Throughput and Energy Efficiency in Wireless Ad Hoc Networks with Gaussian Channels
Bibliographic record
Abstract
This paper studies the problem of topology control in random wireless ad hoc networks through power assignment for n nodes uniformly distributed in a unit square. We require that the network is strongly connected and look to maximize the minimum throughput (or capacity) link in the case that all the nodes transmit simultaneously. According to the Gaussian channel model, the throughput of a wireless link (u, v) is B log(1 + S/N) bps, where B is the channel bandwidth and S/N is the channel bandwidth. We distinguish between two types of power assignments: homogeneous (all nodes have the same power level) and heterogeneous (nodes may have different power levels) cases. For the homogeneous case we give lower and upper bounds on the minimum capacity link. In the heterogeneous case we develop an energy efficient power assignment algorithm which achieves a minimum throughput of Ω(B log(1 + 1/√n log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n)) and also discuss how to implement this algorithm in a distributed fashion. Finally, we present some simulation results. To the best of our knowledge, these are the first provable bounds for capacity in wireless networks, when nodes are allowed to transmit simultaneously.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".