Low Complexity Phase-Unaware Detectors Based on Estimator-Correlator Concept
Bibliographic record
Abstract
Our goal is to present an overview of a class of low complexity detectors working in linear fading multipath channels. In addition, we present briefly a unified theory based on the optimal maximum a posteriori probability (MAP) receiver concept (Woodward & Davies, 1952), which in additive Gaussian noise leads to the estimator-correlator receiver (Price, 1956; Middleton, 1957; Kailath, 1960; Kailath, 1969). The terms receiver and detector are interchangeable. Detectors are estimators where the parameter or symbol set to be estimated is discrete (Kay, 1993; Kay, 1998). We consider phase-unaware detectors (PUDs) such as differentially coherent detector (DD), noncoherent detector (ND), and energy detector (ED). The term PUD is used to emphasize that the receiver does not have any knowledge of the absolute phase of the received signal although it may have some knowledge of the internal phase structure. We use the term noncoherent to represent a special case of PUD system, and this will be clarified later. PUD detectors are more robust than coherent detectors in a fading multipath channel since the carrier phase of a signal with a wide bandwidth or high carrier frequency may be difficult to estimate with a low complexity. Earlier extensive reviews include (Schwarz et al., 1966; Van Trees, 1971) and more recently (Garth & Poor, 1994; McDonough & Whalen, 1995; Proakis, 2001; Mammela et al., 2002; Simon & Alouini, 2005; Witrisal et al., 2009). A summary of the estimator-correlator receiver is presented in (Kay, 1998). Unless stated otherwise, we exclude equalizers which increase the complexity of the receiver significantly (Lodge & Moher, 1990; Colavolpe & Raheli, 1999). Thus we avoid intersymbol interference (ISI) by signal design and concentrate on the reception of a single symbol, which may include several bits in 警-ary communications. It is, however, conceptually straightforward to generalize the single symbol or “one-shot”detectors to symbol sequence detection by replacing the symbols by symbol sequences. The noise is assumed to be additive white Gaussian noise (AWGN). The frequency offset caused by the channel is assumed to be known and compensated. We also assume that the receiver is synchronous in the sense that the start of each symbol interval is known. Estimation of frequency and timing is a highly nonlinear problem, which is studied in (Mengali & D’Andrea, 1997; Meyr et al., 1998), see also (Turin, 1980). Also because of complexity reasons in general we exclude coherent detectors which are such that they assume that the alternative received
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".