Bibliographic record
Abstract
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 paper we are introducing a new paradigm in the design of direct sequence spread spectrum receiver by combining analog and digital signal processing. The main difference with respect to the conventional all-digital receiver design approach is that the proposed mixed analog/digital processing results in a symbol rate sampling rather than the high-rate subchip sampling. Analog signal despreading is the key part of the proposed receiver solution, which is based on a five-port device, a passive RF square-law-type device. It is used to perform two important tasks at the same time, namely, the direct conversion and analog despreading. To achieve lower complexity, the proposed receiver uses rectangular instead of pulse-matched despreading at the cost of only a small performance degradation. Also, we propose a new noncoherent pseudonoise (PN) code tracking scheme based on error signal generated through the L1 norm. This results in comparable or even better PN code tracking performance than L2 norm circuitry, using less complex hardware. Further, we explore how this technology can be applied in the design of DS-CDMA RAKE receiver for mobile terminals. Depending on how the pilot signal is multiplexed, we propose two types of RAKE receivers. It is shown that under Rayleigh fading channel such receiver structures offer robustness and high performance, while maintaining the low complexity achievable through the five-port device.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".