Machine-Learning RF Plan Generation for Accurate AP Placement
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Solution Overview
Problem
Manually creating RF plans for network deployment is error-prone and burdensome, leading to inaccurate placement of access points (APs) and suboptimal network performance due to manual errors in capturing and modifying spatial and network features.
Innovation Solution
Utilizing machine learning models to process RF plans, determining candidate AP locations based on modified features, and iteratively adjusting parameters to optimize network metrics such as bandwidth, throughput, and latency, thereby reducing manual intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to create RF plans and determine AP locations, then administrators can deploy networks with visual representation and control, but the process becomes error-prone and burdensome leading to inaccurate AP placement
Solution Approach 1:
The system performs automated RF plan generation and AP location optimization without requiring manual administrator intervention. The machine learning model independently processes network deployment data, captures spatial and network features, and generates optimized RF plans, eliminating manual errors and reducing operational burden while maintaining high accuracy in AP placement
Solution Approach 2:
The manual mechanical process of RF plan creation and AP location determination is replaced with an automated machine learning system. The ML model substitutes human administrators' manual surveying, analysis, and decision-making processes with algorithmic processing that captures spatial features, network features, and generates optimized placements automatically
2Reliability
If manual RF planning processes are used, then administrators can create visual representations and set up network connections, but manual errors occur in capturing and modifying spatial and network features
Solution Approach 1:
The machine learning model incorporates feedback mechanisms that continuously learn from network performance data and spatial feature information. The system captures spatial features and network features accurately, processes them through the ML model, and uses performance feedback to refine and update the RF plans, ensuring that accurate information is maintained and manual errors are corrected through iterative learning
Solution Approach 2:
The system creates accurate digital copies of the physical network deployment environment by capturing spatial features (walls, floors, ceilings) and network features (AP locations, client devices, bandwidth usage). These digital representations are processed by the ML model to generate optimized RF plans that accurately reflect the intended network configuration without manual errors
3Productivity
If automated machine learning models are used to generate RF plans, then AP locations are optimized and errors are reduced, but the system complexity increases
Solution Approach 1:
The machine learning system is designed as a universal platform that performs multiple functions: capturing spatial features, capturing network features, generating RF plans, optimizing AP locations, and evaluating network performance. This multi-functional approach consolidates what would otherwise require multiple separate tools and processes into a single automated system, improving productivity while managing complexity through integration
Solution Approach 2:
The system optimizes RF plans by dynamically adjusting parameters such as AP locations, bandwidth allocation, and network resource distribution based on spatial and network features. The ML model changes these parameters iteratively to maximize network performance metrics, achieving high productivity through automated parameter optimization rather than manual configuration
Data Source
AI summary
Examples described herein relate to generation of radio frequency (RF) plans for network deployments. Examples described herein may receive an input RF plan with modified set of features of a network deployment area. A first machine learning (ML) model generates an intermediate RF plan indicating candidate AP locations based on the modified set of features and a first set of parameters. A second ML model determines a network optimization score for the intermediate RF plan. Based on the optimization score, the first set of parameters are optimized. The first ML model generates an output RF plan indicating optimized AP locations based on the optimized first set of parameters and the modified set of features.