A generic cognitive radio based on commodity hardware
Bibliographic record
Abstract
In this paper we describe the process that we undertook to build a configurable wireless platform that can be used to implement cognitive radio network (CRN) architectures. Consisting of a commodity IEEE 802.11 a/b/g (WiFi) router at the physical (PHY) layer and RF signal processing and IP traffic shaping circuitry, the resultant hybrid terminal (called the WiFi CR) becomes a building block that can implement cognitive femtocells, point to multipoint, mesh, and relay wireless networks. The IP addressable WiFi CR terminals can sense their radio environment, schedule IEEE 802.11 packet transmissions in space and time, and select channel, modulation rates, and transmit power. Intelligent operation is undertaken by a cognitive network management system (CR NMS) which controls a number of WiFi CR terminals and solicits sensor information from them. The CR NMS gathers the spectrum and sensed interference information and builds a memory map with such knowledge, thereby creating radio environment awareness for the CRN. Cognitive engines (the intelligent control algorithms) within the CR NMS use radio environment knowledge and other information (such as spectrum policy) to give the system a capability to seek white space spectrum, avoid interference, identify primary users, and take on other tasks associated with cognitive radio (CR). Designed around ISM band operation, this generic terminal, with proper RF modifications, can work in the TV bands, 3.65-3.70 GHz, and up to 60 GHz.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".