Cooperative Deep Reinforcement Learning Enabled Power Allocation for Packet Duplication URLLC in Multi-Connectivity Vehicular Networks
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Bibliographic record
Abstract
Ultra reliable low latency communication (URLLC) in vehicular networks is crucial for safety-related vehicular applications. Mini-slot with a short packet that carries only a few symbols is used to reduce the transmission time interval and enable quick scheduling for URLLC that requires extremely low latency. However, a single air interface transmission of URLLC packets may fail due to the high mobility of vehicles. Leveraging multi-connectivity technologies, the real-time reliability of URLLC can be greatly enhanced without relying on packet retransmission. In this paper, we propose a multi-connectivity URLLC downlink transmission scheme for vehicular networks, where the URLLC packet is duplicated and transmitted over multiple independent wireless links to improve packet reliability. Specifically, we design a multi-agent cooperative deep reinforcement learning algorithm, called transformer associated proximal policy optimization (TAPPO), to achieve real-time robust power allocation for multi-connectivity URLLC with imperfect channel state information (CSI). The transformer neural network architecture is employed to share the information among multiple links serving the same URLLC user and choose appropriate transmit powers, enabling cooperation to ensure reliability while minimizing inter-cell interference and energy consumption. Extensive simulation results validate the effectiveness of multi-connectivity packet duplication for URLLC and proposed TAPPO for power allocation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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