Resource adaptations for revenue optimization in cognitive mesh network using reinforcement learning
Bibliographic record
Abstract
Nowadays, licensed users (primary users, PUs) can provide a means for offering internet service for unlicensed users (secondary users, SUs). We explore the ability of cognitive mesh network (CMN) to offer quality of service (QoS) required by real time services, streaming multimedia and other applications. We consider the approach where PUs rent the surplus spectrum to SUs to get some reward. However, when a PU rents more spectrum to SUs, its quality of service (QoS) is degraded due to a reduction of the 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 the extracted control policy. In this work, we propose a novel resource management scheme in the radio environment. RL is used as a means for extracting an optimal policy that helps a PU to adapt to the changing network conditions, so that the PU's profit is maximized continuously over time. The proposed scheme integrates different requirements such as rewards for PUs, QoS for PUs and the radio environment conditions. Performance evaluation of the proposed RL solution shows that the scheme is able to adapt to different network conditions and to guarantee the required QoS for PUs. Moreover, it is shown that CMN can support additional SUs traffic while still ensuring PUs QoS and maximizing PUs profits.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".