Energy‐efficient cross‐layer design of dynamic rate and power allocation techniques for cognitive green radio networks
Bibliographic record
Abstract
ABSTRACT In this paper, we investigate cross‐layer adaptive rate scheduling techniques for cognitive green radio networks, where a secondary base station is communicating with secondary users (SUs). The base station is equipped with individual finite size buffer for each SU. The activity statistics of the primary users (PUs) are independent and identically distributed. The SUs detect their states and select free channels of the PUs. We study two different methods for PU (or channel) selection. The power minimization problem is formulated as an infinite‐horizon partially observable Markov decision process. The adaptation policy is obtained using maximum likelihood heuristic policy (MLHP) technique because optimal policy for partially observable Markov decision process is intractable. We assume that transition probabilities of the PUs and fading channel between the SU's transmitter and receiver are known. By tracking beliefs of the PUs' hidden states, the SU takes decision on the transmission rate to minimise energy consumption along with delay for a given bit error rate of the communications. Simulation results are given to show the performance of the proposed MLHP. We find that MLHP performs very close to fully observable optimal policy. We provide pointers to choose design parameters (such as delay, number of antennas and channels) for the cognitive green radio network so that the scheduler becomes the most energy‐efficient for a given quality of service requirements of the handled application. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 | 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".