Cloud Aggregated Cell Data for Wireless Handoff Optimization
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Solution Overview
Problem
Current cell handover processes in wireless communication systems are inefficient due to reliance on signal strength and noise ratios alone, which do not account for future service quality and can lead to delayed or interrupted connections, as they do not consider additional parameters like data throughput and cell loading.
Innovation Solution
A method and apparatus that aggregate cell-related data from multiple user equipment (UEs) to provide informed cell handoff decisions by predicting future cell usage based on historical data, reordering scan lists to prioritize cells with better service quality and throughput, and biasing cell measurements to optimize handoff processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If handoff decisions are based on signal strength and noise ratios alone, then the handoff process is simple and fast, but the service quality and throughput are poor
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing cell data (throughput, loading, interference) before handoff decisions are made. The cloud server aggregates historical cell data and predicts future cell conditions, allowing the mobile device to make informed handoff decisions based on pre-analyzed information rather than raw signal strength alone.
Solution Approach 2:
A cloud server acts as an intermediary between the mobile device and the network cells. The server aggregates cell data from multiple sources, analyzes throughput, loading, and interference metrics, and provides refined handoff recommendations to the mobile device. This intermediary processes complex data so the device doesn't need to handle all complexity itself.
2Reliability
If handoff is executed based on current signal strength, then the process is quick, but future service quality cannot be guaranteed
Solution Approach 1:
The system performs preliminary analysis of future cell conditions by aggregating historical cell data and predicting upcoming throughput, loading, and interference levels. This allows handoff decisions to be based on predicted future performance rather than current signal strength, ensuring quality without excessive delay.
Solution Approach 2:
The system uses feedback from aggregated historical cell data and predicted future conditions to refine handoff decisions. The cloud server continuously updates cell performance metrics and uses this feedback to recommend optimal handoff targets, ensuring decisions are based on actual service quality patterns rather than instantaneous signal measurements.
3Reliability
If existing solutions use signal-to-noise ratios to create ordered cell lists, then cell priority can be determined, but additional quality parameters like throughput and loading are not considered
Solution Approach 1:
The cloud server serves as an intermediary that handles the complex task of aggregating and analyzing multiple cell parameters (throughput, loading, interference) from various sources. It processes this complex data and delivers simplified handoff recommendations to the mobile device, improving selection accuracy without burdening the device with complex data aggregation requirements.
Solution Approach 2:
The cloud server performs multiple functions: it aggregates cell data from multiple sources, analyzes various quality parameters (throughput, loading, interference), predicts future cell conditions, and generates handoff recommendations. This multi-functional approach consolidates complex operations in one centralized system rather than requiring the mobile device to perform all functions.
Data Source
AI summary
A method and apparatus for providing wireless access to a radio access network for a user equipment (UE) obtains, at a server (102), a plurality UE upload cell messages from a plurality UEs (106) served by different cells (402). Each UE upload cell message includes cell specific data and corresponding UE specific data for a specific cell. The method and apparatus aggregate network cell data based at least on the cell specific data and the corresponding UE specific data (404). For example, the method and apparatus may aggregate the cell specific data and the corresponding UE specific data for each serving cell, such that the observances by the UEs are grouped together according to each serving cell. A UE may then receive cell information that is based on the aggregated network cell data (506) to, for example, re-order a scan list or cell roaming list, or bias a cell measurement.