Dynamic Beam Weights for Wireless Signal Adaptation
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Solution Overview
Problem
Conventional wireless communication systems using static beams are not optimal in environments with changing electric fields, such as hand blockage or non-line-of-sight channels, as they do not adapt to improve signal quality.
Innovation Solution
The method involves estimating a channel using a set of predefined sensing beams to measure reference signals, generating dynamic beam weights to maximize reference signal received power (RSRP) based on the estimated channel, and applying these weights to an antenna array.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static beams are used in conventional wireless communication systems, then device complexity is reduced and ease of operation is improved, but signal quality and spectral efficiency deteriorate in environments with changing electric fields such as hand blockage or non-line-of-sight channels
Solution Approach 1:
The patent applies dynamics by transitioning from static beam configurations to dynamic beam weights that adapt to changing channel conditions. The base station determines beam weights based on channel state information and updates them dynamically, allowing the system to respond to hand blockage and non-line-of-sight conditions while maintaining manageable complexity through structured weight determination methods.
Solution Approach 2:
The patent changes the parameter of beam weights from fixed static values to dynamically adjusted values based on channel conditions. By modifying beam weights according to channel state information, the system improves signal quality in challenging environments while the structured approach to weight determination keeps implementation complexity reasonable.
2Adaptability or versatility
If static beams are used, then system simplicity is maintained, but adaptability to changing channel conditions deteriorates
Solution Approach 1:
The patent implements feedback by using channel state information to determine beam weights. The base station receives channel state information from user equipment, processes this feedback, and adjusts beam weights accordingly. This closed-loop feedback mechanism enables adaptability to changing conditions while maintaining systematic control over complexity.
Solution Approach 2:
The system transitions from static to dynamic beam management, where beam weights are continuously adapted based on channel conditions. This dynamic approach improves adaptability while the structured weight determination process based on channel state information keeps the system manageable.
3Reliability
If dynamic beam weights are generated to maximize RSRP, then signal quality improves in changing environments, but processing complexity increases
Solution Approach 1:
The patent changes beam weights from static to dynamic parameters based on channel state information. By systematically adjusting weights to maximize RSRP in response to channel conditions, the system improves signal quality while the structured parameter adjustment process manages processing complexity.
Solution Approach 2:
The base station performs preliminary determination of beam weights based on channel state information before transmission. This advance preparation of optimal beam weights maximizes RSRP for upcoming transmissions, improving signal quality while the pre-computed weights reduce real-time processing complexity.
Data Source
AI summary
Example implementations include a method, apparatus and computer-readable medium for wireless communication configured for estimating a channel using a set of predefined sensing beams to measure reference signals. Estimating the channel may include generating an N×N channel correlation matrix, where N is a number of antenna elements in the antenna array. The implementations further include generating a set of dynamic beam weights to maximize a reference signal received power (RSRP) based on the estimated channel. The set of dynamic beam weights may be based on an eigenvector of the channel correlation matrix. Additionally, the implementations further include applying the set of dynamic beam weights to an antenna array.