Scarcity-Coefficient Gated Projection Reinforcement Learning for Planning-Layer Capacity Activation in Emergency Wireless Networks.

Jingxiang Ma, Ping Liu, Hongbin Ma, Guiping Lu, Youzhi Zhang

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In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

5 authors.

Jingxiang MaSchool of Automation, Beijing Institute of Technology, Beijing 100081, China.ORCID 0009-0009-2139-4102

Ping LiuSchool of Automation, Beijing Institute of Technology, Beijing 100081, China.

Hongbin MaSchool of Automation, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0002-5734-3157

Guiping LuMarine Science and Technology Domain, Beijing Institute of Technology, Zhuhai 519088, China.

Youzhi ZhangCentre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China.ORCID 0000-0002-2984-734X

Funding

National Natural Science Foundation of China 62473052State Key Laboratory of Catalysis 241-HF-D09-01

6 · The paper itself

Abstract

After infrastructure disruption, an emergency wireless controller must meet urgent communication demand and preserve resources for later periods. We propose Scarcity-Coefficient Gated Projection Reinforcement Learning (SCGP-RL). It jointly selects total planning-layer activation and a regional capacity upper-bound vector. Scarcity and urgent-demand evidence shape the activation intent. Scalar and capped-simplex projections enforce the coupled action constraints. A planning capacity unit (PCU) is defined as a calibratable service-capacity quantum. In the common constrained evaluation, SCGP-RL reduced the unmet urgent-demand score from 0.6643 for Projected CPO to 0.4919. It also satisfied all the executed hard constraints. Component tests show that the urgent-demand gate drives rapid service response and that the marginal demand-relief estimate provides a smaller benefit. Binding-condition tests show that the power, backhaul, and node-health mechanisms protect the resources they represent.

Indexed as

capacity safetyconstrained action decodingemergency wireless networksplanning-layer capacity activationregional upper-bound projectionscarcity-coefficient gated projection reinforcement learning

Identifiers

PMID42655556

DOI10.3390/s26165248

PMCPMC13517145

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