Modular Network Planning Engine for 5G Deployment ROI
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Solution Overview
Problem
Current network planning methods for advanced wireless communication systems like 5G and 6G face challenges in optimizing radio access network capacity and coverage due to limited resources and rising demands, lacking a holistic approach to maximize investment return while adhering to budget constraints.
Innovation Solution
A modular optimization system that uses a planning engine to determine optimal deployment scenarios for network equipment by modeling traffic distribution and user performance, selecting deployment conditions that maximize return on investment (ROI) without exceeding budget limits, and employing greedy algorithms to simplify complex optimization problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If new network resources (nodes) are placed to increase capacity and coverage, then network performance is improved, but deployment cost increases
Solution Approach 1:
The system performs preliminary network assessment and planning before actual deployment. It evaluates current network state, predicts future demands, and pre-determines optimal node placement locations and deployment scenarios, allowing operators to prepare and execute deployments more efficiently and cost-effectively
Solution Approach 2:
The system analyzes multiple deployment parameters and scenarios (e.g., different node types, locations, deployment timings) to determine optimal configurations. It changes and evaluates various parameters to find the best balance between capacity/coverage improvements and deployment costs
2Productivity
If comprehensive network optimization is implemented to maximize ROI, then investment return is improved, but planning complexity increases
Solution Approach 1:
The planning system is divided into modular components: network assessment module, demand prediction module, scenario generation module, and optimization module. Each segment handles a specific aspect of the complex optimization process, making the overall system more manageable and easier to implement
Solution Approach 2:
The system introduces intermediate computational models and simulations that bridge the gap between network planning and optimization. These intermediaries translate complex network parameters into actionable insights and recommended deployments, simplifying the decision-making process
3Speed
If short-term network upgrades are prioritized to meet immediate demands, then immediate network performance is improved, but long-term optimization is compromised
Solution Approach 1:
The system performs forward-looking demand prediction and long-term network planning before implementing upgrades. By assessing future traffic patterns and network evolution needs in advance, it ensures that deployed solutions provide both immediate relief and long-term value, avoiding short-sighted decisions
Solution Approach 2:
The optimization system operates continuously, constantly monitoring network performance, updating demand predictions, and adjusting deployment recommendations. This continuous optimization ensures that short-term upgrades align with long-term strategic goals, maintaining balanced network development over time
Data Source
AI summary
Facilitating implementation of communication network deployment through network planning in advanced networks (e.g., 5G, 6G, and beyond) is provided herein. Operations of a system can include, configuring a first deployment scenario for first network equipment and a second deployment scenario for second network equipment. The first deployment scenario is selected from a group of first deployment scenarios and can include a first parameter. The second deployment scenario is selected from a group of second deployment scenarios and can include a second parameter. The configuring can include determining that a sum of the first parameter and the second parameter satisfies a function of a defined parameter level. The operations also can include facilitating a first enactment of the first deployment scenario for the first network equipment and a second enactment of the second deployment scenario for the second network equipment.