Radio Access Network Measurement Result Routing Across Base Stations
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Solution Overview
Problem
Existing wireless communication networks face challenges in efficiently managing and optimizing the use of radio resources, particularly in heterogeneous networks with varying cell sizes and traffic loads, leading to suboptimal performance and resource utilization.
Innovation Solution
Implementing a flexible and configurable radio access network architecture that utilizes artificial intelligence and machine learning to dynamically manage radio resources based on network conditions, device capabilities, and traffic characteristics, enabling adaptive resource allocation and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional static resource allocation is used in heterogeneous networks, then network architecture is simple, but resource utilization is suboptimal and network performance deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation where the network gNodeB continuously monitors channel conditions, device capabilities, and traffic loads, then adapts resource allocation in real-time. This transforms the static resource allocation into a dynamic system that responds to changing network conditions, improving resource utilization and network performance while managing complexity through structured adaptation mechanisms.
Solution Approach 2:
The system changes multiple parameters simultaneously including bandwidth allocation, modulation schemes, coding rates, and power distribution based on real-time measurements of channel quality indicators (CQI), signal-to-interference-plus-noise ratio (SINR), and traffic demand. These parameter adjustments enable the network to optimize performance for diverse device types and traffic patterns without requiring complete architectural redesign.
2Productivity
If homogeneous cell sizes are used, then network deployment is simple, but traffic load balancing is poor and resource utilization is inefficient
Solution Approach 1:
The patent implements heterogeneous cell deployment with different cell sizes (macrocells, microcells, femtocells) deployed in appropriate locations based on local traffic demands and coverage requirements. Each cell type is optimized for its specific function: macrocells for wide coverage, microcells for medium-density areas, and femtocells for high-density hotspots. This local optimization improves overall resource utilization while maintaining deployment feasibility through standardized interfaces and procedures.
3Adaptability or versatility
If manual resource allocation is used, then system complexity is low, but adaptability to varying traffic loads and device capabilities is poor
Solution Approach 1:
The system implements continuous feedback loops where the gNodeB monitors channel quality indicators (CQI), signal-to-interference-plus-noise ratio (SINR), buffer status, and device capabilities, then uses this feedback to dynamically adjust resource allocation decisions. This feedback mechanism enables automatic adaptation to varying traffic loads and device capabilities without manual intervention, improving versatility while managing complexity through established feedback control procedures.
Solution Approach 2:
The network system performs self-optimization by automatically monitoring its own performance metrics, identifying bottlenecks and opportunities for improvement, and adjusting resource allocation without external intervention. The gNodeB autonomously makes decisions about bandwidth allocation, scheduling priorities, and parameter optimization based on real-time network state, reducing the need for manual configuration while enhancing adaptability.
4Productivity
If fixed bandwidth allocation is used, then resource management is simple, but data throughput is limited under varying traffic conditions
Solution Approach 1:
The patent implements dynamic bandwidth allocation where the available spectrum is continuously divided and allocated to different devices and services based on real-time traffic demand, channel conditions, and QoS requirements. This dynamic approach allows the system to allocate more bandwidth to high-priority traffic or devices with poor channel conditions, maximizing overall data throughput while managing allocation complexity through structured scheduling algorithms.
Data Source
AI summary
A wireless device receives, from a first base station, one or more first messages comprising a measurement configuration, an identifier of the wireless device, and destination information of measurement results associated with the measurement configuration. The destination information comprises an identifier of the first base station. The wireless device transmits, to a second base station, one or more second messages comprising the measurement results, the identifier of the wireless device, and the destination information.