Abstract
Secure transmission of cached files in wireless networks is crucial to protect the transmitted files from interception by illegitimate nodes. This paper analyzes the secrecy rate performance of a cache-enabled millimeter wave (mmWave) cellular network in the presence of colluding eavesdroppers. The average cache hit probability is computed and the stochastic geometry framework is used to evaluate the average secrecy rate of the network, taking into consideration directional beamforming and Nakagami-m fading. Moreover, the size of the available files is modeled by a Pareto distribution based on existing studies, while a memory and file size-aware caching (MFC) scheme is proposed. The MFC scheme incorporates an optimized memory allocation algorithm that reserves memory blocks in the cache of a base station (BS) based on the frequency of the requested files, the parameters of the Pareto file size distribution, and the network design parameters. The numerical results show that the proposed MFC scheme achieves up to two-fold gain in the average secrecy rate compared to the state-of-the-art probabilistic and most-popular-content (MPC) caching schemes. Furthermore, the impact of the skew exponent and the colluding eavesdroppers' intensity on the average cache hit probability and secrecy rate, respectively, is investigated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".