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How to Leverage AI and Machine Learning Development Services to Improve 5G Experience? 



The future of wireless networks consists of multiple access networks and frequency bands and cells. The overlapping coverage areas cause wireless operators some challenges in network planning and deployment. Hence, companies look for ways to improve the function. And, using AI and machine learning development services can help operations manage these challenges.   

To do so, they must analyze the geographic data, engineering parameters, and historical logs to:  

  • Determine peak traffic, resource uses, and types of applications
  • Moreover, optimize the network parameters for capacity expansion.
  • In addition, get rid of coverage holes through interference measurements and inter-site distance information.

5G networks will play a key role in enabling AI and ML integration into the network edge. The use of AI and ML in 5G will help operators offer the following to users:  

  • High automation using distributed AI and ML architecture at the network edge.
  • Moreover, traffic steering and aggregation based on application throughout the heterogeneous access networks.
  • Furthermore, dynamic network slicing to serve varied use cases.
  • And, lastly, Offering AI and ML as a service to the end users.

Use of AI and Machine Learning Development Services in Beamforming  

5G is deployed with the help of mm-wave, which comprises beam-based cell coverage. Therefore, it is significantly different from 4G as it follows sector-based coverage. In addition, The machine learning algorithm will help the 5G cell site compute a set of candidate beams and that start from the serving or neighbouring cell site.   

To get the ideal set, you need to focus on a set with fewer beams because it has a higher chance of containing the best beam.   

AI ML Development For 5G

What is Best Beam? The best beam is the one with the highest signal strength, also known as RSRP. 

To improve your chances of getting the best beam, you need more activated beams. But, it can increase the system’s resource consumption.  

The User Equipment (UE) is available to measure and report on Beam State Information (BSI). It depends on the Beam Reference Singal (BRS) that considers parameters like Beam Index (BI), and Beam Reference Signal Received Power (BRSRP).   

The use of AI and ML development services can help find the best beam. Using the instantaneous values at each UE measurement using the parameters below:

  • Beam Index (BI)
  • Distance (of UE to serving cell site),
  • Beam Reference Signal Received Power (BRSRP)
  • Speed (UE mobility)
  • Position (GPS location of UE)
  • Channel quality indicator (CQI)
  • Historic values

Massive MIMO using AI and ML  

5G technology relies on Massive MIMO for improved functioning. In this case, massive refers to several antennas (32 or more logical antenna ports) that are available in the base station antenna array.  

Massive MIMO through AI helps improve user experience by elevating throughput, network capacity, and coverage, simultaneously reducing interference.  

  • It helps serve several spatially separated users using an antenna array at the same time through the use of frequency resources.
  • It also allows the operators to serve specific users using beam forming steering. The process sends a narrow beam with high gains to the radio signals and information straight to the device without broadcasting it to the entire cell. Therefore, leading to reduced radio interference across the cell.

To maximize the beamforming effect, the weights for antenna elements of a massive MIMO 5G cell site are critical. AI and ML can be used here for: 

  • Create forecasts of user distribution. Base the forecast on historical data and identify any dynamic changes.
  • Additionally, optimize the weights of antenna elements through the use of historical data.
  • Furthermore, carry out adaptive optimization of weights for special use cases for specific user distribution.
  • And finally, improve coverage in case of multi-cell while keeping inter-site interference in consideration.

AI and ML Development in Network Slicing  

The one-size-fits-all approach to wireless networks leads to severe under-utilisation of resources. Hence, it is not an efficient approach to use the network resources.  

Network Slicing

In contrast, Network Slicing creates several dedicated virtual networks with the help of physical infrastructure. Consequently, it enables independent management and orchestration of each network slice.  

Embedding AI and ML algorithms in 5G networks can improve automation and adaptability, hence allowing better efficiency in orchestration and dynamic provisions of the network slice. Above all, the AI ML Development uses the following information for multidimensional analysis of each network slice:  

  • User Subscription
  • Network Performance
  • Events and Logs
  • Quality of Service (QoS)

Leverage the Artificial Intelligence and Machine Learning Development Services for:

  • Predicting network resources helps operators anticipate network outages, performance degradation, and equipment failure.
  • Additionally, it can help with cognitive Scaling for dynamic modification of network resources based on predictive analysis.
  • Lastly, the technology leads to better security leads to fewer attacks and frauds through user pattern recognition.

To manage better service, 5G operators need to manage several KPIs, and AI ML development can help with the job.

Machine Learning Development Services – To Fulfil All Your 5G Needs

In conclusion, the next-gen wireless networks will use artificial intelligence and machine learning development services to create an ideal usage ecosystem. After that, users will have the chance to utilise their network better.

Get in touch with MoogleLabs to help you find the best algorithms to support 5G deployments that can benefit your industry.