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Low-Complexity Slot-Based Bit Loading for Multicarrier Wireless Systems

2018· article· en· W2883845218 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsBit error rateComputer scienceThroughputFadingSignal-to-noise ratio (imaging)Bit (key)Computational complexity theoryAlgorithmReduction (mathematics)WirelessOrthogonal frequency-division multiplexingElectronic engineeringReal-time computingComputer networkChannel (broadcasting)MathematicsTelecommunicationsDecoding methodsEngineering

Abstract

fetched live from OpenAlex

In this paper, a low-complexity discrete adaptive bit loading algorithm is proposed for multicarrier systems with uniform power allocation operating in fading environments under the discontinuous bit rates assumption. The algorithm objective is to maximize the overall throughput of the system while guaranteeing that the average bit error rate (BER) remains below a prescribed threshold. The algorithm uses a signal-to-noise ratio (SNR) threshold to group adjacent subcarriers into slots where it allocates the same number of bits for all subcarriers within the slot. The grouping mechanism reduces the complexity of the bit-loading process with negligible throughput degradation as compared to optimal and near-optimal algorithms. In particular scenarios, the achieved complexity reduction may exceed 70% with throughput penalty that is less than 1% as compared to other well established bit loading algorithms.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.743

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)0.0000.000

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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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