Dynamic Load-Balancing Spectrum Decision for Heterogeneous Services Provisioning in Multi-Channel Cognitive Radio Networks
Bibliographic record
Abstract
In this paper, we study dynamic load-balancing spectrum decision for a cognitive radio network (CRN) that dynamically distributes packets from the secondary user (SU) to different available primary channels. We consider two different classes of services at the SU, i.e., delay sensitive (DS) and best effort (BE) services, and assign a higher priority to the DS services. We apply priority queuing model to address this priority issue in the CRN. Based on the queuing model, two Markov decision processes (MDPs) are formulated with objectives to minimize the average delay of both services while guaranteeing the priority of the DS services. Reinforcement learning is applied to find the optimal solutions when the traffic and channel characteristics are unknown. To address the computational complexity issue in the MDP solutions, we propose a myopic method based on the estimated packet sojourn time, which is derived by formulating a phase type distribution. Simulation results demonstrate the effectiveness of all proposed algorithms for load-balancing spectrum decision. It also shows that the proposed myopic scheme can achieve significant reduction on computational complexity with a cost on the delay performance of low priority BE services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".