Delay Optimization and Cross-Layer Design in Multihop Wireless Networks With Network Coding and Successive Interference Cancelation
Bibliographic record
Abstract
Network coding (NC) and multipacket reception with successive interference cancellation (SIC) have been shown to improve the performance of multihop wireless networks (MWNs). However, previous work emphasized maximization of network throughput without considering quality of service (QoS) requirements, which may lead to high packet delays in the network. The objective of this work is minimization of packet delay in a TDMA-based MWN that is jointly utilizing NC and SIC techniques for a given traffic demand matrix. We assume conflict-free scheduling and allow multipath routing. We formulate a cross-layer optimization that assigns time slots to links in a way that the average packet delay is minimized. The problem formulation results in a difficult mixed integer nonlinear programming (MINLP) that the state-of-art software can only solve for very small-sized networks. For large networks, we develop a heuristic approach that iteratively determines the optimal solution. We present numerical results, which show that the average packet delay and traffic handling capacity of a network, using w/o NC+SIC, NC, SIC and NC+SIC schemes, improves from left to right. The traffic capacity of NC+SIC is double of the w/o NC+SIC. Thus, combined utilization of NC and SIC techniques results in significant performance improvement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".