Physical layer‐optimal and cross‐layer channel access policies for hybrid overlay–underlay cognitive radio networks
Bibliographic record
Abstract
The authors study the opportunistic spectrum access techniques for hybrid overlay–underlay cognitive radio networks. A secondary user (SU) chooses a channel, transmission mode and adjusts its power so that the interference limit is not crossed and its throughput is maximised. The authors assume that multiple primary user (PU) channels are available and the SU conducts spectrum sensing to access the channels. The objective is to maximise the throughput by switching between the overlay and underlay transmission modes. Using finite‐horizon partially observable Markov decision process framework, the authors first study the optimal policies, where the PU is assumed to be in busy, concurrent or idle state, and the SU either stays idle or transmits with any of the two designed power levels. Although the PU's states are hidden, their activity statistics, transmission ranges and interference thresholds are assumed to be known. Via Monte Carlo simulation, the authors evaluate the performance of physical layer optimal policy (PLOP) and cross‐layer policy (CLAP) and compare them with a fully observable optimal policy. The beliefs in each slot for both policies are updated using the forward algorithm based technique. Simulation results show that the proposed CLAP is more throughput efficient than the conventional PLOP.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".