5G network has seen steady growth in deployment globally, with the total number of subscribers of 5G services crossing the billion mark. Although most of the deployment has been through the Non-Standalone (NSA) mode, the Standalone (SA) core will see big commercial deployment in the years to come to explore the full potential of 5G, including venturing into newer 5G applications such as Network Slicing. To provide high bandwidth and low-latency connectivity with processing capabilities at the Edge to support enterprise and mission-critical use cases, it becomes important to manage the network effectively and autonomously, and AI/ML can help in this case. As the algorithm is advancing every day, AI/ML can help automate most of the tasks. The huge amount of data collected by network vendors and operators can be used to train an effective algorithm, thereby helping in the effective management of resources.
Some of the verticals where AI/ML will be useful in network management are:
Intelligent Network Automation
5G networks are complex and managing them is a difficult and expensive task. AI/ML can provide intelligent algorithms that can automate various network management tasks, thus reducing the time and resources required to manage the network. AI can help in managing the network traffic, as an increasing number of devices connected to a network makes it harder for an operator to monitor the usage, and the algorithm can monitor the network traffic pattern and optimize it, and allocate resources based on the devices’ bandwidth requirements, thus ensuring the efficient use of resources.
AI can also be used to get insights into network behavior, which can be used to identify bottlenecks and anomalies in the network that can cause security issues.
AI can help improve network energy savings by managing energy usage. The algorithm can optimize the transmission power of base stations for the devices based on their proximity. Another application can be the activation of sleep mode to reduce energy consumption when there is less network load on the base station or it is idle.
AI can also be used for precision planning in small-cell deployments. The ever-increasing demand for data is congesting the network in some areas, especially in urban and compact spaces such as stadiums. To solve the problem, small cells are required to be deployed. AI can analyze the data on network traffic and latency, and identify the black spots where small cells can be deployed. It can also help in identifying suitable locations for small-cell deployment so that not many cells are deployed at a site.
Huawei has launched intelligent RAN solutions iFaultCare and iPowerStar. The company claims that its iPowerStar AI-based intelligent RAN solutions can generate power savings of 25% and reduce OPEX by 20 million KWh per year, whereas iFaultCare can improve troubleshooting efficiency by 40%.
One of the major use cases of automation will be network management. The algorithms can monitor the network metrics, such as load factor, traffic and latency, and adjust them to optimize the performance. Another way in which AI can help is by improving network reliability through the prediction of issues that may arise. The algorithm can analyze the network data to identify patterns that may lead to outages, thus allowing time for preventive action.
In 5G, we are going to see an increasing number of connected devices along with an increasing volume of data transmitted across the network. With an increasing number of devices, the potential for cyberattacks also increases, and operators must enhance cybersecurity to prevent a possible attack on the network. Critical use cases such as private networks are more prone to cyberattacks, which can result in revenue losses to the enterprise. AI can come in handy in preventing cyberattacks. It can help identify potential threats, such as malware or phishing attacks, and respond quickly to mitigate the risk. Besides, AI can play a significant role in 5G network security by detecting, analyzing, and responding to security threats in real-time. With the past dataset provided to analyze network behavior, the algorithm can identify patterns and anomalies that may lead to cyberattacks. AI algorithms, for instance, can recognize a potential security breach if a certain device is transmitting an unusually large volume of data. It can then take appropriate steps to prevent any damage.
AI can help in effective MIMO management. The algorithm can analyze the network and adjust the number of MIMO antennas to be used for optimized device performance. AI can also be helpful in beamforming, a technique that allows the transmitter to focus its energy in a specific direction to improve network coverage and capacity. The algorithm can identify from where the demand is coming and ensure that sufficient bandwidth is provided to the device. By effective use of beamforming, operators can provide high-speed, low-latency services to different devices and applications.
Network slicing is one of the most discussed topics in the industry. It is being touted as one of the important use cases for 5G networks. Network slicing is a technique that allows operators to create multiple virtual networks on top of a shared physical infrastructure. Each virtual network can be designed to meet the specific requirements of a particular use case, such as high-speed data transfer and low latency. AI can be of immense help in network slicing, as it can automate most of the prerequisite tasks, such as:
- Preparing the network: The algorithm can use past data to predict the demand that might come from the user.
- Resource reservation: The algorithm, after predicting the demand, can slice the network and reserve it for the task which the network might be getting.
- Resource allocation: Once the requirements come from the user, the reserved resource can be allocated to the user.
Networks are becoming complex and AI/ML-based solutions are being used to reduce the complexity and make the network more intelligent. Introduced with Release 15, and with subsequent enhancements in Releases 16 and 17, AI/ML is being used for different use cases, such as network energy savings, network load balancing and mobility optimization.
Release 18 will be looking to incorporate more enhancements for automating the network and predicting the network behavior to make it efficient. Different areas are being looked into to study the potential of AI/ML for different elements of air interface, such as beam management, mobility, and position accuracy.
Drawbacks of AI/ML
Although AI offers lots of benefits in effectively managing the network and automating most of the tasks, it also has some deficiencies. One of the biggest problems faced in writing an effective algorithm is getting a large amount of reliable and relevant training data. Bigger players have access to large amounts of data and resources to train their models, whereas smaller players lack them and have to rely on other players to get the algorithms, which might not be relevant for their use cases. A lack of appropriate training data can make the model less reliable and relevant, and it might produce undesired outcomes.
Some of the challenges which an AI algorithm can face are:
- Complexity: Implementing AI technology in 5G is a complex task and it requires significant investments in resources. Besides, the AI algorithm needs to be effectively trained and tested before being deployed, to ensure that it provides the desired outcome.
- Bias: AI algorithms are trained on the data they get. If the data on which they are trained is biased or skewed towards one side, then it can generate biased results and could lead to the wrong label of false positives or false negatives.
- Privacy: Privacy has become one of the most important issues today for AI algorithms, as the algorithm needs data to be trained upon, but some of the data might contain sensitive information. Privacy laws must be in place to ensure that sensitive information is not used for inappropriate purposes.