Bibliographic record
Abstract
Cognitive radio networks are smart networks that automatically sense the channel and adjust the network parameters accordingly. Cognitive radio is an emerging technology that enables the dynamic deployment of highly adaptive radios that are built upon software defined radio technology. The radio technology allows the unlicensed operation to be in the licensed band. The cognitive radio network paradigm therefore raises many technical challenges such as the power efficiency, spectrum management, spectrum detection, environment awareness, the path selection as well as the path robustness, and security issues. \nTraditionally, in the routing approaches in the wired network, each node allows a maximum load through the selected route while traditionally in the routing approaches in wireless network, each node broadcasts its request with the identification of the required destination. However, the existing routing approaches in cognitive radio networks (CRN) follow the traditional approaches in wireless network especially those applied for ad hoc networks. In addition, these traditional approaches do not take into account spectrum trading as well as spectrum competition among licensed users (PUs). \nIn this thesis, a novel QoS based route management approach is proposed by introducing two different models; the first model is without game theory and the second model is with game theory. The proposed QoS routing algorithm contains the following elements: (i) a profile for each user, which contains different parameters such as the unlicensed user (secondary user, SU) identification, number of neighbors, channel identification, neighbor identification, probabilities of idle slots and the licensed user (primary user, PU) presence. In addition, the radio functionality feature for CRN nodes gives the capability to sense the channels and therefore each node shares its profile with the sensed PU, which then exchanges its profile with other PUs, (ii) spectrum trading, a PU calculates its price based on the SU requirements, (iii) spectrum competition, a new coefficient α is defined that controls the price because of competition among PUs and depends on many factors such as the number of primary users, available channels, and duration of the usage, (iv) a new function called QoS function is defined to provide different levels of quality of service to SUs, and (v) the game theory concept adds many features such as the flexibility, the dynamicity in finding solutions to the model and the dynamic behaviors of users. Based on the previous elements, all possible paths are managed and categorized based on the level of QoS requested by SUs and the price offered by the PU. The simulation results show that the aggregate throughput and the average delay of the routes determined by the proposed QoS routing algorithm are superior to existing wireless routing algorithms. Moreover, network dynamics is examined under different levels of QoS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".