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 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".