Equalization of Multiuser Wireless CDMA Downlink Considering Transmitter Nonlinearity Using Walsh Codes
Bibliographic record
Abstract
<p>Transmitter nonlinearity has been a major issue in many scenarios: cellular wireless systems have high power RF amplifier (HPA) nonlinearity at the base station; satellite downlinks have nonlinear TWT amplifiers in the satellite transponder and multipath conditions in the ground station; and radio-over-fiber (ROF) systems consist of a nonlinear optical link followed by a wireless channel. In many cases, the nonlinearity is simply ignored if there is no out-of-band emission. This results in poor BER performance. In this paper we propose a new technique to estimate the linear part of the wireless downlink in the presence of a nonlinearity using Walsh codes; Walsh codes are commonly used in CDMA downlinks. Furthermore, we show that equalizer performance is significantly improved by taking into account the presence of the nonlinearity during channel estimation. This is shown by using a regular decision feedback equalizer (DFE) with both wireless and RF amplifier noise. We perform estimation in a multiuser CDMA communication system where all users transmit their signal simultaneously. Correlation analysis is applied to identify the channel impulse response (CIR) and the derivation of key correlation relationships is shown. A difficulty with using Walsh codes in terms of their correlations (compared to PN sequences) is then presented, as well as a discussion on how to overcome it. Numerical evaluations show a good estimation of the linear system with 54 users in the downlink and a signal-to-noise ratio (SNR) of 25 dB. Bit error rate ( BER) simulations of the proposed identification and equalization algorithms show a BER of 10(-6) achieved at an SNR of similar to 25 dB.</p> <p> </p>
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.007 |
| 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".