Fuzzy Soft-Set Based Approach for Femto-Caching in Wireless Networks
Bibliographic record
Abstract
5G networks provide an interesting research challenge with the widespread use of Internet and the scale of mobile data traffic that grows explosively. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of small, low power small base stations (SBS) with small cache space (termed femto caches, or more popularly, helpers). Helpers have low rate backhaul but high storage capacity that can be used to cache most popular files. When users request files that do not exist in helpers, then the files will be transmitted from MBS to helpers. The availability of the data in local cache can significantly improve performance since it overcomes the constraints in wireless environment. This leads to improving network throughput and reducing end-toend and backhaul delay. Caching the optimal contents into femto caches proactively depending on the knowledge about files popularity distribution is not enoughthe since Internet users have different context and different preferences. In this paper, we propose an algorithm for proactive caching based on fuzzy softset (FSS) approach for decision making. The algorithm decides which files to cache and where to cache them depending on file popularity distribution, file to user preferences, file clustering, and helpers to connected users clustering. The simulation results show significant overall cache hit rate increase with the increasing number of user requests. The algorithm can effectively improve system performance by reducing the delay for downloading the files and proactively cache those files which will improve user satisfaction.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".