Capacity of multi-hop wireless network with frequency agile software defined radio
Bibliographic record
Abstract
With the advance of both hardware and software technologies, the concept of software defined radio (SDR) is becoming more and more popular in the academic and industrial communities. Its popularity has been increased by the recent intensive research of cognitive radio technology, which is built on top of SDR. One of key features of SDR is its capability of frequency agility, which means a single SDR can access multiple channels, subject to a certain total frequency bandwidth (channel span) constraint. Compared with the traditional multiple-radio solutions, the SDR setup has the advantages of higher flexibility and reduced hardware size (cost). In this paper, we investigate the achievable capacity of a wireless network with single-SDR equipped transceivers, especially a multiple-hop network, for any given network flows. We propose new approaches to formulate the single-SDR constraint, which is unique for the derivation of capacity upper bounds. We also propose a heuristic algorithm to obtain a lower bound for the capacity. Both numerical and simulation results are presented to demonstrate the potential capacity for an SDR network and compared it with a multipleradio network.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".