RSaaS: Radio Signal as a microService

COVID-19 has had a profound impact on mobile network operations: 1) network loads have surged far beyond capacity; 2) network energy consumption has increased, resulting in a higher CO2 emission; and 3) engineers have limited access to sites, in particular, for in-building wireless networks, which has caused significant problems on network deployment and maintenance.

The above points emphasise that: 1) mobile networks must be able to adapt to changes; 2) the planning, commissioning, and operations of radio access networks, need to be automated so that the number of physical site visits, can be minimised.

To this end, many what-if scenarios need to be carefully evaluated before changes of network parameters are commissioned, e.g., how the changes of network parameters will impact the network coverage. Radio propagation models play a central role in evaluating these what-if scenarios. Furthermore, network automation calls for interactions between radio signal prediction with an eco-system of data analytics, optimisation, and operations support system (OSS) software tools, which makes a migration to a cloud computing platform based on microservices, imperative.

In this project, we will enhance Ranplan's **world first** combined indoor-outdoor radio propagation engine with machine learning algorithms and implement the new radio propagation engine as a **microservice** in a cloud-computing platform. The first part of this innovative approach will lead to a universal radio signal (or interference) prediction engine that works in **all scenarios** (indoor-outdoor, millimetre waves, etc), while the second part will make it **universally available** so that it can easily interact with other services and make use of the vast computing power on the cloud. develop a stochastic radio propagation model based on

Mobile networks are a key enabler for economy, social connectivity, remote working/ teaching. As the use of network increase, so does energy consumption. Recent studies suggest that mobile networks will produce 320 million Tonnes of CO2 by the end of the current decade. Ranplan believes that the proposed project forms a foundation to enable network operation automation, resulting in a substantial reduction of use of equipment and CO2 emissions.