A Random Access Protocol with Multi-Packet Reception for Infrastructure-Less Wireless Autonomic Networks
Bibliographic record
Abstract
A random access protocol with multi-packet reception (MPR) capability for infrastructure-less wireless autonomic networks is introduced and analyzed. In these networks mobile nodes may communicate with each other directly without a central entity (base station), where each mobile node either will be in a transmitting mode or in a receiving mode or in an idle mode. The throughput per node and the packet retransmission probability depend exclusively on the MPR capability and the ratio of the transmission probability and the receiving probability of each mobile node. For a given ratio of the transmission probability and the receiving probability of each mobile node, throughput-delay performance increases with the increase of MPR capability. In the proposed infrastructure-less networks, mobile nodes can control the network traffic very precisely by controlling the three parameters. These three parameters are transmission probability, receiving probability and idle mode probability of each mobile node. Since each mobile node can control the network traffic very precisely to obtain the maximum throughput, the network is autonomic, i.e., self-optimizing. The optimum transmission probability of each mobile node to obtain the maximum throughput is evaluated. The throughput utility increases with the increase of MPR capability. On the other hand, the cost per mobile node also increases with the increase of MPR capability. Therefore the MPR capability should be optimized to provide reasonable trade-off between the throughput per node and the cost per mobile node. The results of this study may be used for a system design of an infrastructure-less contention-based multiple access schemes with MPR capability.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.001 |
| 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".