Wireless Intelligent Decision-Making Communication System
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Solution Overview
Problem
Existing wireless communication systems struggle to intelligently switch network resources when application scenarios of network nodes change, leading to limited network capacity and throughput that cannot meet user requirements.
Innovation Solution
A method, apparatus, and system for wireless intelligent decision-making communication that involves obtaining data information from network nodes, predicting application scenarios using convolutional long-short-term memory hybrid neural networks, and determining multi-domain resource combinations using reinforcement learning models to adapt resource activation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single network protocol is used for wireless communication, then the system is simple and easy to operate, but the network capacity and throughput are limited when application scenarios change
Solution Approach 1:
The patent implements dynamic protocol selection by enabling network nodes to automatically switch between different wireless communication protocols (such as LTE, Wi-Fi, 5G) based on real-time application scenario recognition. The system dynamically adjusts the active protocol suite to match current communication needs, transforming a static single-protocol system into a dynamic multi-protocol system that adapts to changing conditions.
Solution Approach 2:
The system changes the operational parameters of network nodes by modifying which protocol suite is active based on recognized application scenarios. When the scenario changes (e.g., from stationary to mobile, from low-data to high-data transmission), the system alters the protocol configuration parameters, enabling optimal performance for each specific scenario while maintaining operational simplicity through automated decision-making.
2Productivity
If network resources are not intelligently switched, then the system is stable and reliable, but network capacity and throughput cannot meet user requirements when scenarios change
Solution Approach 1:
The patent implements self-service by enabling network nodes to autonomously recognize application scenarios and select appropriate protocols without external intervention. The system uses embedded scenario recognition algorithms that automatically analyze current communication conditions and trigger protocol switching decisions, making the network self-adaptive and eliminating the need for manual configuration or centralized control.
Solution Approach 2:
The system incorporates feedback mechanisms where network performance metrics and scenario characteristics are continuously monitored, fed back to the protocol selection module, and used to adjust the active protocol suite. This closed-loop control ensures that the network maintains optimal capacity and throughput by responding to changing conditions based on real-time feedback from the environment and performance data.
3Adaptability or versatility
If multiple network protocols are supported for different scenarios, then network capacity and throughput improve, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary scenario recognition module that acts as a mediator between the physical communication environment and the multiple supported protocols. This intermediary automatically translates complex environmental conditions into simplified scenario classifications, which then map to appropriate protocol suites. This abstraction layer shields users from the complexity of managing multiple protocols while maintaining full adaptability across different scenarios.
Data Source
AI summary
Embodiments of the present application provide a method, an apparatus and a system for wireless intelligent decision-making communication, where the method includes: obtaining data information of a network node; where the data information includes scheduling request information, resource pre-occupancy indication information, network status information, wireless channel information, and communication capability information of the network node; predicting an application scenario of the network node at a preset time according to the data information; determining a decision result containing a multi-domain combination according to the application scenario of the network node at the preset time and the communication capability information of the network node, and activating the multi-domain combination of the network node at the preset time; where the multi-domain combination is corresponding to the application scenario of the network node.