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

2022· article· en· W4312831190 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

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0030.001
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 head0.021
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