Optimal multicast in multi-channel multi-radio wireless networks
Bibliographic record
Abstract
Recent advances in wireless technology have made it increasingly feasible to equip wireless nodes with multiple radios, thereby allowing each radio to exploit channel diversity in the form of orthogonal, non-overlapping transmission spectrums. Multi-channel operation mitigates interference, but at the same time raises new challenges for network optimization, in terms of judicious channel assignment for efficient bandwidth utilization. While previous research mostly studies optimizing channel assignment for unicast, we focus instead on multicast, which is an efficient mechanism for one-to-many data dissemination. We derive a model for optimal multicast in multi-channel multi-radio wireless networks under the assumption that channel assignment is static. Our model employs network coding as the multicast mechanism of choice, and exploits the broadcast nature of omnidirectional antennas for efficient bandwidth utilization. Based on the model derived, we formulate optimal multicast as a linear integer program. Two accompanying solutions are proposed: a greedy channel assignment scheme and an improved iterative scheme inspired by primal-dual algorithm design. The effectiveness of the two schemes are empirically examined through simulation studies, and are compared to results obtained from solving the integer program as well as its linear programming relaxation. Finally, we present an alternate model for optimal multicast under the assumption that transmission frequencies are not fixed divisions of the usable spectrum.
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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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".