Blare Technologies implements a project co-financed from European Funds:
An intelligent system for analyzing and optimizing 5G network plans using neural networks (deep machine learning) and expert algorithms.
The aim of the project is to develop an IT solution for professional analysis and optimization of 5G network plans. The result of the project will be an intelligent system using AI methods, including neural networks (deep machine learning) and expert algorithms.
Co-financing from Europen Funds - PLN 4 672 041,67
5G networks require a completely different approach than previous standards and millimeter waves (mmWaves) are the reason for it. They’re enabling incredible data throughput, but the problem is, their range is much shorter, and they’re more influenced by transmission environment, which makes implementation much more complicated and costly. The goal of our project was to make 5G network planning much more manageable and efficient. In order to achieve it, we created a cloud-based simulation tool in voxel space with a precisely recreated 5G mmWave propagation model.
Compared to previous standards, 5G is revolutionary in terms of speed and potential applications far beyond those of current cellular networks. However, this new technology comes with different challenges in terms of network planning. 5G planning tools require much more data in order to work efficiently. We decided to make the process easier and more cost-effective by using machine learning and artificial intelligence.
The most significant difference is in frequency, and that’s what makes planning so challenging. 5G NR (New Radio) networks use the new millimeter-wave spectrum from 2 GHz to even 52,6 GHz and reuse 4G/LTE spectrum part at the 700-3800 MHz range.
The higher frequency of 5G provides larger frequency channels what means significantly increased data throughput, but it’s more susceptible to environment influence and has a much shorter range than 4G. Factors that were irrelevant before, like heavy rain and snow, road traffic, or vegetation, play a significant role in 5G network planning. This is why one of the critical steps was understanding how exactly mmWaves behave.
Additionally, in order to create 5G networks cost-effectively, the planning phase must include recreating the entire 3D spatial environment of a given area in the form of a Digital Twin, which updates all relevant data in real-time. However, even with the mmWave propagation model and a precise 3D area model, the planning process takes time and requires skilled specialists to test different planning scenarios. Substantial improvement in time and quality of planning is predicted within our tool.
The demand for 5G networks is already very high, but it’s only going to grow in the next few years. It’s not only wireless carriers who are interested. The connectivity that the new generation provides has already attracted the interest of local governments, public sector agencies, and private companies that specialize in wireless network implementations. For example, 5G networks are a significant piece of many Smart City transformations.
Because of that, a modern 5G network planning tool has to create efficient networks in terms of bandwidth and reliability, but also when it comes to cost and use of energy. It has to be much more eco-friendly, accessible, and affordable.
The number of industries that are planning to utilize 5G NR technology creates even more challenges. Because mmWave-based networks require a large number of antennas, there’s a risk of exceeding legal norms of electromagnetic radiation. In order to avoid that, a tool must be able to factor in an electromagnetic field (EMF) of existing networks from previous generations (2G, 3G, 4G) and simulate placement for antennas according to norms, without the need to make actual measurements in the field.
The 5G standard was introduced in 2016, but deployment challenges were standing in the way of a worldwide revolution. The introduction of machine learning and AI is a huge step towards simplified and affordable 5G network planning for any industry.
Given the complexity of 5G network planning, we realized that traditional methods and tools currently available on the market are completely insufficient moving forward. The process is complex, expensive, time-consuming, and requires professional expertise. Our goal was to make it as seamless as possible, so we knew that we had to automate anything that could be. We decided to integrate elements of machine learning and AI to help us with analysis and simulations.
Given the planning challenges associated with the mmWave propagation model, it was clear to us that we had to use AI to test and analyze countless possible planning scenarios for a given area. Our role in the process is to create a Digital Twin and set the starting parameters such as bandwidth requirements and legal regulations. We also set site candidates, which are possible placements for antennas such as lamps, traffic signs, or state-owned buildings. Once the tool has all of the necessary information, it can automatically analyze different placement scenarios, searching for the one that’s most reliable and energy-efficient.
Using our tool we have proven that 5G network planning can be much easier, cheaper, and therefore accessible for anyone. If you’re interested in creating a fast mmWave-based network, we’d be happy to help you.
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