Multicast Scheduling with Cooperation and Network Coding in Cognitive Radio Networks
Bibliographic record
Abstract
Cognitive Radio Networks (CRNs) have recently emerged as a promising technology to improve spectrum utilization by allowing secondary users to dynamically access idle primary channels. As progress are made and computationally powerful wireless devices are proliferated, there is a compelling need of enabling multicast services for secondary users. Thus, it is crucial to design an efficient multicast scheduling protocol in CRNs. However, state-of-the-art multicast scheduling protocols are not well designed for CRNs. First, due to primary channel dynamics and user mobility, there may not exist commonly available channels for secondary users, which inevitably makes the multicast scheduling infeasible. Second, the potential benefits provided by user and channel diversities are overlooked, which leads to under-utilization of the scarce wireless bandwidth. In this paper, we present an optimization framework for multicast scheduling in CRNs, by fully embracing its characteristics. In this framework, base station multicasts data to a subset of secondary users first by carefully tuning the power. Concurrently, secondary users opportunistically perform cooperative transmissions using locally idle primary channels, in order to mitigate multicast loss and delay effects. Network coding is adopted during the transmissions to reduce overhead and perform error control and recovery. We jointly consider important design factors in our scheduling protocols, including power control, relay assignment, buffer management, dynamic spectrum access, primary user protection, and fairness. We also incorporate user, channel, and cooperative diversities. Two forms of multicast scheduling protocols in CRNs are proposed accordingly: (i) a greedy protocol based on centralized optimization; (ii) an online protocol based on stochastic optimization in both centralized and decentralized manners. With rigorous analysis based on Lyapunov optimization, we provide closed-form bounds to characterize the performance of our protocols, in terms of the interference to primary users and throughput utility of secondary users. With extensive simulations, we show that our proposed protocols can significantly improve the multicast performance in CRNs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".