MétaCan
Menu
Back to cohort

A Reconfigurable Access Scheme for Critical Massive MTC Networks With Device Clusters

2022· article· en· W4312831190 on OpenAlexaff
Xianyi Zhan, Duc Tuong Nguyen, Tho Le‐Ngoc

Bibliographic record

Venue2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · 2022
Typearticle
Languageen
FieldComputer Science
TopicAge of Information Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceNetwork packetScheduling (production processes)Computer networkRandom accessRelayBandwidth (computing)Scheme (mathematics)ThroughputALARMDistributed computingReal-time computingWirelessMathematical optimizationTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a reconfigurable access scheme for critical massive machine-type communication networks where the access point (AP) is equipped with a large-scale antenna array, and devices can move and form clusters. Devices transmit alarm and regular packets to the AP by two-hop relay connections via cluster leaders. The delay constraints of alarm packets are guaranteed by considering jointly the effective bandwidth and effective capacity with the success probability threshold while the aggregate age of information (AoI) of regular packets is minimized by maximizing their AoI-weighted throughput. Simulation results show that the proposed algorithm outperforms random and round-robin scheduling in terms of minimizing AoI, and it guarantees the delay constraints in contrast to the baselines without delay consideration. Moreover, the proposed algorithm can dynamically distribute resources to serve alarm and regular packet transmissions 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.299
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venue2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)Same topicAge of Information OptimizationFrench-language works237,207