Estimation and equalization of fiber-wireless uplink for multiuser CDMA 4G networks
Bibliographic record
Abstract
Fiber-wireless (Fi-Wi) access fronts can support 100s of Mb/s envisioned by 4G networks. However, a major issue associated with Fi-Wi links is the nonlinear distortion of the radio-over-fiber (ROF) link coupled with the multipath dispersion of the wireless channel. Estimation and subsequent equalization of the concatenated fiber-wireless channel needs to be done, especially at high bit rates. The uplink is severely affected due to large fluctuations in the radio signal. This paper proposes an estimation and subsequent equalization algorithm for the Fi-Wi CDMA uplink. The estimation employs the properties of pseudo noise (PN) sequences and the equalization uses a novel Hammerstein type decision feedback equalizer (HDFE). The estimation and equalization are performed in the presence of multiple access interference (MAI) and wireless and optical channel noise. The cumulative effects of multiuser interference, multipath dispersion, nonlinear distortion, and noise are all considered in our analysis. Correlation properties of white-noise like PN sequences enable decoupling of the linear (wireless) and nonlinear (optical) channel portions. Furthermore, we propose a unique algorithm to mitigate MAI. Numerical evaluations show a good estimation and equalization of both the linear and nonlinear channels. Bit error rate (BER) simulations show that this algorithm leaves only small residual MAI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".