Automated Load Balancing for 5G Mobility Networks
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Solution Overview
Problem
Current cellular communication systems face challenges in balancing traffic load across frequency carriers, leading to poor user experience due to bottlenecks, as existing solutions are not designed to optimize overall user throughput and service providers lack direct access to vendor proprietary functions for corrective actions.
Innovation Solution
An automated load balancing system that iteratively adjusts handoff rates between cells using 3GPP standards-compliant parameters to redistribute traffic, optimizing user equipment throughput by identifying candidate cell pairs and relocating traffic based on estimated throughput improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vendor-based load balancing solutions are implemented, then traffic distribution is improved, but service providers lack direct access to corrective actions due to proprietary functions
Solution Approach 1:
The patent introduces a standardized interface layer that acts as an intermediary between the service provider's optimization system and the vendor's load balancing functions. This interface uses standard 3GPP parameters (CIO, qOffset) to communicate handoff rate adjustments, allowing the service provider to control load balancing without direct vendor access, thus resolving the contradiction between improved traffic distribution and operational accessibility.
2Ease of operation
If manual tuning of network settings is performed, then some load imbalance can be alleviated, but the process is qualitative and requires multiple trial and error iterations
Solution Approach 1:
The patent implements an automated feedback loop where the system continuously monitors traffic load metrics (DL/UL volume, connection count, PRB usage) across cell pairs, calculates throughput improvement estimates, and automatically adjusts handoff rates. This closed-loop feedback mechanism eliminates manual trial-and-error iterations by using quantitative measurements to drive real-time optimization decisions.
Solution Approach 2:
The system automatically changes network parameters (specifically CIO and qOffset values) based on calculated throughput improvement estimates. By systematically adjusting these handoff control parameters according to measured traffic conditions and predicted performance gains, the system replaces qualitative manual tuning with quantitative automated parameter optimization.
3Productivity
If existing resource allocation algorithms are used, then cell selection is determined, but overall user throughput is not optimized due to carrier load imbalance
Solution Approach 1:
The patent segments the network into cell pairs for individual optimization analysis. Each cell pair is evaluated separately based on traffic load metrics and throughput improvement potential, allowing the system to manage complexity by breaking down the overall network optimization problem into manageable discrete units rather than attempting to optimize the entire network simultaneously.
Solution Approach 2:
The system implements dynamic load balancing by continuously monitoring traffic conditions and adjusting handoff rates in real-time. The optimization is not static but adapts to changing network conditions, with the system re-evaluating cell pairs and adjusting parameters as traffic patterns evolve, thereby optimizing user throughput dynamically rather than relying on fixed algorithms.
Data Source
AI summary
The disclosed technology is directed towards load balancing in an adaptive and automated way for wireless mobility networks to improve the overall harmonic-average UE throughput within each controlled group of cells (e.g., different frequency carriers serving a sector of a base station). A load balancer (e.g., analytics component) obtains various device traffic data including throughput data for cells of a group. Pairs of cells in a group (sharing a site and face) can be selected based on satisfying various criteria, with estimated throughput gain achieved by changing the handoff rates between the cell pairs. The technology iteratively repeats the overall process, driving a system to an optimal equilibrium.