Cooperative multicast scheduling with random network coding in WiMAX
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 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".