Within the next decade, operators around the world will embark on a massive infrastructure project – moving from fourth to fifth generation mobile networks. To achieve lightning-fast speeds, low latency data transmission and the capacity to simultaneously support a massive number of devices and connections – carriers need to make pricey up-front commitments to new wireless technologies.
The rollouts for 5G will be expensive, but analytics and machine learning can help operators plan their 5G rollouts in the most effective and customer-centric ways. Here are three ways how:
1Troubleshooting 5G network performance
5G is currently being rolled out in trials and early deployment in major city centers across the globe. Companies such as Nokia are working with operators and collecting data from early 5G deployments to see how AI can be used to understand device performance.
The good news is operators have a lot of data they can analyze to improve performance: mobility, retainability, radio signal quality, accessibility and throughput. Over time, they can train machines to analyze real-time data sets and automate resolutions. This allows them to proactively address network quality problems like network optimization and provides real-time notifications for human intervention when required.
By collecting and assessing multi-dimensional metrics per user device and category (i.e new 5G-supported smartphones, tablets, laptops) operators can use AI and machine learning to reduce the time for network resolution, improve 5G service quality, and enhance the productivity of nationwide 5G rollout strategies.
2Scheduling beamforming in massive MIMO networks to maximize capacity and coverage investments
5G is set to connect everything at data rates up to 20 Gbps with a 100 times increase in capacity. New antenna technologies known as massive Multiple Input Multiple Output (MIMO) helps to serve multiple users – and multiple devices – simultaneously within a confined area while maintaining fast data rates and consistent performance.
Beamforming is a traffic-signaling system that identifies the most efficient data delivery routes to a particular user, reduces interference for nearby users and maintains more efficient use of the radio frequency spectrum. One of the challenges of rolling out 5G is deciding to get the highest spectral efficiency – a measurement of how many bits per second a base station can send to a set of users.
Through the use of AI and ML, operators can train their network to predict the best schedules on demand. This optimizes vertical and horizontal beamforming from massive MIMO antennas to enhance radio capacity and coverage without additional infrastructure investment.
The net result of using signal data with AI is predicting where 5G devices will be used and automatically adjusting the signal strength to accommodate increasing demand in specific areas.
3Improve positioning of indoor base stations
Many operators are using miniature 5G base stations known as small cells indoors to make more efficient use of the 5G spectrum. This also helps the network to find the location of indoor devices such as sensors and other IoT equipment.
By measuring the received signal strength from each miniature base station from multiple points in a room, operators can train neural networks to predict the location of devices based on the strength of the signal from nearby cells. The net result of using signal data with AI is predicting where 5G devices will be used and automatically adjusting the signal strength to accommodate increasing demand in specific areas.
Transitioning to a 5G world is considerably more complex than the transitioning to previous generations of wireless technology. Thankfully, recent advancement in AI and machine learning can provide a variety of cost-saving opportunities to operators expecting to make huge financial investments in infrastructure, and make 5G a reality.
Recent Comments