Decentralized and Joint Resource Allocation, Beamforming, and Beamcombining for 5G Networks With Heterogeneous MARL
Bibliographic record
Abstract
In this paper, we propose a novel Multi-Agent Reinforcement Learning (MARL) -based paradigm for distributed and joint resource allocation, beamforming (BF), and beam combining of uplink transmissions in 5G networks. The proposed paradigm employs two types of heterogenous agents that learn to perform and optimize different tasks in order to achieve the main objective of the system, as well as the objective of the individual agents. In the proposed paradigm, UEs can be multi-agents that optimize their own resource allocation and BF. In addition to these multi agents (i.e., UEs), the BS is a different type of agent that optimizes the combining of UEs’ transmissions. We developed three different implementations of our proposal using three different MARL algorithms: Independent Q Learners (IQL), Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and QTRAN. Various experiments were conducted to validate the usability of our proposal. Our results show that the proposed paradigm can successfully optimize the task of joint resource allocation, beamforming, and combining. Furthermore, we provide a comparative analysis of the three different implementations, highlighting noteworthy insights into the strengths and limitations of fully distributed algorithms, such as IQL, in comparison to algorithms employing the Centralized Training with Decentralized Execution (CTDE) framework, exemplified by QTRAN and MADDPG.
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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 | 0.002 | 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".