Downlink Precoding Method for Base Station Scheduling
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Solution Overview
Problem
In wireless communications, the base station's downlink scheduling accuracy is compromised due to the UE's inability to accurately estimate Inter-Cell Interference (ICI) from other cells, leading to suboptimal modulation and coding schemes and reduced throughput and spectrum utilization.
Innovation Solution
A downlink precoding method is introduced, where a first precoding mode is configured for each cell with a precoding matrix for candidate users, selecting a scheduling user and performing precoding on channel estimation and data signals, which reduces the need for UE feedback and ensures consistent ICI estimation, thereby improving scheduling accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the base station uses conventional downlink scheduling without precoding, then the system is simpler to implement, but the downlink scheduling accuracy deteriorates due to UE's inability to accurately estimate Inter-Cell Interference
Solution Approach 1:
The base station performs preliminary precoding on channel estimation signals before transmission. By pre-processing the reference signals with the same precoding matrix that will be used for data transmission, the UE can accurately estimate the effective channel including precoding effects, thereby improving SINR estimation accuracy and enabling more precise downlink scheduling decisions.
Solution Approach 2:
The precoded channel estimation signal acts as an intermediary that carries information about the effective channel conditions. Instead of directly transmitting raw channel estimation signals, the base station uses precoded reference signals as a mediator to convey channel state information that already incorporates the effects of precoding, interference management, and resource allocation, enabling the UE to make more accurate quality assessments.
2Measurement precision
If the base station performs precoding on channel estimation signals and downlink data, then the CQI estimation accuracy and scheduling precision improve, but the processing complexity and computational overhead increase
Solution Approach 1:
The patent merges the precoding operation for channel estimation signals with the precoding operation for downlink data. By using the same precoding matrix for both reference signals and data transmissions, the system consolidates multiple processing functions into a unified framework, reducing the need for separate processing chains and minimizing additional computational overhead while maintaining consistent channel estimation accuracy.
3Productivity
If the base station uses conventional scheduling without precoding, then the computational resources are conserved, but the system throughput and user throughput are reduced due to suboptimal modulation and coding scheme selection
Solution Approach 1:
The patent changes the parameter of channel estimation by applying precoding transformations to reference signals. This parameter change in the estimation process enables more accurate SINR measurements, which directly improves the selection of modulation and coding schemes, thereby increasing system throughput and spectral efficiency while the computational energy cost is offset by the performance gains.
Data Source
AI summary
Disclosed are a downlink precoding method and a base station. The method includes: configuring a first precoding mode for a first cell, wherein the first precoding mode includes information of a precoding matrix configured for each candidate user of the first cell on each resource; selecting a scheduling user on each resource from candidate users according to the first precoding mode; and performing precoding to a channel estimation signal and downlink data of the scheduling user according to a precoding matrix of the scheduling user, and transmitting precoded channel estimation signal and precoded downlink data to the scheduling user. Such method and base station of the present disclosure can reduce the feedback overhead of the user, effectively improve the accuracy of channel quality indicator (CQI) estimation, and improve the system throughput and user throughput.