Cognitive mesh network resource adaptations using reinforcement learning
Bibliographic record
Abstract
In this paper, we consider the cognitive network (CR) that can provide a means for offering quality of service (QoS) required by real time services, streaming multimedia and other applications. We consider the approach where the licensed users (primary users, PUs) rent the surplus spectrum to unlicensed users (secondary users, SUs) to get some reward. However, when PU leases more spectrum to the SUs, its quality of service (QoS) is degraded due to a reduction of spectrum. This complex contradicting requirement is embedded in our reinforcement learning (RL) model that is developed and implemented as shown in this paper. Available spectrum is managed by the PU which executes control policy to provide end-to-end QoS connection to the SUs as well as maximizing its revenues. Maximizing profit is a key objective for the PU. In this work, we propose a novel resource management approach in the radio environment. RL is used as a means for extracting an optimal policy that helps a PU to adapt to the changing radio environment conditions, so that the PU profit is maximized continuously over time. The approach integrates different requirements such as rewards for PUs, and the cost of spectrum. Performance evaluation of the proposed spectrum sharing approach shows that the approach is effective at attaining maximized revenue under varying network conditions.
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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 | 0.000 | 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 | 0.001 | 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".