Data-Driven Network Roll-Out Planning Optimization
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Solution Overview
Problem
Conventional roll-out planning methods for mobile telecom carriers are costly and inefficient, struggling to maximize network capacity while managing increasing data traffic and mobility demands, especially with limitations in WLAN range and lack of seamless handover between areas.
Innovation Solution
A data-driven approach using predictive algorithms and carrier aggregation techniques to optimize network roll-out planning, incorporating cumulative distribution functions and probability mass functions for accurate data traffic predictions and resource allocation across multiple cells, allowing for sector-by-sector analysis and cost-effective capacity adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional roll-out planning methods are used, then implementation is simpler, but cost increases and efficiency decreases
Solution Approach 1:
The patent replaces conventional mechanical/manual roll-out planning methods with data-driven algorithms and predictive models. The system uses automated data collection from network counters, predictive algorithms for traffic growth, and optimization algorithms to generate roll-out plans, substituting manual analysis and decision-making processes with computational systems that improve efficiency while maintaining ease of use through automated workflows.
2Reliability
If network capacity is increased to meet growing data traffic demands, then service quality improves, but cost increases
Solution Approach 1:
The patent changes the parameter of network capacity planning from static, over-provisioned capacity to dynamic, demand-driven capacity. By using predictive algorithms to forecast traffic growth and optimization algorithms to determine optimal capacity deployment, the system adjusts network capacity parameters to match actual service quality requirements, avoiding both over-provisioning and under-provisioning.
Solution Approach 2:
The patent implements feedback loops where network performance data and traffic patterns are continuously monitored, fed into predictive models, and used to adjust roll-out plans. The system compares predicted traffic demands with actual network capacity, uses the discrepancy as feedback to refine capacity allocation decisions, and iteratively optimizes the balance between service quality and cost.
3Measurement precision
If data-driven predictive algorithms are implemented for accurate traffic predictions, then roll-out planning precision improves, but system complexity increases
Solution Approach 1:
The patent segments the complex data-driven system into distinct functional modules: data collection from network counters, predictive algorithm processing, optimization algorithm computation, and plan generation. Each module handles specific tasks independently, reducing overall system complexity while maintaining high prediction accuracy. The segmentation allows for modular implementation and easier maintenance of the sophisticated predictive capabilities.
Data Source
AI summary
Methods and systems are provided for data-driven network roll-out planning. Network data associated with a network that includes a plurality of cells may be analyzed, with the analyzing including assessing throughput of users on a sector-by-sector basis in the network. Prioritization information on per-sector basis for the network may be generated, and based on the prioritization information, a network roll-out plan for use in the network may be optimized. The prioritization information may be generated based on a valuation scheme and/or a congestion level. Cost optimization information, for use in the optimizing of the network roll-out plan, may be generated. One or more actions may be recommended based on the network data.