Transceiver Deployment Configuration Using Curriculum RL
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Solution Overview
Problem
Existing methods for configuring transceiver deployment in challenging environments, such as buildings with obstructing structures or complex layouts, are labor-intensive and time-consuming, and existing Machine Learning (ML) training processes are inefficient, particularly when using reinforcement learning (RL).
Innovation Solution
Implement a curriculum learning approach with multiple stages of training for ML models, starting with simplified environments and progressively increasing complexity, using reinforcement learning to optimize transceiver deployment configurations efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical testing is used to determine transceiver deployment configurations, then coverage effectiveness can be verified, but the process becomes cost and labour intensive and disruptive to users
Solution Approach 1:
The patent creates virtual copies of the physical environment through digital twins and simulation environments. These virtual models replicate the geometric structure, radio wave propagation characteristics, and environmental conditions of the actual deployment site, allowing comprehensive testing and optimization of transceiver configurations without physical intervention or user disruption.
2Extent of automation
If reinforcement learning is used to train ML models for transceiver deployment, then automated optimization is achieved, but training time and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-training ML models on synthetic data generated from virtual environments and digital twins before actual deployment. This pre-training phase establishes baseline performance and reduces the complexity of subsequent fine-tuning with real-world data, significantly cutting down total training time and computational resource requirements.
Solution Approach 2:
The training process is segmented into multiple phases: initial training on synthetic data from virtual environments, intermediate training on partial real-world data, and final fine-tuning on complete deployment data. This segmentation allows the model to progressively adapt to real conditions while managing computational resources efficiently at each stage.
3Ease of manufacture
If regular spatial configuration is used for transceiver deployment, then installation is simplified, but coverage effectiveness in complex environments with obstructing structures deteriorates
Solution Approach 1:
The patent applies local quality by determining deployment configurations tailored to specific local conditions within the environment. Instead of uniform spacing, transceivers are positioned at optimized locations that account for local obstructions, reflective surfaces, and coverage requirements, with each position calculated based on its unique geometric and electromagnetic characteristics.
Data Source
AI summary
Methods and apparatus for network coverage configuration are provided. A computer-implemented method for configuring network coverage using a ML agent hosting a ML model comprises obtaining first training data and second training data relating to an operating environment. The method further comprises training the ML model in a first training stage using the first training data. When the ML model satisfies a first performance criterion, the first training stage ends, and the ML model is trained in a second training stage using the second training data. When the ML model satisfies a second performance criterion, the second training stage ends. The ML model is then used to generate a deployment configuration for configuring the spatial deployment of a plurality of transceivers to provide network connection capability to the operating environment