AI-Optimized SSB Beam Direction and Periodicity for 5G gNodeB
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current 5G network SSB configurations are statically set, leading to inefficiencies such as increased overhead, interference, and power consumption due to uniform beam directions and periodicity, which do not adapt to dynamic UE distribution, resulting in sub-optimal network performance and energy waste.
Innovation Solution
An AI/ML-based method dynamically adjusts SSB beam directions and periodicity based on UE-specific beam direction history, determining the minimum number of beams and optimal directions through K-means clustering, and periodically updates configurations to maximize throughput and minimize energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statically configured beam directions and periodicity are used to accommodate worst-case scenarios, then coverage reliability is improved, but network overhead and resource utilization deteriorate
Solution Approach 1:
The patent implements dynamic SSB beam direction configuration where the gNB adjusts beam directions and periodicity based on real-time UE distribution patterns. Instead of static worst-case coverage, the system continuously adapts beam configurations to match actual UE locations, using machine learning models to predict optimal beam directions and adjust periodicity dynamically, thereby reducing unnecessary transmissions while maintaining reliable coverage where UEs actually exist.
Solution Approach 2:
The patent applies different SSB beam configurations to different spatial regions based on local UE distribution characteristics. Rather than uniform static configuration across all directions, the system identifies high-density UE regions and concentrates beam resources there, while reducing or eliminating beams in low-density directions, achieving local optimization of resource allocation matched to actual coverage needs.
2Device complexity
If uniformly configured SSB periodicity is applied to all beam directions, then configuration simplicity is improved, but energy consumption deteriorates
Solution Approach 1:
The patent dynamically changes SSB periodicity parameters based on UE distribution and traffic conditions. The machine learning model determines optimal periodicity values (e.g., 5ms, 10ms, 20ms, 40ms, 80ms, or 160ms) for different beam directions and time periods, adjusting the periodicity parameter to match actual network conditions rather than using fixed uniform periodicity, thereby reducing energy consumption during low-traffic periods.
Solution Approach 2:
The patent implements periodic reconfiguration of SSB beams with adaptive periodicity. Instead of continuous uniform transmission, the system uses periodic SSB bursts with dynamically adjusted intervals, where the periodicity itself is periodically updated based on UE distribution patterns, achieving energy-efficient periodic action that adapts to changing network conditions.
3Ease of manufacture
If fixed-direction SSB beams are used, then implementation ease is improved, but network throughput deteriorates
Solution Approach 1:
The patent transitions from fixed static beam directions to dynamic adaptive beam directions. The system uses machine learning models to continuously determine optimal beam directions based on UE distribution, adjusting beam pointing angles and directions dynamically to align with actual UE locations, thereby maximizing network throughput while maintaining implementation feasibility through automated ML-based configuration.
Solution Approach 2:
The patent implements feedback mechanisms where the gNB monitors UE distribution patterns, RSRP measurements, and connection status, then uses this feedback to continuously optimize beam directions. The machine learning model learns from historical and real-time data, adjusting beam directions based on feedback about actual UE locations and channel conditions, achieving throughput optimization through closed-loop adaptive control.
Data Source
AI summary
A method for optimizing Synchronization Signal Block (SSB) sweep by a gNodeB in a 5G wireless system includes: sweeping, by a gNB, SSBs and collecting a user equipment (UE)-specific beam direction history; determining, by at least one of an artificial intelligence (AI) and machine learning (ML) engine, an optimal number of SSB beams and optimal set of beam directions based on the UE-specific beam direction history, and a complementary set of beam directions for the optimal set of beam directions; transmitting, by the at least one of the AI and ML engine, both the optimal set and the complementary set of SSB beam directions to the gNB; and transmitting, by the gNB, i) at every t1 milliseconds (ms), SSBs in the optimal beam directions, and ii) at every t2 ms, SSBs in the optimal directions as well as the complementary directions, wherein t2>t1 and t1, t2∈{5, 10, 20, 40, 80, 160} ms.