Distributed opportunistic spectrum access via adaptive carrier sensing in cognitive radio networks
Bibliographic record
Abstract
The limitations of current static spectrum management policy drive the idea of a more dynamic access policy to improve the efficiency of radio spectrum usage and accommodate the increasing demand for wireless communication applications. Known as the opportunistic spectrum access (OSA), the new paradigm allows cognitive secondary users (SUs) to access the licensed spectrum, provided that the interference to the licensed primary users (PUs) is limited. In a cognitive radio network, since SUs are intended to track and take advantage of instantaneous spectrum opportunities, adaptive learning-based spectrum access schemes are desired to optimize spectrum utilization and ensure a peaceful coexistence of licensed and unlicensed systems. This thesis deals with the modeling, development and analysis of OSA schemes in a cognitive radio network from both SU and PU perspectives. The research objective is to maximize the overall throughput of SUs, while sufficiently protecting the ongoing operation of PUs.From the SU perspective, to avoid the high-risk data loss due to the random return of PUs, we present a dynamic hopping transmission strategy for SUs to access the temporarily idle frequency slots of a licensed frequency band, with adaptive activity factors. Upon applying the dual decomposition, the optimal activity factor allocation algorithm is developed. To facilitate spectrum sharing in a decentralized manner, we propose an adaptive carrier sense multiple access (CSMA) scheme. Based on the proposed CSMA scheme, learning-based distributed access algorithms for SUs are devised, including non-game-theoretic and game-theoretic approaches. The proposed algorithms can be independently performed by each SU to learn its optimal activity factors from the locally available information. To evaluate the effects of inevitable collisions among SUs in the proposed adaptive CSMA scheme, the collision probability and saturation throughput are studied by both analysis and simulation. Simulation results show significant performance improvements in terms of the achievable throughput compared to the conventional CSMA scheme. From the PU perspective, by applying the proposed access scheme to SUs, we study the interference caused by SUs to the PU due to miss-detection, and also its effects on the capacity-outage performance of the PU in a cognitive radio network. Based on the developed statistical models for the interference distribution, closed-form expressions for the capacity-outage probability of the PU are derived to examine the effects of various system parameters on the performance of the PU in the presence of interference from SUs. The model is extended to investigate the effects of cooperative sensing on the aggregate interference and the capacity-outage performance, considering OR (logical OR operation) and maximum likelihood cooperative detection techniques.
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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) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".