Resource Allocation for Image Transmission Using Adaptive Semantic and Bit Communication
Bibliographic record
Abstract
Semantic communication is an emerging technology to improve the communication efficiency in future networks. In this paper, we propose a multi-AP multi-user adaptive semantic and bit communication framework for image transmission, where each user can communicate with the access points via either semantic communication (SemCom) or bit communication mode. While the peak mean square error (PMSE) is a key parameter to characterize the difference between the original and corresponding recovered images, this metric has no closed form. We propose a data regression approach to approximate the PMSE. Then, the cost functions for the two types of communication modes are designed, where the delay and energy consumption for image transmission and the PMSE for the recovered image are considered simultaneously. Moreover, the computation delay and energy consumption for semantic feature extraction and recovery are also integrated into the SemCom cost function design. Then, an overall user cost minimization problem is formulated to jointly optimize the communication mode decision, user association, channel selection, power control, and computation resource allocation. To solve the formulated problem, we propose an improved particle swarm optimization based semi-cooperative matching (IPSO-SCM) algorithm, where a semi-cooperative matching (SCM) game is established to determine the communication mode decision, user association, and channel selection and the improved particle swarm optimization algorithm is designed to jointly optimize the power control and computation resource allocation in each step of the constructed SCM game. We further prove the effectiveness, convergence, stability, and extensibility of the proposed IPSO-SCM algorithm. Simulation results are provided to demonstrate the superiority of the proposed scheme.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".