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 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".