AI-Driven SSB Beam Configuration for O-RAN NR Access
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Solution Overview
Problem
Conventional radio access networks face challenges in optimizing synchronization signal block (SSB) configurations due to manual, static settings that lead to increased power consumption, degraded spectral efficiency, and UE battery life, especially in 3GPP NR-based systems with massive MIMO, where network usage-dependent, time-varying configurations are complex and inefficient.
Innovation Solution
Implementing an AI/ML optimizer in the service management and orchestration layer to dynamically infer optimal SSB beam configurations based on real-time observations, using methods such as static mapping, beam book-based approaches, or dynamic IQ signaling, to efficiently communicate these configurations to network elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually derived SSB beam configurations are used to accommodate worst case scenarios, then network reliability is improved, but power consumption increases and spectral efficiency degrades
Solution Approach 1:
The patent implements dynamic SSB beam configuration that adapts to actual network conditions rather than using static manual configurations. The system continuously monitors network usage patterns and demographic changes, then automatically adjusts beam configurations to match current conditions, resolving the contradiction between reliability and power consumption by eliminating unnecessary transmissions while maintaining service quality.
Solution Approach 2:
The system changes key parameters including beam width, beam direction, and transmission power based on real-time network conditions and demographic data. By dynamically adjusting these parameters rather than maintaining fixed conservative settings, the system achieves both reliability and reduced power consumption.
2Reliability
If manually derived SSB beam configurations are used to accommodate worst case scenarios, then network reliability is improved, but spectral efficiency degrades
Solution Approach 1:
The system dynamically adjusts beam configurations based on actual network demand and demographic patterns, optimizing spectral efficiency by allocating resources according to real conditions rather than worst-case assumptions. This enables more efficient use of available spectrum while maintaining necessary service reliability.
Solution Approach 2:
The system performs self-optimization by automatically monitoring network conditions and adjusting SSB configurations without manual intervention. This self-service capability enables continuous optimization of spectral efficiency while maintaining reliability through adaptive response to changing conditions.
3Use of energy by moving object
If inferior static SSB beam configuration is used at installation time, then power consumption is reduced, but initial access latency increases and tracking performance degrades
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing multiple beam configuration options based on demographic data and historical network patterns. When deployed, it can quickly switch between pre-prepared configurations rather than calculating from scratch, enabling low-latency adaptation that reduces initial access time while maintaining power efficiency.
Solution Approach 2:
The system transitions from static to dynamic configuration, allowing real-time adaptation of beam parameters based on current network conditions. This dynamic approach enables the system to maintain low power consumption while avoiding the latency and performance degradation associated with inferior static configurations.
4Productivity
If AI/ML optimizer is implemented to dynamically infer optimal SSB beam configurations, then spectral efficiency is improved, but device complexity increases
Solution Approach 1:
The patent introduces an AI/ML optimizer as an intermediary component that bridges network data and beam configuration decisions. This intermediary processes demographic data, network conditions, and performance metrics to generate optimized configurations, centralizing the complexity in a dedicated module while keeping the rest of the system simple and manageable.
Solution Approach 2:
The AI/ML optimizer enables self-service optimization by automatically inferring optimal beam configurations from network data without requiring manual intervention or complex configuration management. This self-service capability improves spectral efficiency while managing system complexity through automated, data-driven decision-making.
Data Source
AI summary
A method of optimizing synchronization signal block (SSB) beam configuration for 3rd Generation Partnership Project (3GPP) New Radio (NR) network includes: using at least one of an artificial intelligence (AI) and machine learning (ML) engine in one of service management and orchestration (SMO) module, non-real time radio access network intelligent controller (Non-RT RIC), and near-real time radio access network intelligent controller (Near-RT RIC), to derive at least one optimal SSB beam configuration for the 3GPP NR network; and communicating, by the at least one of the AI and ML engine, the derived at least one optimal SSB beam configuration to a network node of the 3GPP NR network. Both the training and deployment of the at least one of the AI and ML engine are executed in the Non-RT RIC, and a static mapping between SSB beam identifiers (IDs) and beamforming weights (BFWs) is predefined.