Unit Sourcing Server for Adaptive Modem Hypothesis Tuning
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Solution Overview
Problem
Existing wireless communication systems lack the ability to adaptively tune modem algorithms post-deployment, leading to performance variations across different regions and network experiences, as they are initially tuned conservatively to support all devices and markets without considering device-specific configurations or current service demands.
Innovation Solution
A method and apparatus for wireless communications that utilize a unit sourcing server to select and fine-tune modem hypotheses based on network access node frequency, employing either unit sourced or crowd sourced hypotheses, which are adjusted using learning algorithms and crowd sourced data to optimize modem performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If modem algorithms are tuned conservatively to support all devices and markets, then device compatibility and reliability are improved, but performance optimization for specific regions and networks deteriorates
Solution Approach 1:
The patent implements dynamic modem tuning by transitioning from static, pre-deployment algorithm parameters to post-deployment adaptive parameters. The system continuously collects performance data from multiple UEs in specific geographic regions and network conditions, then updates modem algorithms dynamically to optimize performance for local conditions while maintaining broad compatibility through the ensemble approach.
Solution Approach 2:
The patent applies local quality by creating region-specific and network-specific modem parameter sets. Instead of using uniform conservative parameters globally, the system generates localized parameter optimizations based on geographic region, network operator, and specific environmental conditions, allowing each local deployment to have tailored performance characteristics.
2Productivity
If device-specific configurations and current service demands are considered for tuning, then modem performance is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a server-based intermediary system that handles the complex data collection, analysis, and parameter optimization tasks. Instead of implementing complex tuning logic directly in each UE modem, the system uses a centralized server to process performance data from multiple UEs and generate optimized parameters, which are then distributed back to UEs. This intermediary approach manages complexity by externalizing the computational burden.
Solution Approach 2:
The system implements self-service through automated performance monitoring and parameter optimization. UEs automatically report their performance metrics and operating conditions to the server, which then automatically generates and distributes optimized parameters without requiring manual intervention. This self-service mechanism reduces operational complexity while enabling continuous performance improvement.
3Adaptability or versatility
If post-deployment tuning is implemented, then adaptability to different network experiences is improved, but memory and processing load on user equipment increases
Solution Approach 1:
The patent implements partial action by having UEs participate selectively in the tuning process. Instead of requiring full participation from all UEs in all conditions, the system allows UEs to report performance data under specific trigger conditions and use pre-computed parameter sets for optimization. This partial participation approach reduces the processing and energy burden on individual UEs while still achieving population-level optimization.
4Productivity
If multiple hypotheses with different weights are maintained for different regions, then performance optimization is improved, but memory requirements and data management complexity increase
Solution Approach 1:
The patent applies universality by creating a unified parameter set that serves multiple functions and regions simultaneously. The ensemble of weighted hypotheses is designed to be broadly applicable across different geographic regions, network operators, and device types. This universal parameter set reduces the need for maintaining completely separate parameter sets for each specific condition, thereby reducing memory requirements while maintaining optimization effectiveness.
Data Source
AI summary
A method for wireless communication, for example, accessing a wireless network via a network access node and determining whether the network access node is a frequented node or a non-frequented node based on an identifier for the network access node is provided. The method executes a modem function using a corresponding selected hypothesis having associated weights for each feature associated with the modem function, and being a unit sourced hypothesis or a crowd sourced hypothesis, each hypothesis corresponding to a modem function and including a plurality of features, state information and at least one trigger point. The method sends information, to a server, the information comprising a device identifier identifying the UE, the modem function, the selected hypothesis and associated weights, metrics for each feature and state information, if state information is available, in response to a trigger point being met when executing the modem function. Other aspects are provided.