Application for radio signal coverage calculation
The PieSoft software architects and developers contributed significantly to the Customer's project - an application for a radio signal coverage calculation - by switching it to the distributed system.
The major Customer’s project was a desktop application development for a radio signal coverage calculation for a building/area/room (5G, Wi-Fi).
To work with the application, the user had to build the room plan indicating wall material and place antennas, indicating the model for a particular room. The application calculated the radio signal, considering the room for each particular antenna. Then it built a common prediction for the radio signal room coverage. It allowed choosing the modes for one antenna coverage or all of them in total. All indications were fixed by graphical visualization.
The Сustomer took an order for enormous areas and buildings coverage in Japan. The calculations took a long period of time (a few hours). The scope of area coverage reached several kilometers.
The PieSoft team’s main task was to minimize the calculation time by switching to the distributed system, which resulted in two software development phases:
- Implementing the distributed system for a single antenna signal coverage calculation and for the signal coverage prediction of the total number of antennas.
- Developing a web application for its interaction with a distributed computing system.
On the Customer’s side, micro applications for GPU Computing with OpenCL were developed. They also implemented models of buildings and streets based on photos. PieSoft contribution:
Migrated the calculation on Amazon Web Services (Microsoft Azure was potentially considered)
Created a web application using Spring Framework for the major application and distributed system interaction
Implemented the distributed system using Spark Framework
Languages & frameworks
- Spring WebFlux
- Hadoop HDFS
- Amazon S3
- Azure DevOps
PieSoft team contributed significantly to
the Customer’s project, preparing the existing application for the distributed system. We built software architecture, broke down the user’s primary task into subtasks, which can be calculated on computing units, described
the distributed system’s principles, and optimized big data environments with Hadoop and Spark Frameworks so that the solution could be quickly delivered and run smoothly.
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