Large Channel Model for Multi-Task Wireless Communication
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Solution Overview
Problem
Current AI-based solutions for wireless communication tasks are inefficient due to the use of task-specific neural networks that require separate modules for each task, leading to low training efficiency and hardware utilization inefficiencies.
Innovation Solution
A large channel model trained on task-independent data that outputs a task-independent metric, combined with task-dependent metrics at the base station to solve multiple wireless communication tasks, such as channel estimation and prediction, using a single AI model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If task-specific neural networks are used for each wireless communication task, then each task can be solved with dedicated optimization, but the system complexity increases and hardware utilization efficiency decreases
Solution Approach 1:
The patent applies universality by designing a single large-scale neural network model that can perform multiple wireless communication tasks (channel estimation, channel prediction, interference cancellation, etc.) rather than using separate task-specific networks. This universal model is trained on task-independent channel data and can be flexibly applied to different tasks, reducing system complexity while maintaining performance through shared feature extraction and processing pathways.
Solution Approach 2:
The patent merges multiple task-specific neural networks into a single unified large-scale model. By combining the functionalities of separate networks into one integrated system, the patent reduces hardware requirements, simplifies system architecture, and improves resource utilization while still addressing various wireless communication tasks through the same model infrastructure.
2Power
If multiple task-specific neural networks are deployed, then each task receives dedicated processing power, but hardware utilization efficiency becomes low
Solution Approach 1:
The universal neural network model serves multiple wireless communication tasks simultaneously, allowing the same hardware infrastructure to be utilized for different functions. This multi-functionality ensures that processing power is not wasted on idle dedicated networks while still providing adequate computational resources for each task through shared processing pathways.
Solution Approach 2:
The patent enables dynamic task assignment and processing to the single large-scale model, allowing the system to adaptively allocate computational resources based on current task requirements. This dynamic approach improves hardware utilization efficiency by ensuring the processing power is actively used for relevant tasks rather than being statically allocated to potentially idle dedicated networks.
3Measurement precision
If task-specific neural networks are used, then training can be optimized for each specific task, but training data complexity increases
Solution Approach 1:
The patent uses a universal training approach with task-independent channel data that can be applied across multiple wireless communication tasks. By training a single model on general channel characteristics rather than task-specific data, the patent reduces training data complexity while maintaining the ability to perform various tasks through the shared learned representations and features.
Data Source
AI summary
Apparatuses and methods of solving wireless communication tasks. A method includes receiving, at an electronic device, a wireless channel model trained based on task-independent data; obtaining task-dependent data; determining, based on the wireless channel model and the task-dependent data, a task-independent metric and a task-dependent metric; and combining the task-independent metric and the task-dependent metric to solve wireless communication tasks.