Multi-Agent Moth-Flame Reinforcement Learning Based Broadcast Beam Optimization
Bibliographic record
Abstract
Currently, beamforming antenna array technologies are of utmost importance in 5G communication systems. These technologies are essential for optimizing the coverage and signal quality of the cellular network. However, the optimization of broadcast beams presents significant challenges due to the complex strategy profile space. Each beam can be configured with different widths and heights, making it difficult for conventional algorithms to handle. To address this issue, we propose a novel approach called Multi-Agent Moth-Flame Reinforcement Learning (MAMF-RL) algorithm for broadcast beam optimization. MAMF-RL combines reinforcement learning and moth-flame optimization algorithms to interactively search for the optimal broadcast beams. By decomposing the problem into multiple single-sector antenna configuration problems, MAMF-RL effectively reduces the algorithm complexity. We conducted experiments utilizing real data in an 18-sector wireless coverage area. To evaluate the performance of our proposed method, we compared it with traditional methods such as the particle swarm algorithm. The results demonstrate that our MAMF-RL model achieves an average coverage rate of 1.82% higher and a 13.74% lower overlapping coverage rate compared to traditional methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".