Reliability Gain of Network Coding in Lossy Wireless Networks
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Bibliographic record
Abstract
The capacity gain of network coding has been extensively studied in wired and wireless networks. Recently, it has been shown that network coding improves network reliability by reducing the number of packet retransmissions in lossy networks. However, the extent of the reliability benefit of network coding is not known. This paper quantifies the reliability gain of network coding for reliable multicasting in wireless networks, where network coding is most promising. We define the expected number of transmissions per packet as the performance metric for reliability and derive analytical expressions characterizing the performance of network coding. We also analyze the performance of reliability mechanisms based on rateless codes and automatic repeat request (ARQ), and compare them with network coding. We first study network coding performance in an access point model, where an access point broadcasts packets to a group of K receivers over lossy wireless channels. We show that the expected number of transmissions using ARQ, compared to network coding, scales as ominus (log K) as the number of receivers becomes large. We then use the access point model as a building block to study reliable multicast in a tree topology. In addition to scaling results, we derive expressions for the expected number of transmissions for finite multicast groups as well. Our results show that network coding significantly reduces the number of retransmissions in lossy networks compared to an ARQ scheme. However, rateless coding achieves asymptotic performance results similar to that of network coding.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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