Cross-Sensory Transmission for 6G-Enabled Immersive Communication
Bibliographic record
Abstract
Immersive communication, as a key usage scenario in 6 G, aims to provide interactive experiences by delivering real-time, high-fidelity sensory feedback (e.g., vision and touch). However, simultaneously achieving low latency, high data rate, and high reliability often poses a conflicting challenge from a transmission perspective. Unlike the optimization of a physical transmission environment (e.g., RIS-THz), in this work, we propose a cross-sensory transmission strategy that involves both encoding and networking, leveraging the potential correlations among various sensory modalities to support both data compression and enhancement. On the encoding side, we explore explainable surface semantics (e.g., texture, compliance) as intermediaries to associate visual and tactile sensory modalities for the design of a cross-sensory visual coding method. This method compresses the massive volume of visual data based on semantic correlations, significantly reducing bitrates to enable low-latency transmission. On the networking side, a cross-sensory masked pre-training approach is incorporated under a wide range of simulated packet loss. This approach facilitates fast and precise reconstruction of lost data using minimal observed data packets from both modalities, compensating for transmission reliability degradation under random and significant packet loss rates. Experimental results from a constructed VR education platform demonstrate that the proposed transmission strategy improves the data compression rate by more than 33% while maintaining a tolerance for packet loss rates of at least 50%.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".