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Record W2106590895 · doi:10.1109/cjece.2007.4407666

Integer QP relaxation-based algorithms for intercarrier-interference reduction in OFDM systems

2007· article· en· W2106590895 on OpenAlexaffvenue
Y. H. Zhang, Wu-Sheng Lu, T. Aaron Gulliver

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingRelaxation (psychology)AlgorithmQAMOrthogonalityComputational complexity theoryReduction (mathematics)Quadrature amplitude modulationInterference (communication)Computer scienceFadingMathematicsMathematical optimizationChannel (broadcasting)TelecommunicationsBit error rateDecoding methods

Abstract

fetched live from OpenAlex

Orthogonal frequency-division multiplexing (OFDM) modulation can be utilized to deal with severe channel conditions without complex equalization. However, in a fast-fading channel, Doppler spread caused by user mobility destroys the orthogonality among subcarriers, prompting intercarrier interference (ICI). In this paper, the OFDM ICI reduction problem is formulated as a combinatorial optimization problem. Two relaxation methods are proposed to relax the maximum-likelihood detection problem into convex quadratic programming (QP) problems. To further reduce computational complexity, the QP problems are solved by limiting the search to the two-dimensional subspace. A low-bit descent search can also be employed to improve the system performance. The extension to higher-order quadrature amplitude modulation (QAM) OFDM systems is also addressed. Performance results are given which demonstrate that the integer QP relaxation-based algorithms provide excellent performance with reasonable computational complexity.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2007
Admission routes2
Has abstractyes

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