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 log2n)) 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 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".