Narrowband Data Transmission in TV White Space: An Experimental Performance Analysis
Bibliographic record
Abstract
TV White Spaces (TVWS) are UHF or VHF TV channels that are not under use by TV broadcasters at a particular time. As a means of achieving the global objective of efficient utilization of expensive spectrum bandwidth, TVWS can be exploited by Cognitive Radio (CR) enabled Software Defined Radio (SDR) systems for alternative services like communications for disaster-time rescue teams, Internet service for hard-to-reach communities etc. In this contribution, we report the design and prototype development of a testbed for real-time testing of secondary user transmission in TVWS. Once an unused TV channel has been identified, our system uses t hat idle channel for transmitting and receiving a narrowband signal. The testbed is built on Universal Software Radio Peripheral (USRP) 2901 device powered by GNU Radio software, RTL SDR 2832U and Spectrum Analyzer Tektronix MDO4054-3. Using 25kHz narrow bands within the TV channel#23 in Windsor, ON, Canada, and considering text, voice, and image data, we show channel and transmission characteristics through measured Bit Error Rate (BER) and constellation diagrams. Based on the experimental results, we affirm that it is possible to use this approach to successfully implement narrowband dynamic spectrum access (DSA) by secondary users over an idle TV channel.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".