AI-Driven UE Technology Switching for Adverse Coverage Conditions
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Solution Overview
Problem
User equipment (UE) performance is degraded in adverse environmental conditions, such as high speed or reduced coverage, leading to latency and loss of connectivity, which impacts the quality of user experience in wireless communications networks like 5G.
Innovation Solution
An AI engine on the UE detects adverse conditions and switches between technologies like UL MIMO and UL CA to optimize performance, using a database to dynamically apply strategies tailored to the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional UE operation is used in adverse environmental conditions, then device simplicity is maintained, but performance degradation occurs including latency and loss of connectivity
Solution Approach 1:
The UE autonomously detects adverse environmental conditions and triggers AI engine procedures without external intervention. The device self-monitors its operating environment and automatically initiates performance optimization when degradation is detected, eliminating the need for complex external control systems.
Solution Approach 2:
The system pre-loads multiple AI engine procedures into the UE database before adverse conditions occur. When connectivity issues are detected, the UE can immediately execute pre-prepared optimization strategies without waiting for network instructions, reducing latency and maintaining connectivity reliability.
2Productivity
If AI engine procedures are implemented to optimize UE performance, then uplink throughput is improved, but device complexity increases
Solution Approach 1:
The AI engine functionality is extracted as a separate, modular component within the UE architecture. Multiple AI procedures are stored in a dedicated database, allowing the core UE functionality to remain simple while the AI engine provides advanced optimization capabilities when needed. This modular extraction enables performance improvement without fundamentally complicating the base system.
Solution Approach 2:
The AI engine is designed to perform multiple optimization functions through a single unified architecture. It can execute various procedures for different adverse conditions (high speed, reduced coverage, latency issues) using the same engine, thereby improving uplink throughput across multiple scenarios without proportionally increasing device complexity.
3Adaptability or versatility
If multiple AI procedures are stored in database for different conditions, then adaptability to adverse environments is improved, but memory requirements increase
Solution Approach 1:
The AI procedure database is organized with local quality characteristics, where specific procedures are stored for specific adverse environmental conditions. The database structure allows efficient retrieval of condition-matched procedures without storing all possible optimizations for all scenarios, reducing overall storage requirements while maintaining high adaptability to the actual environmental conditions encountered.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, detecting conditions of an operating environment of a user equipment device (UE); determining that the operating environment is an adverse environment; and providing a signal to an artificial intelligence (AI) engine on the UE regarding the adverse environment; the AI engine, responsive to the signal, obtains from a database on the UE a list of procedures for improving the performance of the UE and creates a strategy that specifies one or more of the listed procedures to be performed; one of the specified procedures comprises switching between a use of a first technology for improving the performance of the UE and a use of a second technology for improving the performance of the UE. Other embodiments are disclosed.