Recovery-Limiting Signal-Conditioned Action Decoding for Emergency Wireless Access–Backhaul Capacity Allocation

Post-disaster emergency wireless access–backhaul recovery requires limited capacity to be allocated across affected regions under nonnegativity and fixed-total-capacity constraints. We propose Recovery-Limiting Signal-Conditioned Action Decoding (RLS-CAD). Critical-region access outage, backhaul shortfall, traffic backlog, and end-to-end recovery deficit form regional score bases, while a 15-dimensional policy controls signal fusion, score correction, and finite-support simplex projection. Experiments use five training seeds and 100 shared test scenarios and retain both training and scenario variability. RLS-CAD outperforms Graph-PPO and both information-matched controls on FinalCap, CTI, and TaskUtility. The evaluation concerns normalized planning-level simulation; physical-layer scheduling is outside its scope.

Authors

Institutions

Publication Details

Journal
Sensors
Published
2026-08-21
DOI
https://doi.org/10.3390/s26165317
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Recovery-Limiting Signal-Conditioned Action Decoding for Emergency Wireless Access–Backhaul Capacity Allocation

Minglei Han, Hongbin Ma, Youzhi Zhang, Jingxiang Ma et al.
Sensors
Software-Defined Networks and 5G
article

Recovery-Limiting Signal-Conditioned Action Decoding for Emergency Wireless Access–Backhaul Capacity Allocation

Minglei Han, Hongbin Ma, Youzhi Zhang, Jingxiang Ma, Ying Jin
article en

Abstract

Post-disaster emergency wireless access–backhaul recovery requires limited capacity to be allocated across affected regions under nonnegativity and fixed-total-capacity constraints. We propose Recovery-Limiting Signal-Conditioned Action Decoding (RLS-CAD). Critical-region access outage, backhaul shortfall, traffic backlog, and end-to-end recovery deficit form regional score bases, while a 15-dimensional policy controls signal fusion, score correction, and finite-support simplex projection. Experiments use five training seeds and 100 shared test scenarios and retain both training and scenario variability. RLS-CAD outperforms Graph-PPO and both information-matched controls on FinalCap, CTI, and TaskUtility. The evaluation concerns normalized planning-level simulation; physical-layer scheduling is outside its scope.

SensorsVol. 26(16)
Beijing Institute of Technology (CN), Chinese Academy of Sciences (CN), Chinese University of Hong Kong, Shenzhen (CN), Zhuhai Institute of Advanced Technology (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 7%
Software-Defined Networks and 5G
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.