SC <sup>3</sup> -MDRA: A New Approach to Coordinating Bi-Level Age of Information in AAV-Enabled 6G Integrated Networks
Bibliographic record
Abstract
6G confronts a paradigm shift towards integrated sensing, caching, computation and communication (SC3) networks, designed to render comprehensive information services and support diversified applications, in which Age of information (AoI) serves as a pivotal metric for evaluating the data freshness during the end-to-end information service procedure. However, existing works mainly focus on single-level AoI modeling, which fails to maintain fresh information in heterogeneous network infrastructure including autonomous aerial vehicles (AAVs) and ground access points (APs). Therefore, in this paper, we propose a AAV-enabled integrated SC3network model with bi-level AoI concept, where a AAV exploits common signals to collect sensory information from targets whilst updating the cached items of APs. Thereafter, we formulate a long-term optimization problem to coordinate bi-level AoI by jointly scheduling target sensing and caching updates, together with AAV trajectory and beamforming design. To tackle this intractable problem, we develop a deep reinforcement learning-based solution named SC3multi-domain resource allocation (SC3-MDRA). This algorithm innovatively incorporates hindsight experience replay and sharpness-aware minimization to overcome sparse reward as well as enhance policy adaptivity, thereby making immediate decisions in response to dynamic AoI status. Additionally, SC3-MDRA allocates computation and bandwidth resources of APs for effectively delivering information to requesting users. Experimental results reveal that the proposed SC3-MDRA outperforms baseline methods in terms of both learning convergence and system overall performance. Besides, the tradeoff between information freshness and AAV energy consumption is delineated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".