Spectral and energy efficient cognitive radio‐aided heterogeneous cellular network with uplink power adaptation
Bibliographic record
Abstract
Abstract In future heterogeneous cellular networks, cognitive radio compatible with device to device communication technique can be an aid to further enhance system spectral and energy efficiency. The unlicensed smart devices (SDs) are allowed to detect the available licensed spectrum and utilise the spectrum resource which is detected as not being used by the licensed users. In this work, we propose such a system and provide comprehensive analysis of the effect of selection of SDs' frame structure on the energy efficiency, throughput and interference. Moreover, uplink power control strategy is also considered where the licensed users and SDs adapt the transmit power based on the distance from their reference receivers. The optimal frame structure with power control is investigated under high‐signal‐to‐noise ratio (SNR) and low‐SNR network environments. The impact of power control and optimal sensing time and frame length, on the achievable energy efficiency, throughput and interference are illustrated and analysed by simulation results. It has been also shown that the optimal sensing time and frame length which maximizes the energy efficiency of SDs strictly depends on the power control factor employed in the underlying network such that the considered power control strategy may decrease the energy efficiency of SDs under very low‐SNR regime. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".