Pure harmonic upconversion in radio-on-fiber systems
Bibliographic record
Abstract
The demand for residential broadband connectivity is growing world-wide, spurring on research efforts to solve the 'last mile' problem. Radio-on-fiber (ROF) based networks are an attractive solution as they offer significant infrastructure cost savings and economically leverage existing optical infrastructure. This network topology would necessarily operate in the 30GHz, LMCS band (Local Multi-User Communication system) by virtue of its broadband requirements. Such high operating frequencies and bandwidths demand expensive RF hardware. Pure harmonic upconversion of the radio subcarrier is an attractive method of reducing the frequency requirements of the system oscillator, mixer, and optical modulator with minimal complexity, thus reducing infrastructure costs. Unfortunately, severe phase and amplitude distortion results from the harmonic upconversion process and is further compounded by chromatic dispersion. This paper characterizes these distortions, and introduces a mitigating phase and amplitude predistortion scheme for harmonic upconversion with chromatic dispersion in Mach-Zehnder/direct detection based optical links. As a proof of concept, a prototype Mach-Zehnder based, 3rd order harmonically upconverted 10.41GHz, 16QAM subcarrier radio-on-fiber system is described, and preliminary experimental results presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".