Intelligent Power Management for QoS-Aware Multimedia in Cognitive Mesh Networks Using Reinforcement Learning
Bibliographic record
Abstract
In cognitive multimedia mesh networks (CMMNs), unlicensed users (secondary users, SUs) can share the spectrum with licensed users (primary users, PUs) on a non-interfering basis. The main challenge in CMMN is how to implement an efficient optimal control policy that can allocate spectrum and transmission powers for the SUs efficiently and how to adapt these resources to the changing network conditions. The power management scheme should control the SUs powers in such a way that their data communication rate is not affected. SUs power cannot be dropped indefinitely but it should be bounded such that the quality of service (QoS) for the SUs is still supported. Our objective is to serve the maximum number of users and support their QoS while protecting the PUs' rights of using the spectrum exclusively. Due to the direct relationship between the data rate and the quality of the communication channel, we propose a new spectrum allocation scheme that exploits the physical properties of the channel to achieve better performance. In addition to consider QoS for users, our scheme uses several heuristics for selecting channels that meet users requirements. These heuristics include channel error rates, PUs activities, channel capacity and channel adaptation time. Performance evaluation of the proposed scheme shows that the scheme is able to support additional SUs traffic while still ensuring PUs QoS.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".