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Record W2095615832 · doi:10.1109/milcom.2010.5680115

An enhanced data rate chaos-based multilevel transceiver design exploiting ergodicity

2010· article· en· W2095615832 on OpenAlexaff
Deyasini Majumdar, Robin Moritz, Henry Leung, J. Maundy Brent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicChaos control and synchronization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTransceiverComputer scienceChaoticQuadrature amplitude modulationField-programmable gate arrayElectronic engineeringQAMChannel (broadcasting)Computer networkBit error rateWirelessComputer hardwareTelecommunicationsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Conventionally, practical chaos-based communication transceivers have failed to offer supportable data rates of the order of Mbps. The actual feasibility of such a high data rate chaos-based transceiver has been successfully addressed by exploiting the basics of Ergodic Theory. The proposed implementation is based on the Ergodic Chaotic Parameter Modulation (ECPM) scheme. However, the designed transceiver could only support a data rate of about 1–2 Mbps. Therefore, in order to address real-time applications with data rate requirements of the order of tens of Mbps, the supportable data rate of the practical transceiver needed enhancement. The issue of further increasing the data rate has been addressed by using multilevel Quadrature Amplitude Modulation (QAM) together with the basics of ECPM. Although, fundamentals of theoretical evaluation proposed the possibility of a M-level QAM-ECPM transceiver, the actual feasibility of such a transceiver still remained ambiguous. A primary factor contributing to this lack of feasibility is the need for practically viable solutions of suitable chaotic maps that can support multilevel schemes. This paper presents the design and implementation of a practical multilevel ECPM transceiver with data rate of the order of 48 Mbps. Theoretical as well as practically achievable data rates have also been estimated. Design of suitable chaotic maps required to make the proposed transceiver a reality has also been addressed. Power efficiency and resource usage of the designed prototype has been evaluated based on both Altera Stratix FPGA implementation as well as silicon.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.748
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.285
Teacher spread0.243 · 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 teacher head, not a consensus.

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

Citations5
Published2010
Admission routes1
Has abstractyes

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