Toward Intelligent Resource Allocation on Task-Oriented Semantic Communication
Bibliographic record
Abstract
Task-oriented semantic communication (TOSC) has significant advantages in reducing the amount of data transmission and alleviating the scarcity of spectrum resources. Unlike traditional communication, the resource allocation in semantic communication is tightly linked to target intelligent tasks and specific interaction requirements. In this article, the intelligent resource allocation in a task-oriented manner is investigated. To further improve spectrum utilization and energy sustainability, a communication network combining energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is considered. This article proposes a semantic-aware resource allocation scheme in the EH-CR-NOMA scenario, where the quality of experience (QoE) is adopted as the evaluation metric. To achieve the preferential occupation of resources by data with richer semantic information, a joint optimization problem of the transmit power, time slot division factor, and semantic compression ratio of the semantic communication user is formulated. With the goal of maximizing the long-term QoE of TOSC, a two-tier deep reinforcement learning framework is designed to solve the semantic-aware resource allocation problem. By striking a trade-off between semantic rate and semantic fidelity, the proposed scheme can better satisfy user intentions.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".