Decentralized Resource Allocation in 5G Networks with Heterogeneous Multi-Agent Reinforcement Learning
Bibliographic record
Abstract
This thesis proposes the use of decentralized Multi-Agent Reinforcement Learning (MARL) for distributed resource allocation in 5G networks. We consider the cases where Resource Block (RB) allocation and Beamforming (BF) for uplink transmission is performed by each User Equipment (UE). Additionally, in a heterogeneous deployment, Base Stations (BSs) are a different type of agent optimizing Beam Combining (BC). We developed different implementations of our proposal using three different MARL algorithms: Independent Q-Learners (IQL), Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and QTRAN. Various case studies were conducted in a 5G simulation environment to validate the usability of our proposal. Our results show that the proposed approach can successfully perform joint RB allocation, BF, and BC. Our MARL solution achieved a minimum data rate for each UE and maximized the sum rate of all the UEs in the network.
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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) | 0.001 | 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".