Deep Reinforcement Learning-Based Signal Processing for Cell-Free Massive MIMO Networks in Coal Mine Power Grids
Bibliographic record
Abstract
In coal mine power grids, ensuring reliable, high-capacity, and low-latency communication is critical to maintaining efficient operations.To meet these demands, combining nonorthogonal multiple access (NOMA) with cell-free massive multiple input multiple output (CF-mMIMO) networks presents a powerful solution.This paper focuses on the signal processing and resource allocation challenges inherent in the downlink of CF-mMIMO-NOMA systems, specifically tailored to the complex communication environment of coal mines.We propose a hierarchical deep deterministic policy gradient (H-DDPG) algorithm to optimize system performance, with a focus on user pairing and power allocation.The algorithm addresses signal processing tasks at both system and link levels by leveraging two-layer control networks that operate on distinct time scales.At the system level, the user pairing problem is solved, while power allocation is optimized at the link level, with dedicated DDPG agents guiding both processes.Extensive simulations demonstrate that the proposed H-DDPG method significantly enhances the system sum rate compared to benchmark approaches, making it a robust solution for improving signal processing and resource management in coal mine power grid CF-mMIMO networks.
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 | 0.001 |
| 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 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".