Adaptive Beam Grid Selection for 5G NR Interference Conditions

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Heuristic beam grids in 5G NR systems do not account for UE distribution, hardware imperfections, and interference, leading to inefficient beam utilization and network performance issues.

Innovation Solution

A method for optimizing beam grids by selecting a grid based on current network state and beam utility functions, using random greedy search to identify a beam grid that maximizes system performance and minimizes overhead, considering UE distribution, hardware distortions, and interference.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If heuristic beam grid is used with predefined beam parameters, then beam coverage can be established, but beam utilization becomes inefficient due to not accounting for UE distribution and interference

Engineering Contradiction:

Improvebeam utilization efficiencyVSAvoidbeam grid configuration complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The beam grid configuration is made dynamic by allowing network devices to select from multiple candidate beam grids based on current network state. The system transitions from static predefined configurations to adaptive selection, where beam grids can be reconfigured according to changing UE distribution, interference conditions, and network load, thereby improving beam utilization efficiency without requiring complete redesign of the beam grid structure

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The invention changes key beam grid parameters including beam width, beam direction, and number of beams by selecting from multiple candidate configurations. Each candidate beam grid represents a different parameter set, and the network device selects the optimal parameters based on current network conditions, resolving the contradiction between efficiency and complexity through parameter adaptation rather than structural redesign

Inventive Principle:

Principle #35Parameter changes

2Reliability

If beam grid is optimized for uniform UE distribution, then theoretical coverage is achieved, but actual performance degrades due to imperfect hardware implementation and interference

Engineering Contradiction:

Improvenetwork performance reliabilityVSAvoidadaptation to actual network conditions

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system implements feedback mechanisms by monitoring actual network state including UE distribution, interference levels, and beam performance metrics. This feedback information is used to select the most appropriate candidate beam grid configuration, allowing the system to adapt to real-world conditions such as hardware imperfections and interference patterns that deviate from ideal theoretical models

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

Multiple candidate beam grids are prepared in advance, each optimized for different network conditions. This preliminary preparation allows the system to quickly switch to an appropriate configuration when conditions change, rather than attempting to optimize a single beam grid for all possible scenarios, thereby improving reliability across varying actual network conditions

Inventive Principle:

Principle #10Preliminary action

3Productivity

If beam grid configuration is changed frequently to adapt to network state, then beam utilization improves, but system overhead increases

Engineering Contradiction:

Improvebeam utilization efficiencyVSAvoidconfiguration update time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system dynamically selects from a finite set of pre-defined candidate beam grids rather than continuously optimizing beam parameters. This dynamic selection approach allows the system to adapt to changing network conditions while limiting the frequency and scope of reconfigurations, as changes only occur when switching between discrete candidate configurations rather than continuous parameter adjustment

Inventive Principle:

Principle #15Dynamics

4Adaptability or versatility

If multiple candidate beam grids are maintained for selection, then adaptability to network conditions improves, but device complexity increases

Engineering Contradiction:

Improveadaptation to network conditionsVSAvoidbeam grid management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The beam grid configuration space is segmented into multiple discrete candidate beam grids, each representing a specific configuration option. This segmentation allows the system to manage complexity by breaking down the continuous parameter space into manageable discrete choices, where each candidate grid is independently defined and can be selected based on simple comparison criteria rather than requiring complex real-time optimization

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12556258B2Beam grid optimization

Publication Date: 2026.02.17 NOKIA SOLUTIONS & NETWORKS OY

AI summary

A method including: obtaining information representative of a current network state; selecting a beam grid in a plurality of candidate beam grids supported by a network device based on the current network state and current beam utility function values determined for one or more beams in the plurality of candidate beam grids; and configuring the network device with the selected beam grid.