Deep Reinforcement Learning-Based Computation Offloading for Mobile Edge Computing in 6G
Bibliographic record
Abstract
The impending 6G network is envisioned to seamlessly interconnect a myriad of consumer electronics (CEs), facilitating a wide array of applications accessible from any location and at any time. To advance this objective, our paper proposes the integration of Mobile Edge Computing (MEC) with a multi-rotor Unmanned Aerial Vehicle (UAV), aiming to furnish computation offloading services for CEs of Ground Devices (GDs). Additionally, charging stations (CSs) are utilized to wirelessly charge the UAVs. Our objective is to minimize the UAV’s energy consumption for the entire mission by jointly optimizing both resource allocation and the UAV’s trajectory simultaneously. This entails solving a mixed-integer nonlinear programming (MINLP) optimization problem. Initially, we decompose the UAV’s trajectory into discrete offloading and charging locations, guided by a decision matrix. we decompose the optimization problem into two sub-problems. The first one determines offloading locations and resource allocation using Particle Swarm Optimization (PSO). The second one optimizes the decision matrix by incorporating PSO outputs and employing Double Deep Q-Network (DDQN), a form of deep reinforcement learning. Simulation results demonstrate that the proposed solution significantly reduces energy consumption compared to baseline schemes.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".