Transceiver Deployment Configuration Using Staged ML Training
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Solution Overview
Problem
Existing methods for configuring transceiver deployment in challenging environments, such as buildings with obstructing structures, are labor-intensive and time-consuming, and existing machine learning training processes are inefficient.
Innovation Solution
Utilize a curriculum learning approach with multiple stages of training for machine learning models, starting with simplified environments and progressively increasing complexity, combined with reinforcement learning to optimize transceiver deployment configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical testing is used to determine transceiver deployment configuration, then coverage effectiveness can be verified, but the process becomes cost and labour intensive and disruptive to users
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical environment including obstacles and transceivers. This digital model allows simulation and verification of deployment configurations without physical testing, eliminating the need to disrupt actual users while maintaining coverage verification capability.
Solution Approach 2:
The patent introduces a ray-tracing simulation as an intermediary between deployment configuration and coverage verification. This simulation acts as a mediator that predicts coverage outcomes without requiring physical testing, thus avoiding disruption to users while still verifying effectiveness.
2Ease of operation
If regular spatial configuration is used for transceiver deployment, then deployment is simple and systematic, but coverage effectiveness in environments with obstructing structures is reduced
Solution Approach 1:
The patent optimizes transceiver positions locally based on environmental characteristics. Instead of uniform spacing, each transceiver position is individually adjusted according to local obstacle distributions and coverage requirements, achieving both effectiveness and reasonable deployment simplicity.
Solution Approach 2:
The patent performs preliminary simulation and optimization of transceiver positions before actual deployment. By pre-calculating optimal positions using ray-tracing simulations, the system avoids trial-and-error physical testing while ensuring coverage effectiveness in complex environments.
3Reliability
If ML model training uses complex operating environments from the start, then the model learns realistic scenarios, but training time and computational resources increase significantly
Solution Approach 1:
The patent segments the training process into multiple stages with increasing complexity. Training begins with simplified environments and progressively introduces more complex scenarios, allowing the model to build foundational knowledge before learning intricate patterns, thereby reducing overall training time.
Solution Approach 2:
The patent performs preliminary training on simplified environments before transitioning to complex realistic scenarios. This preliminary action allows the model to converge faster on basic patterns, reducing the total training time required when eventually training on complex environments.
Data Source
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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