SC <sup>3</sup> -MDRA: A New Approach to Coordinating Bi-Level Age of Information in AAV-Enabled 6G Integrated Networks
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Bibliographic record
Abstract
6G confronts a paradigm shift towards integrated sensing, caching, computation and communication (SC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>) 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 SC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> network 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 SC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> multi-domain resource allocation (SC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>-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, SC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>-MDRA allocates computation and bandwidth resources of APs for effectively delivering information to requesting users. Experimental results reveal that the proposed SC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>-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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it