MétaCan
Menu
Back to cohort
Record W4415624250 · doi:10.1109/tnse.2025.3626216

Joint Uplink-Downlink Transmission Design and Full-Loop Control for Latency-Critical Cyber-Physical Systems

2025· article· W4415624250 on OpenAlexaff
Ling Lyu, Haitian Liu, Yanpeng Dai, Nan Cheng, Cailian Chen, Xinping Guan, Xuemin Shen

Bibliographic record

VenueIEEE Transactions on Network Science and Engineering · 2025
Typearticle
Language
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsTelecommunications linkTransmission (telecommunications)MulticastWirelessResource allocationInteger programmingScheme (mathematics)Wireless network

Abstract

fetched live from OpenAlex

With the significant advance of wireless communication technology, more networked control systems are looped via wireless networks. However, the dynamics and uncertainties of wireless channels as well as the limitation of radio resources make it challenging to close all loops at each control step. As the open-loop control will lead to performance deterioration, it is essential to jointly optimize the uplink and downlink transmissions for the full-loop control. In this paper, we analyze the impact of transmission delay in uplink and downlink on the full-loop control performance. We then propose a novel multicast transmission scheme for the latency-critical full-loop control. Accordingly, the uplink-downlink transmission and the full-loop control are jointly considered to minimize the control and communication cost. To effectively solve this mixed integer non-linear programming problem, the original problem is decomposed into the uplink transmission problem and the downlink transmission problem. Alternate resource optimization algorithm and multicast resource allocation algorithm are designed. Simulation results show that the proposed scheme has advantage on reducing both the communication and control cost.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.014
GPT teacher head0.234
Teacher spread0.219 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Network Science and EngineeringSame topicStability and Control of Uncertain SystemsFrench-language works237,207