5G Beam Training via Quasi Co-Location Assumptions
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Solution Overview
Problem
The 3GPP protocol lacks clarity on the behaviors of base stations and user equipment during beam training in 5G millimeter wave communication, leading to poor beam training performance and system delays due to misalignment and inappropriate behaviors.
Innovation Solution
A beam training method that utilizes a quasi co-location (QCL) assumption to align the beam sweeping behaviors of base stations and terminals, enhancing the speed and accuracy of beam training by constraining transmit and receive beams based on specific conditions related to periodic instances and resource sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If beam sweeping is performed without clear protocol alignment between base station and terminal, then beam training can be performed, but beam training performance is poor and system delay increases
Solution Approach 1:
The patent applies parameter changes by introducing QCL (Quasi-Co-Location) assumptions with specific periodicity relationships. The base station configures reference signals with defined QCL parameters where periodic instances at N-period intervals share the same QCL assumption, allowing the terminal to infer beam sweeping patterns and align its receiving behavior accordingly, thus resolving the protocol misalignment issue.
Solution Approach 2:
The patent implements feedback mechanisms where the base station configures QCL assumptions that provide implicit feedback to the terminal about beam sweeping patterns. The terminal uses these QCL assumptions to determine when to switch receive beams, creating a coordinated feedback loop that aligns both ends' behaviors without requiring explicit signaling for each beam switch.
2Reliability
If beam sweeping behaviors are not aligned between base station and terminal, then beam training can proceed, but beam training cannot be completed successfully
Solution Approach 1:
The patent applies preliminary action by having the base station pre-configure QCL assumptions before beam training begins. These QCL assumptions contain pre-defined periodicity relationships (N-period intervals) that tell the terminal in advance when beam sweeping will occur, allowing the terminal to prepare its receive beam switching timing beforehand, thus ensuring successful beam training completion.
3Reliability
If inappropriate beam sweeping behaviors are used, then beam training can be performed, but beam training performance deteriorates
Solution Approach 1:
The patent uses parameter changes by defining specific QCL assumption parameters with periodicity relationships. The base station configures reference signals where periodic instances at N-period intervals share identical QCL assumptions, creating a predictable pattern that simplifies behavior coordination while maintaining high beam training performance.
Data Source
AI summary
Embodiments of this application provide a beam training method and an apparatus, to improve beam training performance. The method includes: The first apparatus determines a quasi co-location assumption of a first reference signal, and receives, based on the quasi co-location assumption of the first reference signal, the first reference signal sent by a second apparatus. The quasi co-location assumption meets at least either of the following conditions: A plurality of periodic instances of the first reference signal at an interval of N periodicities have a same quasi co-location assumption, where N is an integer greater than 0; and first reference signals that have different indexes and that are in a same resource set have different quasi co-location assumptions.