MUD receiver capacity optimization for wireless ad hoc networks
Bibliographic record
Abstract
In general the performance of ad hoc networks is limited by mobility, half-duplex operation, and possible collisions. One way to overcome these bottlenecks is to apply a Multi-User Detection (MUD) reception that can significantly increase the network throughput and improve quality of service. Recent technological advances allow implementation of a multi-user detection receiver in one software-defined radio chip and thus making it feasible to consider this technology for ad hoc networks. Nevertheless, the MUD receiver power consumption and complexity grow exponentially with its capacity defined as the maximum number of CDMA signals, originated at different nodes, which can be received simultaneously. Therefore the receiver capacity should be optimized to provide reasonable trade-off between the network performance and the MUD receiver cost and power consumption. We address this issue by presenting an approximate analysis of the network throughput as a function of MUD receiver capacity and other network parameters such as node density and offered traffic. The numerical analysis illustrates the gains in network throughput and performance with increasing receiver capacity. Based on this analysis we propose a framework for MUD receiver capacity optimization based on performance and cost utility functions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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 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".