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Record W2164811834 · doi:10.1155/s1110865704312229

Signal Reception for Space-Time Differentially Encoded Transmissions over FIR Rich Multipath Channels

2004· article· en· W2164811834 on OpenAlexaff
Zhan Zhang, Jacek Ilow

Bibliographic record

VenueEURASIP Journal on Advances in Signal Processing · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMultipath propagationComputer scienceIntersymbol interferenceFadingMIMOElectronic engineeringAntenna diversityAlgorithmChannel (broadcasting)TelecommunicationsAntenna (radio)Engineering

Abstract

fetched live from OpenAlex

With sophisticated signal and information processing algorithms, air interfaces with space-time (ST) coding and multiple reception antennas substantially improve the reliability of wireless links. This paper proposes a new receiver algorithm for differential ST coded transmissions over the finite-impulse-response (FIR) rich multipath fading channels. The symbol detection introduced in this paper is a deterministic subspace-based approach in a multiple-input and multiple-output (MIMO) system framework. The receiver (i) operates in a blind fashion without estimating the channel or its inverse and (ii) is able to work with a small number of signal samples and hence can be applied in the quasistatic channels. The proposed scheme employs multiple antennas at both sides of the transceiver and exploits both the antenna diversity and the multiple constant modulus (MCM) characteristics of the signaling. The receiver is able to blindly mitigate the intersymbol interference (ISI) in a rich multipath propagation environment, and this has been verified through the extensive Monte Carlo 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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.288
Teacher spread0.274 · 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 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

Citations0
Published2004
Admission routes1
Has abstractyes

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