Does Network Coding Combined With Interference Cancellation Bring Any Gain to a Wireless Network?
Bibliographic record
Abstract
We investigate the achievable performance gain that network coding (NC) when combined with successive interference cancellation (SIC) brings to a multihop wireless network. While SIC enables concurrent receptions from multiple transmitters, NC reduces the transmission time-slot overhead, and each of these techniques has shown independently great benefits in improving the network performance. We present a cross-layer formulation for the joint routing and scheduling problem in a wireless network with NC (with opportunistic listening) and SIC capabilities. We use the realistic signal-to-interference-plus-noise ratio (SINR) interference model. To solve this combinatorially complex nonlinear problem, we decompose it (using column generation) to two linear subproblems-namely opportunistic NC aware routing and scheduling subproblems. Our scheduling subproblem consists of activating noninterfering NC components, rather than links, which do not interfere with each other and will be used to route the traffic. We further extend our design to consider a multirate multihop wireless network with interference cancellation capabilities. We use numerical evaluation to present the achieved performance gain and compare our work to three other models: a base model with no NC and SIC, a model with only NC, and a model with only SIC capabilities. The numerical results show that our proposed method (both with and without variable transmission rate selection) achieves performance gains that range between moderate and significant for the various considered scenarios. Such improvements are attributed to the joint capabilities of SIC and NC in effectively controlling the interference and improving the spatial reuse.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 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".