PDCCH Parameter Prediction for Low-Power Terminal Detection
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Solution Overview
Problem
Existing wireless communication systems face high power consumption due to the need for terminals to blindly detect physical downlink control channels (PDCCH) without knowing the aggregation degree and transmission location used by the base station, leading to inefficient power usage.
Innovation Solution
Implementing a channel parameter determination method using machine learning to predict the aggregation degree and transmission location of PDCCH through a channel parameter model, such as regression, Kalman filter, or BP network, based on service type, terminal conditions, and other parameters, guiding terminal detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If terminals perform blind detection of PDCCH without knowing aggregation degree and transmission location, then the system maintains flexibility in resource allocation, but terminal power consumption increases significantly
Solution Approach 1:
The network device performs preliminary action by determining the aggregation degree and transmission location of PDCCH in advance, then notifying the terminal of these parameters. This allows the terminal to perform targeted detection rather than blind detection across all possible locations and aggregation degrees, significantly reducing power consumption while maintaining system flexibility
2Reliability
If terminals perform blind detection across all possible aggregation degrees and transmission locations, then detection reliability is maintained, but detection time and power consumption increase
Solution Approach 1:
The network device determines the actual aggregation degree and transmission location in advance and notifies the terminal before PDCCH transmission. This preliminary action enables the terminal to directly detect at the correct location with the correct aggregation degree, ensuring detection reliability while dramatically reducing the time required compared to exhaustive blind detection
Data Source
AI summary
A channel parameter determination method, apparatus and computer readable medium that improve efficiency of a network device in a communication network. The efficiency of the network device is improved by obtaining a first parameter; and, determining a second parameter of a channel by using at least one channel parameter model based on the first parameter.


