Concatenated fibre-wireless channel identification in a multiuser CDMA environment
Bibliographic record
Abstract
Radio-over-fibre (ROF) has received increasing attention for its ability to enable broadband wireless access. This fibre-based wireless access scheme meets the demand for broadband service by integrating the high capacity of optical networks with the flexibility of radio networks (the optical and wireless channels are concatenated with one another). There are, however, impairments that come with this appealing technology. The nonlinear distortion of the optical link and the multipath dispersion of the wireless channel are two of the major factors. In order to limit the effects of these distortions, estimation, and subsequently equalisation, of the concatenated fibre-wireless channel needs to be done. An estimation algorithm for the fibre-wireless uplink in a multiuser code division multiple access (CDMA) environment is presented using pseudonoise training sequences. It has already been shown by Fernando et al. (2001) that identification of the fibre-wireless uplink is possible in a single user CDMA environment. However, the more difficult task of identification in a multiuser spread spectrum environment, which is more realistic, is shown. In the multiuser case, the cumulative effect of multiuser interference, multipath dispersion, nonlinear distortion and noise should all be handled together which makes it more challenging. Numerical evaluations of the developed algorithm show that a good estimation of both the linear and nonlinear systems is possible in the presence of 16 independent users and an signal-to-noise ratio (SNR) of 22 dB. The estimation accuracy increases with the length of the PN sequence.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".