Power Control of 5G-Connected Vehicular Network Using PPO-Based Deep Reinforcement Learning Algorithm
Bibliographic record
Abstract
In this paper, we propose a novel power control in vehicular 5G-connected network using Deep Reinforcement Learning (DRL) algorithm. We investigate power allocation for Connected Autonomous Vehicles (CAVs) on uplink connections in mm-wave bands between the CAVs and Roadside Units (RSUs). Our objective is to achieve the desired uplink transmission capacity using the minimum required power and minimize co-channel interference for neighboring cells. To achieve this goal, we use the Proximal Policy Optimization (PPO) algorithm implemented by modified actor-critic architecture to solve the problem. In the proposed architecture, a Deep Neural Network (DNN) model is used to gain the desired outputs of the problem. The suggested approach is fully compatible with the existing 3GPP-based 5G architecture and uses the available quantized information in cellular users’ measurement reports which provides seamless integration within existing RAN architectures. The performance of the proposed algorithm is compared with multiple power control algorithms in various road conditions. Simulation results show that the proposed algorithm outperforms the 3GPP-based power control algorithm in the dynamic road environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".