Autonomous Beam Failure Detection Timer Adjustment
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently detecting and recovering from beam failures due to changing channel conditions, leading to reduced reliability and increased latency in beam failure procedures.
Innovation Solution
A method and apparatus for user equipment (UE) to autonomously determine timer values for beam failure detection (BFD) and beam failure recovery (BFR) timers using machine learning (ML) models or mappings, allowing for dynamic adjustment based on current channel conditions and UE capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed timer values are used for beam failure procedures, then device complexity is reduced, but reliability deteriorates due to inability to adapt to changing channel conditions
Solution Approach 1:
The UE autonomously determines the timer value for beam failure detection procedures by measuring channel conditions and selecting appropriate timer values without requiring network configuration or intervention. This self-service approach enables the device to adapt to changing channel conditions dynamically, improving reliability while avoiding the complexity of network-side timer management.
Solution Approach 2:
The timer value is made dynamic rather than fixed, allowing the UE to adjust the beam failure detection timer based on current channel conditions. This dynamic adjustment mechanism enables the system to respond to varying wireless environments, improving detection reliability without requiring complex reconfiguration procedures.
2Adaptability or versatility
If network-configured timer values are used, then ease of operation is improved, but adaptability deteriorates due to inability to respond to changing channel conditions
Solution Approach 1:
The UE independently determines timer values based on its own channel measurements without requiring network configuration messages or manual intervention. This eliminates the need for complex network-side timer management while maintaining ease of operation through autonomous device behavior.
Solution Approach 2:
The timer value parameter is changed dynamically based on measured channel conditions. The UE selects from a set of possible timer values or determines a continuous timer value based on channel quality metrics, enabling adaptation to varying wireless environments without network reconfiguration.
3Reliability
If conservative timer values are used, then reliability is improved, but loss of time increases due to extended beam failure procedure duration
Solution Approach 1:
The timer value is adjusted dynamically based on channel conditions rather than using a fixed conservative value. When channel conditions are good, shorter timer values are used to reduce latency. When conditions deteriorate, longer timer values ensure reliable detection. This dynamic approach resolves the trade-off between reliability and time loss.
Solution Approach 2:
The timer parameter is optimized based on measured channel conditions, selecting from multiple possible values to balance detection reliability with procedure speed. This prevents the system from always using overly conservative timer values that unnecessarily extend procedure duration.
Data Source
AI summary
This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for techniques for beam failure detection and recovery. A UE may support autonomous determination of a beam failure timer for a beam failure procedure, such as a beam failure detection (BFD) timer for a BFD procedure or a beam failure recovery (BFR) timer for a BFR procedure. For example, a network entity may transmit reference signals to the UE that are associated with the beam failure procedure. For instance, the UE may measure the reference signals as part of a BFD procedure or may initiate a BFR procedure in response to receiving the reference signals. The UE may determine a timer value for the beam failure timer using, for example, a machine learning (ML) model or a mapping and may perform the beam failure procedure in accordance with the UE-determined beam failure timer.