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Record W3083445715 · doi:10.1109/lcomm.2020.3021976

Channel Coding Rate for Finite Blocklength Faster-Than-Nyquist Signaling

2020· article· en· W3083445715 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Communications Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNyquist–Shannon sampling theoremComputer scienceCode rateTransmission (telecommunications)Bandwidth (computing)Orthogonal frequency-division multiplexingChannel (broadcasting)Nyquist rateAlgorithmTopology (electrical circuits)MathematicsElectronic engineeringTelecommunicationsDecoding methodsSampling (signal processing)Combinatorics

Abstract

fetched live from OpenAlex

The fundamental tradeoff between low latency and high reliability makes the design of ultra-reliable low-latency communications (URLLC) wireless systems challenging. To support URLLC for a fixed bandwidth, faster-than-Nyquist (FTN) signaling is a promising approach since it increases the degrees of freedom (i.e., channel uses) per time interval, which can be exploited to improve reliability. In this letter, we derive analytical expressions for the approximate maximum channel coding rate (MCCR) for finite blocklength FTN signaling for water-filling and equal power allocations. We show that for practical non-sinc square-root Nyquist pulses, the penalty on the rate incurred due to the finite blocklength can be significantly reduced by non-orthogonal FTN transmission. Our results reveal that the MCCR for finite blocklength FTN signaling exceeds the Shannon capacity achieved for infinite blocklength and orthogonal transmission.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.888

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 head0.064
GPT teacher head0.264
Teacher spread0.200 · 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