A VLC-RF network resource matching method based on martingale theory and reinforcement learning

By employing martingale theory and reinforcement learning methods, a handover service mechanism for VLC-RF heterogeneous networks is constructed. Multidimensional QoS indicators are derived and a user satisfaction utility function is established, which solves the problem of singular network performance evaluation in existing technologies and achieves optimal resource allocation and improved network service efficiency.

CN120090705BActive Publication Date: 2026-06-26JILIN INST OF CHEM TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
JILIN INST OF CHEM TECH
Filing Date
2025-02-28
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing VLC-RF network resource matching schemes only focus on system latency performance, and do not fully consider the nonlinear mapping relationship between user satisfaction and service quality parameters, resulting in a single network performance evaluation and insufficient guarantee of user QoS requirements.

Method used

We employ martingale theory and reinforcement learning to construct a VLC-RF heterogeneous network handover service mechanism. We describe state transitions using a Markov chain model, derive multidimensional QoS indicators, establish a user satisfaction utility function, and utilize reinforcement learning to obtain the optimal service rate combination.

Benefits of technology

It achieves improved network service efficiency and user satisfaction, and optimized resource allocation, while ensuring diverse QoS requirements of users.

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Abstract

The present application relates to a kind of VLC-RF network resource matching method based on martingale theory and reinforcement learning, the present application constructs visible light communication (Visible Light Communication, VLC)-radio frequency (Radio Frequency, RF) heterogeneous network switching service mechanism, deduces the state transition matrix of system service process.Based on martingale theory, network QoS parameters are evaluated, and queue reliability QoS, delay reliability QoS and jitter reliability QoS are combined to construct user satisfaction utility function.Finally, the service rate combination required by the system network is obtained using reinforcement learning to achieve optimal allocation of resources and improve network service efficiency.
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