Cooperative multicast scheduling with random network coding in WiMAX
Why this work is in the frame
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Bibliographic record
Abstract
The Multicast and Broadcast Service (MBS) in WiMAX has emerged as the next-generation wireless infrastructure to broadcast data or digital video. Multicast scheduling protocols play a critical role in achieving efficient multicast transmissions in MBS. However, the current state-of-the-art protocols, based on the shared-channel single-hop transmission model, do not exploit any potential advantages provided by the channel and cooperative diversity in multicast sessions, even while WiMAX OFDMA provides such convenience. The inefficient multicast transmission leads to the under-utilization of scarce wireless bandwidth. In this paper, we revisit the multicast scheduling problem, but with a new perspective in the specific case of MBS in WiMAX, considering the use of multiple ODFMA channels, multiple hops, and multiple paths simultaneously. Participating users in the multicast session are dynamically enabled as relays and concurrently communicate with others to supply more data. During the transmission, random network coding is adopted, which helps to significantly reduce the overhead. We design practical scheduling protocols by jointly studying the problems of channel and power allocation on relays, which are very critical for efficient cooperative communication. Protocols that are theoretically and practically feasible are provided to optimize multicast rates and to efficiently allocate resources in the network. Finally, with simulation studies, we evaluate our proposed protocols to highlight the effectiveness of cooperative communication and random network coding in multicast scheduling with respect to improving performance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it