SSB Burst Pattern Modification for Wireless Resource Optimization
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Solution Overview
Problem
Current wireless communication systems, particularly in 5G NR and LTE technologies, face inefficiencies in beam management for synchronization signal block (SSB) transmissions, leading to resource wastage and increased monitoring time for user equipment (UE), as they sweep across all possible beam directions, which reduces available resources for data transmissions.
Innovation Solution
Implementing a method that uses machine learning-based beam prediction and dynamic modification of SSB burst patterns to select a reduced number of optimal beams for transmission, based on detected conditions such as UE location and mobility state, allowing for reduced monitoring time and resource allocation for data transmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system sweeps across all possible beam directions for SSB transmissions, then complete coverage and synchronization are achieved, but resource wastage increases and monitoring time for UE is extended
Solution Approach 1:
The network entity performs preliminary beam prediction using machine learning models before actual SSB transmissions. By predicting the most likely beam directions based on historical data and current conditions, the system prepares a reduced set of candidate beams in advance, avoiding the need to sweep all possible directions while maintaining synchronization reliability.
Solution Approach 2:
Instead of performing complete beam sweeping across all possible directions, the system transmits SSBs on a partial set of predicted optimal beams. This partial action is sufficient for most synchronization scenarios, reducing resource consumption while maintaining adequate coverage through the accuracy of beam prediction algorithms.
2Adaptability or versatility
If the system sweeps across all possible beam directions, then all UEs can be reached, but monitoring time for UE increases
Solution Approach 1:
The system performs preliminary beam prediction and selects optimal beam directions before SSB transmissions. This advance preparation allows UEs to monitor only the predicted relevant beams rather than all possible directions, significantly reducing monitoring time while maintaining the ability to reach all UEs that require service.
Solution Approach 2:
The beam prediction system adapts to local conditions by considering UE-specific factors such as location, mobility state, and historical beam performance. This localized optimization allows each UE to monitor only the specific beams relevant to its situation, reducing overall monitoring time while maintaining comprehensive UE reachability.
3Productivity
If a reduced number of beams are selected for SSB transmissions, then resource allocation for data transmissions improves, but synchronization coverage may be compromised
Solution Approach 1:
The network entity performs preliminary beam prediction using machine learning models that analyze historical data, current mobility states, and environmental conditions to identify the most probable optimal beams. This advance prediction ensures that the reduced set of selected beams maintains synchronization coverage reliability while freeing resources for data transmissions.
Solution Approach 2:
The system implements feedback mechanisms where UE synchronization performance and beam measurement results are continuously monitored and fed back to the network entity. This feedback loop allows the beam prediction model to learn from actual performance and dynamically adjust the selected beam set, ensuring synchronization reliability is maintained even with fewer beams.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for efficiently selecting beams for synchronization signal block (SSB) burst transmissions based on a condition. Techniques include selecting certain directions to transmit higher power beams and selecting certain directions to transmit lower power SSB burst transmissions. In some cases, an SSB burst parameter may be modified to use a reduced number of optimal SSB beams. The modified SSB burst parameter may have a reduced SSB burst duration, which may allow for reduced monitoring time by a UE and/or free up resources (that would otherwise be used for SSB transmissions) for data transmissions. Additional aspects relate generally to the beam management procedures in wireless communications systems. Some aspects more specifically relate to the selection of beams for communications to and from a UE and a network entity based on predicted mobility state information for a user equipment (UE).