Joint Transmission Scheme and Coded Content Placement in Cluster-centric\n UAV-aided Cellular Networks
Bibliographic record
Abstract
Recently, as a consequence of the COVID-19 pandemic, dependence on\ntelecommunication for remote working and telemedicine has significantly\nincreased. In cellular networks, incorporation of Unmanned Aerial Vehicles\n(UAVs) can result in enhanced connectivity for outdoor users due to the high\nprobability of establishing Line of Sight (LoS) links. The UAV's limited\nbattery life and its signal attenuation in indoor areas, however, make it\ninefficient to manage users' requests in indoor environments. Referred to as\nthe Cluster centric and Coded UAV-aided Femtocaching (CCUF) framework, the\nnetwork's coverage in both indoor and outdoor environments increases via a\ntwo-phase clustering for FAPs' formation and UAVs' deployment. First objective\nis to increase the content diversity. In this context, we propose a coded\ncontent placement in a cluster-centric cellular network, which is integrated\nwith the Coordinated Multi-Point (CoMP) to mitigate the inter-cell interference\nin edge areas. Then, we compute, experimentally, the number of coded contents\nto be stored in each caching node to increase the cache-hit ratio,\nSignal-to-Interference-plus-Noise Ratio (SINR), and cache diversity and\ndecrease the users' access delay and cache redundancy for different content\npopularity profiles. Capitalizing on clustering, our second objective is to\nassign the best caching node to indoor/outdoor users for managing their\nrequests. In this regard, we define the movement speed of ground users as the\ndecision metric of the transmission scheme for serving outdoor users' requests\nto avoid frequent handovers between FAPs and increase the battery life of UAVs.\nSimulation results illustrate that the proposed CCUF implementation increases\nthe cache hit-ratio, SINR, and cache diversity and decrease the users' access\ndelay, cache redundancy and UAVs' energy consumption.\n
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".