Analysis of a Random Channel Access Scheme with Multi-Packet Reception
Bibliographic record
Abstract
A key advantage of viewing communications in wireless networks as multiple access rather than a plurality of point-to-point transmissions, is its robustness towards multiple access interference. Concurrent packet transmissions are allowed to coexist thus deviating from the traditional view of enforcing collision-footprints around the transmitter-receiver pairs. What are the performance gains of employing channel access strategy based on a multiple access channel in a multihop wireless network? We consider a wireless multihop network, where nodes have a joint decoding capability to resolve up to K multiple concurrent packet transmissions from other nodes in their range. The basic assumptions are that the packet transmissions are asynchronous, i.e., nodes are completely uncoordinated, and that the packet transmission at each node is based on a probabilistic model. In this paper, we show that a simple random access strategy for communication over such channels offers significant gains in throughput while reducing latency in congested wireless networks. More precisely, we characterize the throughput performance gains through an exact analysis for the case of K=2 and also offer tight approximations for arbitrary K. Furthermore, we study the asymptotic throughput behavior and prove asymptotic optimality of random channel access over multiple access channel.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".