UE Beam Management Using Spatial Beam Relationship Indicators
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Solution Overview
Problem
Existing 3GPP communication systems, particularly in 5G NR, face challenges in efficiently managing beamforming and beam management due to diverse deployment scenarios and varying requirements for data rates, latency, and reliability, which affect coverage and interference.
Innovation Solution
Implementing an artificial intelligence/machine learning model to enhance beam management procedures by utilizing a user equipment (UE) with a transceiver that receives a relationship indicator for spatial beam relationships, enabling efficient beam alignment and adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional beam management procedures are used in 5G NR systems, then basic beamforming functionality is maintained, but beam management efficiency is insufficient for diverse deployment scenarios
Solution Approach 1:
The patent applies parameter changes by transitioning from traditional beam management to AI/ML-based beam management, where the system dynamically adjusts beam parameters (such as beam direction, width, and power) based on learned spatial relationships. The AI model processes input features including spatial relationship indicators and generates optimized beam configurations that adapt to diverse deployment scenarios, thereby improving beam management efficiency while maintaining versatility.
2Productivity
If AI/ML models are implemented for beam management, then beam management efficiency is improved, but system complexity increases
Solution Approach 1:
The patent employs an AI/ML model as an intermediary component that sits between the raw spatial relationship data and the beam management decisions. This intermediary processes complex spatial relationship indicators and transforms them into optimized beam configurations, thereby improving beam management efficiency while encapsulating the complexity within the AI model itself rather than distributing it throughout the entire system.
Solution Approach 2:
The system performs preliminary actions by pre-processing spatial relationship indicators and feeding them into the AI/ML model before actual beam management decisions are made. This preliminary processing step allows the AI model to learn and predict optimal beam configurations in advance, improving overall beam management efficiency while keeping the real-time decision-making process simpler.
3Reliability
If spatial relationship indicators are processed using AI/ML, then coverage and interference management are enhanced, but processing requirements increase
Solution Approach 1:
The patent applies partial action by selectively processing only the most relevant spatial relationship indicators through the AI/ML model rather than processing all possible parameters. The system identifies and processes key features that have the greatest impact on coverage and interference management, thereby enhancing reliability while reducing overall processing requirements and energy consumption.
Data Source
AI summary
Some exemplary embodiments relate to a user equipment, UE, a base station and respective methods for a UE and a base station. For example, the UE comprises a transceiver which, in operation, receives a relationship indicator indicating a relative spatial relationship between each of a plurality of beams. The UE further comprises circuitry which, in operation, performs a beam management procedure using the relative spatial relationship.