Beam Training With Forwarding Mode Selection for Fading Mitigation
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Solution Overview
Problem
In wireless communication systems with intelligent surfaces, frequency selective fading due to multipath environments affects signal quality despite beam direction determination, limiting effective communication rates for services like VR, AR, and video.
Innovation Solution
A beam training method involving measurement and reporting of reference signals by a terminal device to determine optimal forwarding modes for intelligent surfaces, using beam directions and phases to reduce frequency selective fading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beam scanning function is applied to intelligent surface, then beam direction can be determined, but frequency selective fading still occurs due to multipath environment
Solution Approach 1:
The patent applies dynamics by transitioning from static beam direction determination to dynamic beam phase adjustment. The intelligent surface device adjusts beam phases in real-time based on measured channel state information to compensate for multipath fading effects, making the system adaptive to changing channel conditions rather than relying solely on fixed beam directions.
Solution Approach 2:
The patent changes the parameter being optimized from beam direction to beam phase. By measuring channel state information and adjusting beam phases accordingly, the system compensates for frequency selective fading caused by multipath propagation, thereby improving signal quality while maintaining the beam direction determined through scanning.
2Reliability
If multiple reference signals are measured in multiple forwarding modes, then optimal forwarding mode can be determined, but terminal device complexity increases
Solution Approach 1:
The patent applies preliminary action by having the network device pre-configure multiple forwarding modes for the intelligent surface device before actual data transmission. The terminal device measures reference signals across these pre-defined modes to obtain channel state information, which is then used to determine the optimal forwarding mode. This approach structures the complexity management by establishing forwarding modes in advance rather than determining them dynamically during transmission.
Data Source
AI summary
This application discloses a beam training method and apparatus, a terminal device, and a network device. The beam training method in this application includes: measuring at least two reference signals used for beam training and forwarded by an auxiliary device in at least two forwarding modes to obtain measurement information, where the measurement information is used for indicating an optimal forwarding mode of the auxiliary device, and a forwarding mode of the auxiliary device is determined by using a beam direction of a forwarded signal of the auxiliary device and a beam phase of the forwarded signal of the auxiliary device; and reporting the measurement information to a network device.