Adaptive Analog Beamforming Weights for 5G Elevation Coverage
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Solution Overview
Problem
Existing hybrid beamforming technologies face challenges in optimizing beamforming weights for diverse wireless deployment scenarios, particularly in 5G networks, leading to suboptimal signal strength and coverage due to limited flexibility in adjusting analog beamforming weights based on elevation angles.
Innovation Solution
A method and apparatus that determine a set of analog beamforming weights based on elevation angle information for user devices, adjusting these weights using pre-tilt angle and mechanical downtilt angles to enhance hybrid beamforming, particularly in 5G networks, ensuring optimal signal coverage across various deployment types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing hybrid beamforming technologies are used with fixed analog beamforming weights, then the system structure is simple, but the signal strength and coverage are suboptimal due to inability to adjust to diverse deployment scenarios
Solution Approach 1:
The patent applies dynamics by transitioning from fixed analog beamforming weights to dynamic weights that can be adjusted based on elevation angle distribution. The system calculates optimal analog beamforming weights according to the actual deployment scenario and user distribution, enabling adaptive beamforming that responds to changing conditions while maintaining reliability of signal strength.
Solution Approach 2:
The patent changes the parameter of analog beamforming weights from fixed values to calculated values based on elevation angle distribution. By determining weights according to the specific deployment scenario and user elevation angles, the system optimizes signal strength without requiring complex mechanical adjustments, resolving the contradiction between reliability and device complexity.
2Area of stationary object
If analog beamforming weights are adjusted based on elevation angle distribution, then coverage and signal strength improve, but the complexity of determining and adjusting weights increases
Solution Approach 1:
The system performs self-service by automatically calculating optimal analog beamforming weights based on the measured elevation angle distribution. The network node autonomously determines the weights without requiring manual configuration or complex external optimization processes, thereby improving coverage while keeping the optimization process manageable through automated algorithms.
Solution Approach 2:
The patent applies preliminary action by pre-calculating and determining the analog beamforming weights based on the elevation angle distribution before actual communication begins. This advance determination of optimal weights allows the system to achieve improved coverage without adding complexity during real-time operation, as the optimization is performed in advance based on deployment scenario analysis.
3Adaptability or versatility
If hybrid beamforming is used with fixed beamforming weights, then the implementation is straightforward, but adaptability to different deployment scenarios is limited
Solution Approach 1:
The patent changes the beamforming weight parameters from fixed to adaptive values that are calculated based on elevation angle distribution. This allows the hybrid beamforming system to adapt to different deployment scenarios (urban macro, urban micro, rural) by automatically adjusting weights according to the specific elevation angle characteristics of each scenario, thereby improving adaptability without significantly increasing implementation complexity.
Solution Approach 2:
The system achieves universality by creating a single hybrid beamforming framework that can handle multiple deployment scenarios through automated weight adjustment. The same basic system structure adapts to different scenarios (urban macro, urban micro, rural) by calculating appropriate weights based on elevation angle distribution, eliminating the need for separate implementations for each scenario and thereby improving versatility without proportionally increasing complexity.
Data Source
AI summary
A method includes obtaining elevation angle information for a plurality of user devices, wherein the elevation angle information includes, for each of the plurality of user devices, a best or preferred beam elevation angle used or applied by a network node for communication with the user device; estimating an elevation angle distribution for the plurality of user devices based on the collected elevation angle information; determining, based on the elevation angle distribution, a set of analog beamforming weights to be used for hybrid beamforming, wherein each analog beamforming weight of the set of analog beamforming weights is associated with an analog elevation angle; and adjusting the set of analog beamforming weights based on at least one of a set of pre-tilt angle beamforming weights or an estimated (or an estimate of) mechanical downtilt angle.