Radio Over IP Voice and Signalling Characterization Through System-of-Systems Radio Over IP Solution Deployment
Bibliographic record
Abstract
The focus of the thesis is on the characterizing Radio over IP voice and signalling delay, availability and voice quality by deploying Radio over IP (RoIP) System-of-Systems solution over large distances.System-of-Systems RoIP solution interfaces various mobile radio systems over multiple third party Wide Area Networks (WANs) based on Multiprotocol Label Switching Internet Protocol (MPLS IP) links to enable different departments such as Police, Military, Fire, Ambulance, Oil, Correctional Services, Forestry, Mining and etc. to communicate using their existing mobile radios.This thesis focuses on 4 major parts.The first part identifies various mobile radio systems and interfaces to extract and convert radio signaling and voice into the IP packets.The second, researches various interoperability management systems (servers/applications) to interconnect various mobile radio clients (agencies and departments) through the voice patches/conferences.The third focuses on the RoIP System-of-Systems solution deployment, testing under real-world condition using both unicast and multicast enabled networks, developing baseline characteristics such as Codecs, Delay, Jitter, Packet Loss, for secure and reliable communication over the third party WAN (MPLS IP backbone).The fourth focuses on RoIP System-of-Systems architectures and their performances when deployed over large distances and multiple third party WAN networks.The fourth primarily focuses on the systems latency characterisation, availability and voice quality.This thesis is the first one to examine radio over IP interoperability into a great level of details and provide a good foundation in understanding where interoperability would work and where not by characterizing major delay contributors in the overall System-of-Systems RoIP solution, from technology perspective.In this thesis RoIP communication over large distances has achieved 99.99%-99.999%availability and audio quality was found to be loud and clear 5/5 (signal strength 5/clarity 5).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".