Towards Immersive Tactile Internet Experiences: Low-Latency FiWi Enhanced Mobile Networks With Edge Intelligence [Invited]
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
Historically, research efforts in optical networks have focused on the goal of continuously increasing capacity rather than on lowering end-to-end latency. This slowly started to change in the access environment with post-Next-Generation Passive Optical Network 2 research. The emphasis on latency grew in importance with the introduction of 5G ultra-reliable and low-latency communication requirements. In this paper, we focus on the emerging Tactile Internet as one of the most interesting 5G low-latency applications enabling novel immersive experiences. After describing the Tactile Internet's human-in-the-loop-centric design principles and haptic communications models, we elaborate on the development of decentralized cooperative dynamic bandwidth allocation algorithms for end-to-end resource coordination in fiber-wireless (FiWi) access networks. We then use machine learning in the context of FiWi enhanced heterogeneous networks to decouple haptic feedback from the impact of extensive propagation delays. This enables humans to perceive remote task environments in time at a 1-ms granularity.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it