When Spectrum Sharing in Cognitive Networks Meets Deep Reinforcement Learning: Architecture, Fundamentals, and Challenges
Bibliographic record
Abstract
Next-generation wireless networks require the integration of cognitive networks (CNs) and decision-making techniques to improve the spectrum efficiency. The conventional spectrum sharing schemes require full channel state information of CNs and cannot satisfy the low-latency requirement of nextgeneration wireless networks. Artificial intelligence has shown its high potential to perform decision-making and improve resource utilization efficiency. Hence, we propose a multi-agent reinforcement learning (MARL)-based scheme for spectrum sharing, which is a promising self-decision technique in highly dynamic and complex wireless networks. We analyze several key challenges when MARL is applied to spectrum sharing, such as multi-objective function formulation, multi-dimensional action space, and partial channel state information. Then, we propose efficient solutions and apply the explainable DRL to improve the convergence efficiency in spectrum sharing. The proposed architecture, fundamentals, and challenges provide a clear vision for MARL in CNs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".