User Equipment Performance Statistics Aggregation for Wireless Modifications
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Solution Overview
Problem
Wireless communication systems, particularly those using high frequency bands like 5G, face performance degradation due to signal propagation losses and blockages, with existing signaling mechanisms and parameter configurations often failing to provide suitable performance for end users.
Innovation Solution
User equipment (UE) aggregates performance statistics at a higher layer to facilitate modifications in communications, enabling users to select configurations or receive suggestions based on presented data, and updates network parameters without additional signaling, thereby optimizing performance and reducing hardware complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing signaling mechanisms and parameter configurations are used, then system compatibility is maintained, but performance degradation occurs due to signal propagation losses and blockages in high frequency bands
Solution Approach 1:
The UE autonomously aggregates performance statistics and modifies communication parameters without requiring additional signaling with network entities. The device serves itself by internally processing deployment statistics to optimize communication modes, RAT selections, and parameter configurations based on observed performance patterns.
Solution Approach 2:
The UE proactively aggregates deployment statistics and identifies performance patterns before communication degradation becomes severe. By analyzing historical performance data in advance, the device can pre-adjust communication parameters and select optimal configurations to prevent performance degradation rather than reacting after problems occur.
2Productivity
If deployment statistics are aggregated and used for modifications, then UE performance and mobility are improved, but additional processing complexity is introduced at the UE
Solution Approach 1:
The performance statistics aggregation mechanism serves multiple functions: it tracks deployment performance across different RATs, identifies optimal communication modes, guides parameter modifications, and enables user-facing recommendations. This single aggregation mechanism supports diverse optimization goals without requiring separate tracking systems for each function.
Solution Approach 2:
The UE continuously monitors communication performance metrics, aggregates deployment statistics, and uses this feedback to dynamically adjust communication parameters. The system establishes a closed-loop control where performance measurements inform statistical aggregation, which in turn drives parameter modifications that improve subsequent performance outcomes.
3Loss of information
If detailed performance statistics are provided to users, then user awareness and control are enhanced, but information overload may occur for non-technical users
Solution Approach 1:
The system provides different levels of information detail to different users based on their technical expertise. Technical users receive comprehensive deployment statistics and granular performance data, while non-technical users receive simplified summaries and actionable recommendations. The information presentation is localized to match user capabilities and needs.
Solution Approach 2:
The UE acts as an intermediary that processes raw performance statistics and transforms them into user-appropriate information formats. The device mediates between detailed technical data and user comprehension by filtering, summarizing, and presenting only relevant information in accessible formats, eliminating the need for users to directly interpret complex technical statistics.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. In some systems, a user equipment (UE) may support one or more applications enabling various types of users to access relevant information relating to performance of the UE across one or more communication modes or radio access technologies (RATs). A type or granularity of the information that the UE provides to the user may depend on a type of the user. For example, if the UE detects that a user is a first user type, the UE may provide the user with relatively simpler or more basic information relating to UE performance. Alternatively, if the UE detects that a user is a second user type, the UE may provide the user with relatively more technical or granular information relating to UE performance. The UE may support a user interface via which the UE may present the information to a user.