UE Beam Change Prediction with Probabilistic Signaling Models
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Solution Overview
Problem
Wireless communication systems face challenges in predicting beam changes due to unawareness of UE devices, leading to increased power consumption and reduced link performance, with existing methods being complex and inefficient.
Innovation Solution
UE devices use machine learning models, such as hidden Markov models, to predict beam changes based on signal measurements and probabilities, transmitting predictions to network nodes to enhance signaling and adjust behavior accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the UE predicts beam changes using existing methods, then link performance may be maintained, but power consumption increases and the methods become complex and rigid
Solution Approach 1:
The patent changes the parameter of prediction output from binary (change/no change) to probabilistic (likelihood value), allowing the network to make more informed decisions about when to switch beams, thereby maintaining link performance while reducing unnecessary beam switches that consume power
Solution Approach 2:
The patent introduces a probabilistic model as an intermediary between the raw signal measurements and the beam switching decision. This intermediary layer processes the uncertainty in a sophisticated manner, enabling better trade-offs between maintaining connection reliability and reducing power consumption through more accurate prediction
2Reliability
If the UE predicts beam changes using existing methods, then connection continuity may be maintained, but the prediction methods become complex and prohibitive
Solution Approach 1:
The patent replaces complex mechanical-like decision systems with a probabilistic mathematical model. Instead of using rigid rule-based or machine learning systems that require extensive computation, the patent uses probability theory to model beam change likelihood, simplifying the prediction mechanism while maintaining connection continuity
Solution Approach 2:
The patent transforms the complexity issue by changing the output parameter from a simple binary decision to a probabilistic value. This allows the system to capture uncertainty without requiring complex algorithms, as the probabilistic framework naturally handles the nuance of prediction confidence
3Reliability
If the UE unnecessarily alters behavior to predict beam changes, then connection may be maintained, but power consumption increases
Solution Approach 1:
The patent applies partial action by having the UE perform predictions only when the probabilistic model indicates sufficient likelihood of beam change. Instead of continuously monitoring and preparing for all possible beam changes, the system selectively activates prediction and behavior alteration only when necessary, reducing unnecessary power consumption while maintaining connection
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a reference signal from a network node. The reference signal may be associated with a beam, of a set of beams. Each beam of the set of beams may be associated with a respective beam index. The UE may determine a probability associated with an adjustment of the beam. The probability may indicate whether the beam will change over a duration from a first beam associated with a first beam index to a second beam associated with a second beam index based on the reference signal. The UE may transmit an indication of the probability associated with the adjustment of the beam to the network node.