Bibliographic record
Abstract
With the explosive growth of wireless data transmission, wireless content sharing is facing significant challenges such as a surge in data transmission scale, resource shortages, and a volatile communication environment. Consequently, dynamic caching technology based on D2D communication has emerged. Cache network is a distributed network architecture where network nodes (such as user devices or base stations) have caching capabilities and can store part of the content to reduce content transmission delay and network traffic. In cache networks, considering the short distance characteristics of direct device communication and the self offloading advantage of user caching, a cache mechanism is proposed. The paper analyzes the file offload capability and packet loss rate of the cache network. First, this paper analyzes network interference. Considering the randomness of content caching and requests, it derives a closed-form expression of the successful transmission probability based on random geometric theory. Secondly, considering the randomness of content requests and the limited cache capacity, a user cache queue model is established to analyze the transmission process of request files, and an M/G/1 queuing service model is established. The files requested by users can be obtained through two offloading methods: self offloading and device direct connection (D2D) communication offloading. The queue state of the files is constructed as a Markov chain, and the file queue state under different transmission modes is analyzed. Key performance parameter expressions such as successful offloading probability are obtained. Simulation shows that in networks with D2D assisted caching, in the actual situation of limited cache space, the higher the popularity index, the easier it is for the requested content to be satisfied by its own cache, the smaller the cache queue length, and the lower the packet loss rate. Auxiliary caching strategies can leverage the advantages of their own caching and D2D communication, effectively reducing the mean delay for users to obtain request files.
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 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 | 0.001 | 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".