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Record W2108699110 · doi:10.1109/mwsym.2003.1212551

Software defined radio receiver based on Six-Port technology

2003· article· en· W2108699110 on OpenAlexaff
Xinyu Xu, Ke Wu, R.G. Bosisio

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSoftware-defined radioComputer scienceDigital signal processingWirelessField-programmable gate arrayDigital radioFlexibility (engineering)Embedded systemSoftwareComputer hardwareTelecommunications

Abstract

fetched live from OpenAlex

Software Defined Radio (SDR) has been identified as one potential method to enhance the flexibility of wireless communication systems. In the past, the operating speed limitation of analog digital converter (ADC) and processing ability limitation of re-configurable chips for signal processing have slowed down the development of SDR for useful commercial application. With recent advances in the semi-conductor processing technology and the development of re-configurable devices such as digital signal processors (DSP) and field programmable gate arrays (FPGA), SDR has now become practical for use in system solutions including wireless LANs, audio and television broadcasting and interoperability between different radio services. In this paper, we describe the application of SDR based on Six-Port technology to provide multi-channel, multi-mode wireless digital receiver. The combination of SDR and Six-Port technology provides a great flexibility in system configuration, a significant reduction in system development cost, and also a high potential for software reuse.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2003
Admission routes1
Has abstractyes

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