Antenna Array Beam Recovery via Predefined Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Beamforming antenna arrays in 5G wireless communication systems face challenges in efficiently and reliably recovering from failures, which can lead to service disruptions and degraded performance, especially in mission-critical and latency-sensitive applications.
Innovation Solution
The implementation of recovery beams and recovery beam patterns that can be pre-defined and rapidly switched to in case of failures, utilizing AI and machine learning to anticipate and mitigate beam failures by adjusting antenna array configurations based on channel state information and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If beamforming antenna arrays are used to improve signal targeting and capacity, then signal to interference ratio and network capacity are improved, but system complexity and vulnerability to beam failures increase
Solution Approach 1:
The system pre-identifies and stores multiple candidate recovery beams before failures occur. When a beam failure is detected, the system can immediately switch to a pre-prepared recovery beam, eliminating the need for complex real-time beam search and recovery procedures.
Solution Approach 2:
The system creates backup copies of beam configurations and characteristics. By maintaining multiple candidate recovery beams that are pre-calculated and stored, the system can quickly replace failed beams without complex real-time computation, reducing both recovery time and system complexity.
2Loss of time
If rapid beam failure recovery is implemented, then service disruption time is reduced, but the complexity of beam management and switching increases
Solution Approach 1:
Candidate recovery beams are pre-identified and prepared in advance based on historical data, channel conditions, and beam characteristics. This preliminary preparation allows the system to perform simple switching operations when failures occur, rather than complex real-time beam management.
Solution Approach 2:
The system automatically detects beam failures and switches to pre-identified recovery beams without requiring complex manual intervention or extensive real-time analysis. The pre-prepared recovery beams contain all necessary configuration information, enabling autonomous rapid recovery.
3Speed
If multiple candidate recovery beams are pre-identified and stored, then recovery speed is improved, but memory requirements and system resources increase
Solution Approach 1:
The system stores recovery beam information with varying levels of detail based on local requirements. Frequently used or critical recovery beams are stored with complete configuration information, while less critical ones may use compressed or partial representations, optimizing memory usage while maintaining recovery speed.
Solution Approach 2:
The system maintains a limited set of candidate recovery beams that are most likely to be needed, rather than storing all possible beams. This partial approach focuses resources on the most critical recovery scenarios, achieving fast recovery for the majority of failure cases while minimizing storage requirements.
Data Source
AI summary
The described technology is generally directed towards beam recovery for an antenna array. One or more recovery beams or recovery beam patterns can be defined for an antenna array, and in response to a failure, the antenna array can be restored to a defined recovery beam or recovery beam pattern. Techniques for defining recovery beams and recovery beam patterns for the antenna array, selecting a recovery beam or recovery beam pattern for the antenna array, and entering a selected recovery beam or recovery beam pattern by the antenna array are also disclosed.