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Record W2136491700 · doi:10.1109/iscas.2006.1693156

Per-Survivor Processing Viterbi Decoder for Bluetooth Applications

2006· article· en· W2136491700 on OpenAlex
S. Au, Shahriar Mirabbasi, 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
KeywordsViterbi decoderComputer scienceViterbi algorithmSoft-decision decoderBluetoothField-programmable gate arrayTrellis (graph)Computer hardwareSoft output Viterbi algorithmSoftwareBit error rateDecoding methodsReal-time computingAlgorithmWirelessSequential decodingTelecommunications

Abstract

fetched live from OpenAlex

Recently, a new noncoherent sequence detection receiver for Bluetooth systems has been developed. The receiver is based on Rimoldi's decomposition of the Bluetooth transmit signal and its power efficiency was shown by software simulations. In this paper, a hardware implementation of the decoder for this receiver is presented. In particular, we describe a low-complexity Viterbi decoder that performs noncoherent sequence detection in a two-state trellis using per-survivor processing. The prototype decoder is implemented in a field programmable gate array (FPGA). The bit-error-rate performance of the implemented decoder is compared with that of the reference software simulations.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.591
Threshold uncertainty score0.396

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 communication0.0000.000
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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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