Wireless Virtual Reality in Beyond 5G Systems with the Internet of Intelligence
Bibliographic record
Abstract
Virtual reality (VR) over wireless has promising applications in healthcare, education, entertainment, and industrial production. However, it is difficult for the existing wireless systems to meet the needs of massive content transmission, ultra-low latency, and high computation in wireless VR. In this article, with the recent advances of edge intelligence and the Internet of Intelligence, we propose a novel framework that can jointly provide computation, storage, and communication resources for wireless VR in beyond 5G systems. In this framework, intelligence can be fully exploited to coordinate the computing, caching, and transmission systems to enable ubiquitous deployments of wireless VR. We present some key techniques and propose specific methods to support wireless VR. In addition, we propose a novel quantum-inspired RL reinforcement learning (QRL) algorithm for the multidimensional resource provisioning issue in wireless VR. In the simulations, some essential performance metrics are evaluated and some interesting results are presented, showing the effectiveness of the proposed strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".