On the Benefits of Network Coding in Multi-Channel Wireless Networks
Bibliographic record
Abstract
Wireless mesh networks have emerged as a favorable infrastructure that promises to unify the existing 802.11 wireless LANs. With multiple orthogonal channels and possibly multiple interfaces on the mesh nodes, such networks can provide broadband access for a large number of wireless clients. However, efficient assignment of channels to the available network interfaces has long been a daunting task for network designers. Existing heuristic and theoretical work unanimously focuses on joint design of channel assignment with the conventional transport/IP/MAC architecture. In this paper, we show that a new paradigm, network coding, is able to further increase the capacity of multi-channel mesh networks. We propose a joint optimization problem that accounts for routing, channel assignment, and network coding, and analyze its potential performance gains over the non-coding schemes. This problem inspires a practical algorithm that naturally combines network coding and routing. We also explore the benefits of network coding for emerging multi-channel wireless networks, including 802.16 and 802.11n, and derive the upper bound for its performance gains over existing channel assignment protocols.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".