Partially Cooperative RL for Hybrid Action CRNs With Imperfect CSI
Bibliographic record
Abstract
Cognitive radio networks (CRNs) mitigate spectrum scarcity by leveraging the holes in the licensed spectrum to enable Internet of Things (IoT) devices to opportunistically access the spectrum. However, IoT devices need to sense the spectrum before they can access it, which is an energy-intensive process and hinders the practical implementation of opportunistic spectrum access for energy-constrained IoT devices. In this context, reinforcement learning-based algorithms that encourage cooperation among IoT devices to eliminate the need for constant sensing are promising candidates for practical CRN implementation. As exciting as the application of reinforcement learning to CRNs is, benchmarking the performance of different algorithms is a huge challenge due to a lack of standardized comparison metrics, especially for hybrid action spaces that comprise both discrete and continuous actions. We propose a hybrid discrete-continuous space deep reinforcement learning algorithm that maximizes the energy efficiency of CRNs by optimizing sensing, cooperation, and transmission by IoT devices. We also analyze the algorithm’s performance by setting the theoretical upper bound for throughput and find that it reaches 99.4% of the theoretical upper bound, while its discrete action-space version reaches 96% and other baseline algorithms range between 70% and 86%.
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 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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".