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A Reconfigurable Access Scheme for Critical mMTC Networks with Unknown Event Occurrence

2023· article· en· W4387870335 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
FieldComputer Science
TopicAge of Information Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceNetwork packetComputer networkScheduling (production processes)Random accessReal-time computingScheme (mathematics)Reliability (semiconductor)ALARMGreedy algorithmDistributed computingAlgorithmEngineering

Abstract

fetched live from OpenAlex

This paper presents a reconfigurable access scheme for critical massive machine-type communication (mMTC) networks with mobile devices, where in-coverage devices directly connect to the access point (AP) and out-of-coverage devices transmit to the AP by two-hop relaying. Given that devices have regular and event-based alarm traffic, we combine grantfree (GF) and grant-based transmissions to acquire unknown event information and ensure high reliability to guarantee alarm packet delay constraints. Then, the average age of information (AoI) is minimized by maximizing AoI-weighted regular packet throughput. Simulation results show that the proposed algorithm outperforms random and greedy scheduling in terms of minimizing AoI and guarantees the delay constraints in contrast to the baselines without delay consideration or only using GF transmissions to obtain event information. Also, the proposed algorithm can adaptively distribute resources to serve alarm and regular packets based on the stringency of delay constraints.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.556
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.032
GPT teacher head0.319
Teacher spread0.287 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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