Capacity Decongestion Engine for Wireless Cell Optimization
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Solution Overview
Problem
Cellular networks face congestion issues due to overloaded cells, leading to reduced quality of service, queueing delays, packet loss, and blocking of new connections, which are costly to mitigate through traditional methods like deploying additional high-speed links or adding new cells.
Innovation Solution
A capacity decongestion engine processes data traffic volume, existing and planned cell sites, and alternative bandwidth to identify optimal locations for new cell sites, calculating expected traffic offloads and determining the best placement to alleviate congestion within the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional dedicated high-speed links or new cells are deployed to overloaded cells, then network capacity and service quality are improved, but deployment costs and system complexity increase
Solution Approach 1:
The system enables self-service by automatically analyzing network traffic data, identifying overloaded cells, calculating optimal offloading strategies, and generating deployment recommendations without requiring manual intervention from network operators. The automated analysis engine processes network data and produces actionable insights that reduce both complexity and costs.
Solution Approach 2:
The system performs preliminary action by proactively identifying cells that are likely to become overloaded before congestion occurs. By analyzing historical traffic patterns and predicting future load conditions, the system enables preventive planning and optimization, allowing operators to address potential bottlenecks before they degrade service quality.
2Productivity
If traditional methods of deploying additional high-speed links or new cells are used, then network congestion is mitigated, but deployment costs increase
Solution Approach 1:
The system applies parameter changes by optimizing existing network parameters such as traffic routing, cell power levels, and resource allocation before considering physical deployments. By adjusting these parameters, the system can achieve capacity improvements without the high costs associated with deploying new infrastructure, thereby reducing deployment costs while maintaining or improving network capacity.
Solution Approach 2:
The system implements partial action by focusing optimization efforts on specific overloaded cells and their immediate surroundings rather than undertaking comprehensive network-wide deployments. This targeted approach achieves sufficient capacity improvements in critical areas without the excessive costs of universal infrastructure expansion.
3Ease of manufacture
If existing network resources are efficiently utilized through automated analysis, then traffic offloading is optimized and deployment costs are reduced, but measurement and detection complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional automated analysis engine that can handle multiple tasks: collecting network data, identifying overloaded cells, calculating traffic offloading strategies, evaluating deployment options, and generating recommendations. This single unified system performs what would otherwise require multiple separate tools and manual processes, reducing overall system complexity despite the sophisticated analysis performed.
Data Source
AI summary
Provided are a system and method of decongesting a highly utilized cell within a sector of a wireless telecommunications network. The method includes receiving a set of reference samples taken from a plurality of points within the wireless telecommunications network within a predetermined time period, calculating, based on the set of reference samples, a first data volume comprising a sum of a total data volume for all highly utilized cells within the sector containing the highly utilized cell, calculating, based on the set of reference samples, a first total expected traffic offload value, determining whether the first total expected traffic offload value is greater than a predetermined percentage of the first data volume, and based on determining that the first total expected traffic offload value is greater than the predetermined percentage of the first data volume, generating an output for decongesting the cell.