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Record W2140263437 · doi:10.1109/glocom.2004.1379038

Sequence detection for bluetooth systems

2005· article· en· W2140263437 on OpenAlex
Manish Jain, Lutz Lampe, Robert Schober

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBluetooth and Wireless Communication Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBluetoothComputer scienceDiscriminatorDetectorTrellis (graph)Sequence (biology)Channel (broadcasting)AlgorithmFilter (signal processing)Real-time computingElectronic engineeringWirelessTelecommunicationsEngineeringDecoding methods

Abstract

fetched live from OpenAlex

The design of power efficient receivers for Bluetooth systems is a challenging task due to stringent complexity constraints. We tackle this problem and present a novel receiver consisting of a single filter and a subsequent sequence detector. This receiver outperforms the conventional discriminator detector by more than 4 dB for typical Bluetooth channels. Thereby, the proposed sequence detection (SD) algorithm is of favorable low complexity, as it operates on a two-state trellis, and is highly robust against channel phase variations caused by low-cost local oscillators. Different from previously published work on SD, we take possible variations of the Bluetooth modulation parameters into account. Hence, the presented receiver design is an attractive solution for practical Bluetooth devices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.273
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it