Distributed Cross-Layer Beam Management Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing beam management systems in new radio (NR) networks face challenges in balancing network performance with signaling overhead, particularly in high mobility scenarios where frequent beam measurement and reporting are necessary to maintain continuous connections.
Innovation Solution
A distributed cross-layer beam management engine is implemented, utilizing an O-RAN architecture to distribute beam management procedures across different layers of the radio access network (RAN) control framework. This includes AI/ML applications in non-real time, near-real time, and real time RAN intelligent controller layers to optimize beam alignment and reduce signaling overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent beam measurement and measurement reporting are implemented to guarantee continuous connection in high mobility scenarios, then connection reliability is improved, but signaling overhead increases which decreases network performance
Solution Approach 1:
The system dynamically adjusts beam management parameters including measurement frequency, reporting intervals, and beam sweeping patterns based on UE mobility state detection. For high mobility UEs, the system modifies measurement and reporting configurations to maintain connection reliability while optimizing signaling overhead through adaptive parameter tuning rather than fixed frequent measurements
Solution Approach 2:
The system implements feedback mechanisms where the network monitors UE mobility patterns and connection stability, then adjusts beam measurement and reporting configurations accordingly. This closed-loop approach allows the system to maintain reliable connections for mobile UEs while reducing unnecessary signaling overhead by adapting the frequency and granularity of beam management procedures based on actual network conditions and UE behavior
2Reliability
If traditional centralized beam management is used, then beam alignment can be maintained, but system complexity and processing burden increase
Solution Approach 1:
The beam management functionality is segmented and distributed across multiple network entities including gNBs, UEs, and network controller. Each entity performs localized beam management tasks such as beam sweeping, measurement, and reporting, while higher-level coordination is handled by the network controller. This segmentation reduces the processing burden on individual components while maintaining overall beam alignment through coordinated distributed operation
Solution Approach 2:
The system enables self-service beam management where UEs autonomously perform beam measurements and reporting based on configured parameters, and gNBs independently conduct beam sweeping and selection. This distributed self-service approach reduces the centralized processing burden and system complexity while maintaining reliable beam alignment through autonomous local decision-making at each network entity
Data Source
AI summary
The technology described herein is directed towards a distributed cross-layer intelligent beam management engine that performs a beam alignment procedure, including learning a site-specific probing codebook and using probing codebook measurements to predict an optimal narrow beam using an AI/ML method. The learned codebook determines site-specific probing beams that can capture particular characteristics of the propagation environment. The distributed engine includes distributed applications with different delay requirements that perform different parts of the beam alignment procedure. One application (e.g., in a non-real time controller) provides probing codebook policy data and reporting policy data, and another application (e.g., in a near-real time controller) learns the probing codebook based on the policy data. A third, real time application performs the beam sweeping based on the probing codebook, with the optimal beam for a user equipment identified by the user equipment.