Deep Reinforcement Learning Enabled Power Allocation for Multi-Connectivity C-V2X Downlink
Bibliographic record
Abstract
Cellular vehicle-to-everything (C-V2X) network is a promising solution to support on road diverse quality of services (QoS) such as ultra reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB). However, satisfying the stringent QoS requirements in high-dynamic C-V2X environment is very challenge. In this paper, we leverage the multi-connectivity technology to enhance the reliability of downlink URLLC in C-V2X. Specifically, with the aid of the cloud radio access network (C-RAN), the network controller duplicates each URLLC packet and transmits its replicas over multiple independent wireless links. To ensure the reliability of URLLC links while maximizing the average rate of eMBB links, we design a coordinated multi-agent deep reinforcement learning algorithm for real-time power allocation of multi-connectivity URLLC links. Each URLLC link is treated as an agent here, and its transmit power is its action. The multiple links serving the same URLLC user are coordinated with a three-layer neural network for information sharing, allowing them to cooperatively choose transmit powers in terms of ensuring reliability while minimizing inter-cell interference and energy consumption. Extensive simulation results validate the effectiveness of the proposed power allocation algorithm for multi-connectivity downlink URLLC.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".