On the performance of successive interference cancellation in random access networks
Bibliographic record
Abstract
Successive Interference Cancellation (SIC) is a physical-layer technique that enables reception of multiple overlapping transmissions. While SIC has the potential to boost the network throughput, if the MAC protocol employed in the network is agnostic to such a capability at the physical-layer, the full potential of SIC can not be utilized in the network. There have been a number of studies to design new SIC-aware MAC protocols or adjust the existing protocols to exploit SIC. Despite that, the exact effect of MAC protocols on the throughput of SIC-enabled networks is unknown. In this paper, we propose a novel SIC-aware MAC protocol based on the disparity of user channels in a wireless network and analyze its performance. We consider a simple random access protocol with no SIC as the base configuration and compare it with three other configurations with different levels of SIC-awareness. We show that while a SIC-enabled physical layer without a SIC-aware MAC protocol can increase the throughput of the network by 1.5×, a specifically designed MAC protocol is far more efficient achieving up to 3.3× improvement in throughput. Our SIC-aware MAC protocol is fully distributed and hence subject to selfish behavior of users. Thus, we also consider the case where the users behave selfishly. We model our proposed protocol as a one-shot simultaneous move game and derive a mixed strategy Nash equilibrium. We also show that we can set the cost of packet transmission in such a way that we get the optimal system throughput at the Nash equilibrium.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".