Wireless Network Layout via Scenario Sampling
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Solution Overview
Problem
Conventional stochastic models fail to accurately emulate the distribution of user devices in practical geometries, particularly in millimeter wave (mmWave) communication networks, which is critical for 5G systems, leading to inefficient base station deployment.
Innovation Solution
A method involving measuring user-device placement at multiple times to gather realizations, selecting a subset of samples using scenario sampling, and solving a base station deployment model to determine optimal base station locations, ensuring quality of service for a majority of user devices with reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional stochastic models (e.g., Poisson point process) are used to emulate user device placement, then the modeling process is mathematically tractable and simple, but the accuracy of emulating practical user device distribution is poor
Solution Approach 1:
The patent uses measured realizations of user device placement as copies of actual practical distributions rather than relying on theoretical stochastic models. By collecting K realizations through measurement and selecting N samples from these real measurements, the system accurately replicates practical user device distribution patterns while avoiding the inaccuracy of conventional stochastic modeling approaches.
2Measurement precision
If numerous realizations of user device placement are measured and analyzed, then the accuracy of reflecting practical distribution improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent extracts a representative subset of N realization samples from the complete set of K measured realizations. This extraction process selectively takes out only the necessary samples needed to accurately represent the practical distribution, thereby maintaining high accuracy while significantly reducing the computational burden of processing all K realizations.
Solution Approach 2:
The patent applies partial action by using N samples rather than all K realizations. By selecting a sufficient but not exhaustive subset of realizations, the system achieves the necessary accuracy for practical deployment while avoiding the excessive computational complexity that would result from processing the complete set of measurements.
3Reliability
If base station deployment is optimized for stochastic models, then the deployment process is computationally efficient, but the quality of service for actual user devices cannot be guaranteed
Solution Approach 1:
The patent performs preliminary measurement and characterization of actual user device placement patterns before optimizing base station deployment. By first collecting K realizations of measured user device placement and selecting N representative samples, the system establishes an accurate foundation that ensures subsequent deployment optimization will guarantee quality of service for actual user devices rather than theoretical models.
Data Source
AI summary
A method for determining a layout of a wireless communication network is provided in the present invention. Numerous realizations of user device placement in a considered geometry are measured to reflect the practical distribution of the user devices in a more accurate way than the conventional approach which emulates the randomness of placements of user devices using a tractable stochastic process. Moreover, a scenario sampling approach is used to provide a lower-complexity and higher efficient way to yield optimal base station deployment results while guaranteeing the quality of service of a specified majority of the overall user device realizations.