Understanding the Benefits of Successive Interference Cancellation in Multi-Rate Multi-Hop Wireless Networks
Bibliographic record
Abstract
The performance of wireless multihop networks depends on the achievable channel capacity for each transmission link as well as the level of spectrum spatial reuse in the network. For the latter one, successive interference cancellation (SIC) has emerged as an advanced PHY technique with the ability of decoding two or more overlapping signals and therefore allowing multiple concurrent transmissions. Effectively managing the transmission concurrency over the shared medium ensures good quality of transmission and therefore results in higher achievable transmission data rates. In this paper, we seek to understand the benefits of SIC and its interference management capabilities in a multi-rate multihop wireless network. To characterize the network performance under these characteristics, we follow a cross-layer design approach and formulate the joint routing and scheduling problem with rate control as a mixed integer linear program with the objective to maximize the minimum flow throughput. Given its large scale and combinatorial complexity, we follow a decomposition approach using column generation to solve the problem. However, the complexity of solving exactly the pricing subproblem limits the application of the model to very small size network instances. We develop one efficient greedy method for solving exactly the pricing subproblem as well as a simulated annealing based heuristic approach with very good performance. Our results indicate that SIC benefits strongly depend on the strength of the received signals. We show that transmission links with fixed higher data rates do not necessarily yield higher SIC gains because higher transmission rates result in sparser network topologies and thus less flexible routing. Larger networks with SIC capabilities and bitrate adaptation however are most effective in controlling the interference and improving the spatial reuse and thus reap the largest benefits with gains exceeding 20% over networks only with SIC capabilities or only with rate control.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".