Joint Power and Admission Control in Cognitive Radio Networks
Bibliographic record
Abstract
Introduction Due to increasing demand for wireless access services, efficient utilization of the limited frequency spectrum has become crucial. Exclusive licensing of spectrum bands to specific users or services is very inefficient from the viewpoint of spectrum utilization, and it lacks the agility needed to support new applications. Cognitive radio networks (CRNs) have thus emerged as an adaptive cohabitation paradigm for wireless communication. The primary radio networks (PRNs) can dynamically share the spectrum with the secondary users (SUs) so that the SUs achieve their minimum acceptable quality-of- service (QoS), and at the same time, all the primary users (PUs) are protected in the sense that the SUs do not violate the QoS requirements of the PUs. The key concept in cognitive radio networks is opportunistic or dynamic spectrum access, which allows SUs to opportunistically access the band licensed to the PUs. There are two approaches for opportunistic spectrum access: spectrum overlay and spectrum underlay . In the overlay spectrum access strategy, the channels that are unused by the PUs are detected by the CRN through spectrum-sensing mechanisms and are assigned to the SUs. With overlay spectrum access, a channel-sharing method such as orthogonal frequency division multiple access (OFDMA) or time division multiple access (TDMA) is employed where spectrum holes (e.g., unused frequency or time slots) are detected and accessed in an opportunistic manner by SUs. In the underlay scenario, the available frequency spectrum is shared by all of the PUs and SUs, and since the admission of any of the SUs causes interference to all of the PUs’ receiving points, the interference caused by the SUs must be controlled through power control strategies in a way that all PUs are protected (i.e., all PUs achieve their target signal-to-interference-plus-noise ratio [SINR]). With underlay spectrum users employ channel sharing methods such as code-division multiple access (CDMA) or OFDMA in a way that the interference imposed by the SUs remains below a specified threshold and the QoS requirements of all of the PUs are supported. Therefore, with underlay spectrum access, which we focus on in this chapter, the overall spectrum can be utilized more effectively at the expense of higher complexity in controlling the QoS of SUs and the aggregate interference caused to the primary receivers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".