RAN Site Handover Using RF Steering for Backhaul Limits
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In low-cost 5G and LTE networks, reducing the capacity of the transport network for cost savings can lead to insufficient backhaul resources, causing connectivity issues when the load exceeds the capabilities of the backhaul network, especially during high-demand situations or emergencies.
Innovation Solution
Modify RF signals using beamforming and other techniques to cause user equipment to handover from a base station with insufficient backhaul resources to one with sufficient resources, based on backhaul capacity rather than RF coverage, using factors like traffic load, location, and priority levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If radio access network site parameters are configured using static templates, then configuration speed is improved, but network performance optimization is worsened
Solution Approach 1:
The patent implements dynamic parameter adjustment by transitioning from static templates to real-time optimization. The system continuously monitors network performance metrics and automatically adjusts site parameters based on current conditions, making the configuration adaptive rather than fixed. This resolves the contradiction by enabling both rapid initial deployment (through template-based provisioning) and ongoing performance optimization (through dynamic adjustment mechanisms).
Solution Approach 2:
The system establishes a closed-loop feedback mechanism where network performance is continuously monitored and fed back to the optimization engine. This feedback drives automatic parameter adjustments, allowing the system to learn from performance data and improve network outcomes over time while maintaining efficient configuration processes.
2Reliability
If manual parameter optimization is performed, then network performance is improved, but operational complexity is worsened
Solution Approach 1:
The patent implements self-service automation where the system performs parameter optimization autonomously without requiring manual intervention. The optimization engine automatically analyzes performance data, determines optimal parameter settings, and applies adjustments independently. This eliminates the need for specialized manual optimization while maintaining or improving network performance, thereby reducing operational complexity.
Solution Approach 2:
The system replaces manual mechanical optimization processes with automated computational algorithms. Instead of relying on human operators to manually adjust parameters, the patent employs software-based optimization engines that use data analysis and decision algorithms to automatically configure and tune network parameters, substituting human labor with automated intelligence.
3Ease of operation
If site parameters are not optimized, then operational simplicity is maintained, but energy efficiency is worsened
Solution Approach 1:
The system dynamically changes operational parameters based on real-time network conditions and performance requirements. By adjusting parameters such as transmission power, resource allocation, and scheduling configurations, the optimization engine improves energy efficiency without requiring complex manual intervention. The changes are automated and adaptive, maintaining operational simplicity while achieving better energy utilization.
4Adaptability or versatility
If frequent parameter adjustments are made, then network adaptability is improved, but system stability is worsened
Solution Approach 1:
The system implements periodic monitoring and adjustment cycles with built-in thresholds and hysteresis mechanisms. Rather than making continuous adjustments, the optimization engine evaluates performance at defined intervals and only makes changes when specific conditions are met. This periodic approach with controlled frequency maintains system stability while still achieving necessary adaptability to changing network conditions.
Solution Approach 2:
The system incorporates stabilization mechanisms that cushion against excessive or premature parameter changes. By setting adjustment thresholds, cooldown periods, and validation checks, the system prevents erratic fluctuations and ensures that parameter changes are made only when truly necessary. This beforehand cushioning maintains stability while preserving the ability to adapt when needed.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Techniques for dynamic optimization of radio access network sites are provided. A wireless communications infrastructure identifies that a piece of user equipment(UE) is connected to a base station associated with a current Radio Access Network(RAN) site that does not have sufficient backhaul resources to support the piece of UE. A target RAN site that has sufficient backhaul resources to support the piece of UE is identified. The target RAN site associated with at least one base station. A Radio Frequency (RF) signal transmitted from at least one of the base station associated with the current RAN site and the at least one base station associated with the target RAN site is modified. The modification causes the piece of UE to handover from the base station associated with the current RAN site to the at least one base station associated with the target RAN site.