On Joint Offloading and Resource Allocation: A Double Deep Q-Network Approach
Bibliographic record
Abstract
Multi-access edge computing (MEC) is an important enabling technology for 5G and 6G networks. With MEC, mobile devices can offload their computationally heavy tasks to a nearby server which can be a simple node at a base station, a vehicle or another device. With the increasing number of devices, slices and multiple radio access technologies, the problem of task offloading is becoming an increasingly complex problem. Thus, traditional approaches experience limitations while machine learning algorithms emerge as promising methods. In this paper, we consider binary and partial offloading problems and aim to jointly find optimal decisions for offloading and resource allocation which maximize the number of computed bits while minimizing the energy consumption. This allows improved usage of uplink transmit power and local CPU resources. We propose the Deep Reinforcement Learning for Joint Resource Allocation and Offloading (DJROM) algorithm that uses the double deep Q-network approach and models UEs as agents. We compare the proposed approach with two other machine learning based techniques, namely, multi-agent deep Q-learning (MARL-DQL) and multi-agent deep Q network (MARL-DQN) under fixed and mobile scenarios. Our results show that, DJROM scheme enhances the efficiency better than the other compared algorithms.
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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 | 0.002 |
| 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) | 0.003 | 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".