Invited paper: The audacity of fiber-wireless (FiWi) networks: revisited for clouds and cloudlets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a growing awareness among industry players of reaping the benefits of mobile-cloud convergence by extending today's unmodified cloud to a decentralized two-level cloud-cloudlet architecture based on emerging mobile-edge computing (MEC) capabilities. In light of future 5G mobile networks moving toward decentralization based on cloudlets, intelligent base stations, and MEC, the inherent distributed processing and storage capabilities of radio-and-fiber (R&F) networks may be exploited for new applications, e.g., cognitive assistance, augmented reality, or cloud robotics. In this paper, we first revisit fiber-wireless (FiWi) networks in the context of conventional clouds and emerging cloudlets, thereby highlighting the limitations of conventional radio-overfiber (RoF) networks such as China Mobile's centralized cloud radio access network (C-RAN) to meet the aforementioned trends. Furthermore, we pay close attention to the specific design challenges of data center networks and revisit our switchless arrayed waveguide grating (AWG) based network with efficient support of east-west flows and enhanced scalability.
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.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it