Design and Performance Evaluation of Successive Interference Cancellation-Based Pure Aloha for Internet-of-Things Networks
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Bibliographic record
Abstract
This paper presents the design and performance evaluation of successive interference cancellation (SIC)-based pure Aloha (PA) for Internet-of-Things (IoT) networks. To this end, a window-based SIC algorithm is presented. A throughput model of the SIC-based PA is developed and analyzed to study both throughput and packet delivery ratio (PDR) performance metrics. The numerical results show that high throughput gain can be achieved in PA, thanks to the increased PDR contributed by the SIC. Specifically, after ten iterations of the SIC, the maximum throughput gain of the SIC-based PA can achieve up to 6.55 dB over the conventional PA. The SIC efficiency results also suggest that the interference residual level after each iteration of SIC plays an important role in terms of improving throughput in PA networks. Finally, the conditions under which the proposed SIC-based PA meet both throughput and PDR performance requirements are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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