Semantic-Empowered Utility Loss of Information Transmission Policy in Satellite-Integrated Internet
Bibliographic record
Abstract
The recent pervasive network intelligence for 6G has further triggered extensive research into semantic communication. In response to the challenges of its effectiveness aspects, we propose a novel semantic metric, utility loss of information (UoI), that can quantify the impact of duration and severity of mismatch transceivers, and address the shortcomings of existing semantic metrics. Then, this paper contributes meaningful results centered around UoI by utilizing a deep reinforcement learning (DRL) technology to intelligently choose when to sample, how to adopt the appropriate number of coded packets, and whether to retransmit in a network coding hybrid automatic repeat request (NC HARQ) aided satellite-integrated Internet, which is characterized by high bit error rate (BER), delayed feedback, and rapid channel dynamics. The approach aims to strike an optimal tradeoff between UoI and energy consumption (TOUE). More precisely, we cast the joint minimization problem of UoIand energy consumption as a partially observable Markov decision process (POMDP). Subsequently, a prioritized experience replay-aided dueling double deep Q-network (PER-D3QN) is adopted to address this problem. Simulation results validate that our TOUE policy can yield significant gains over several state-of-the-art sampling and transmission polices, and demonstrate its sensitivity to changes in goal requirements.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".