Wireless Network Cloud System Density-Based Configuration
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Solution Overview
Problem
Traditional cellular networks face significant capital investment and maintenance challenges due to the need for scaling and upgrading custom-built base stations as wireless standards evolve, while wireless network cloud architectures require dynamic control of remote radio heads (RRHs) to maintain optimal service levels.
Innovation Solution
The method involves estimating a density metric for a given coverage area, which includes user and traffic density, to dynamically configure network-access components such as RRHs and virtual base stations, using real-time geographic user location data and forecasting methodologies to control RRHs and allocate compute resources in the cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cellular networks use fixed base stations with custom-built hardware, then network reliability and signal processing capability are maintained, but capital investment and maintenance costs increase significantly when scaling and upgrading
Solution Approach 1:
The base station functionality is segmented into two parts: remote radio heads (RRHs) that handle radio frequency processing and are deployed at cell sites, and a centralized cloud infrastructure that handles base station processing. This segmentation allows the expensive custom hardware to be replaced with commodity hardware in the cloud, reducing capital investment while maintaining reliability through centralized processing.
Solution Approach 2:
The patent replaces the traditional mechanical/custom hardware base stations with a software-based cloud architecture. Custom-built hardware processing is substituted with virtualized software processing in the cloud, enabling flexible resource allocation and reducing the need for expensive physical infrastructure upgrades.
2Ease of manufacture
If wireless network cloud architecture uses remote radio heads with cloud processing, then capital investment is reduced and scalability is improved, but dynamic control of RRHs becomes complex to maintain optimal service levels
Solution Approach 1:
The system continuously monitors network conditions including user density, traffic patterns, and service quality metrics. This feedback information is used to dynamically adjust RRH configurations, activate/deactivate RRHs in response to changing conditions, and optimize resource allocation. The feedback mechanism simplifies control complexity by providing automated decision-making based on real-time conditions.
Solution Approach 2:
The patent dynamically changes operational parameters of RRHs based on estimated user density and traffic conditions. Parameters such as transmission power, antenna configuration, and resource allocation are adjusted in response to changing network conditions, enabling optimal service levels without complex manual control.
3Productivity
If RRHs are dynamically activated and deactivated based on user density, then network costs are minimized and resource efficiency is improved, but service level consistency across different coverage areas becomes challenging
Solution Approach 1:
The system performs preliminary estimation of user density and traffic patterns before making RRH activation decisions. By predicting future network conditions and pre-configuring RRHs accordingly, the system ensures that service levels are maintained while optimizing resource usage. This preliminary action prevents service degradation when RRHs are deactivated.
Solution Approach 2:
The patent implements dynamic control of RRHs based on real-time network conditions. The system can activate additional RRHs when user density increases or deactivate RRHs when demand decreases, while maintaining service level agreements through continuous monitoring and adjustment. This dynamic approach balances resource efficiency with service consistency.
Data Source
AI summary
Techniques for configuring a wireless network cloud system comprise the following steps. A density metric is estimated corresponding to at least one given coverage area of a wireless network cloud system. A configuration is determined for one or more network-access components in the given coverage area of the wireless network cloud system in response to the estimated density metric. The determined configuration may then be applied to the one or more network-access components. The density metric may correspond to a user density and/or a traffic density in the given coverage area.