Analysis of wideband analog despreading CDMA receiver based on 5-port downconversion device
Bibliographic record
Abstract
The fundamental part of every SS receiver is the signal despreader, or the pseudo-noise (PN) code correlator. It is used for signal demodulation, channel estimation, PN code acquisition and tracking, as well as in Rake receivers. We have examined how each of these elements of the receiver are affected by the analog despreading. We have shown that the non matched filter PN code correlation, which is easier to implement, results in a relatively small loss. In the analysis of the PN code tracking we have proposed a new analog noncoherent solution that has low complexity. We have also proposed and analyzed several structures of the temporal (1D) Rake and space-time (2D) Rake receivers. High data rates, low power consumption and low complexity will be the most important parameters in the design of the next generation mobile terminals. In this thesis we are introducing a new paradigm in the design of a direct sequence (DS) spread spectrum (SS) receiver by combining analog and digital signal processing. The main difference with respect to the conventional all-digital SS receiver design approach is that the proposed mixed analog/digital processing results in a symbol rate sampling rather than the high rate sub-chip sampling. The analog signal despreading is a key part of the proposed receiver solution. The potential advantages of analog despreading are reduced power consumption and low receiver complexity. Also, the proposed receiver solution allows very high chip rates (very high signal bandwidth). The solution is based on a five port device, a passive RF square law type of device. It is used to perform two important tasks at the same time, namely, the direct conversion and the analog despreading.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".