Adaptive Beam Sweeping via UE Context Modeling
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Solution Overview
Problem
Current 5G beam sweeping methods are inefficient as they do not adapt to varying UE density over time, location, and context, leading to excessive energy consumption and suboptimal beam coverage.
Innovation Solution
Implementing an adaptive beam sweeping system using a radio access network intelligent controller that models UE distribution in a multi-dimensional context space, combining real-time measurements with machine learning models to optimize beam sweeping parameters, reducing unnecessary sweeps and enhancing energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional beam sweeping methods are used, then beam coverage is maintained, but energy consumption increases and efficiency decreases
Solution Approach 1:
The beam sweeping system dynamically adjusts sweeping parameters (such as sweeping frequency, beam direction, and beam width) based on real-time UE distribution characteristics. The network device determines adaptive beam sweeping configurations by analyzing UE density and spatial distribution, thereby optimizing energy consumption while maintaining adequate coverage.
Solution Approach 2:
The patent changes key parameters of beam sweeping operation including sweeping periodicity, beamforming vectors, and resource allocation based on UE distribution models. By adjusting these parameters adaptively rather than using fixed configurations, the system achieves better energy efficiency and productivity.
2Reliability
If frequent beam sweeping is performed, then beam coverage is improved, but unnecessary energy consumption increases
Solution Approach 1:
Instead of performing exhaustive beam sweeping in all directions at all times, the system applies partial action by focusing beam sweeping only on regions where UEs are detected or expected. The UE distribution model identifies high-probability user locations, allowing the network to concentrate beam resources where needed rather than performing unnecessary sweeps in empty areas.
Solution Approach 2:
The system performs preliminary actions by using UE distribution models to predict where users are likely to be located before actual beam sweeping occurs. This allows the network to pre-configure beam directions and resource allocation based on historical and real-time UE distribution patterns, reducing the need for exploratory sweeping.
3Use of energy by moving object
If adaptive beam sweeping is implemented, then energy efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces a UE distribution model as an intermediary component that bridges raw UE measurement data and beam sweeping control decisions. This model layer processes and interprets UE distribution characteristics, transforming complex raw data into actionable insights for beam configuration, thereby managing system complexity while achieving energy efficiency.
Solution Approach 2:
The system implements feedback mechanisms where beam sweeping performance and UE distribution measurements are continuously monitored and fed back to adjust subsequent beam configurations. This closed-loop approach allows the system to learn from past performance and automatically optimize energy efficiency without requiring complex manual configuration.
Data Source
AI summary
Given real-time user equipment (UE) measurements from an open radio access network (O-RAN) infrastructure, a radio access network intelligent controller (RIC) can compute a UE distribution context space. The O-RAN can comprise gNode-Bs, centralized units, and distributed units. The UE distribution context space computations can be performed by a UE context correlator module of the RIC. The UE context correlator module can also utilize a pre-defined UE context model, which contains definitions and values for various UE context attributes to generate adaptive beam-forming patterns.