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Improving Network Automation and Security with Artificial Intelligence

Communication service providers (CommSPs) are already saving money and generating revenue from network transformation investments.

The growing complexity of network infrastructure, combined with the low latency and determinism associated with next generation services, makes it impossible to deploy and manage networks based on traditional network management methods and static policies.

When integrated with an operational support system (OSS), AI-powered network monitoring and predictive network analytics capabilities will detect network anomalies and faults, analyze root causes and trigger fault recovery/failover before failures actually occur in the network.

In fact, Intel recently demonstrated how traffic prediction models can be used to dynamically control the power states of the CPUs and platform components to reduce the power consumption—a major contributor to operational expenses.

By populating AI models with telemetry data, such as sequence of packets, packet boundaries, nature of compute operations and memory access patterns, we can effectively and efficiently perform real time intrusion detection, network isolation and preventive actions on encrypted traffic.

AI innovation promises to enable wireless coverage and capacity optimizations, advanced traffic management, dynamic distribution of users across frequencies to improve user experience, dynamic radio resource management, beamforming configuration, multi-radio access traffic steering/management, service aware resource management for network slices, and much more.

Similar to the closed loop automation described earlier, network operators can apply trained, machine learning models, based on traffic patterns, to predict lower traffic conditions and automatically lower the frequency and power consumption (low power state) of the radio units.

From SIMjacking to Bad Decisions: 5G Security Threats to Non-Public Networks

Download Securing 5G Through Cyber-Telecom Identity Federation Many industries are poised to tap into the speed, automation, and global reach of 5G, a telecom technology that is new to many of these industries.

Our latest research explored threats to 5G connectivity — from SIMjacking, IoT identity fraud, false decision engine data and logs, and poisoning machine learning rules for the manipulation of business decisions.

5G is a response to the need for bandwidth, consistency, and speed, especially in an era where mobile and IoT devices are ubiquitous among enterprises and industrial facilities increasingly moving toward digital transformation.

The federated cyber-telecom identity model is an approach to 5G security that provides a single and coherent security architecture for protecting the access to and the identity and integrity of data and other components and technologies within 5G networks.

Leveraging Machine Learning and Artificial Intelligence for 5G

The heterogenous nature of future wireless networks comprising of multiple access networks, frequency bands and cells - all with overlapping coverage areas - presents wireless operators with network planning and deployment challenges.

ML and AI can assist in finding the best beam by considering the instantaneous values updated at each UE measurement of the parameters mentioned below: Once the UE identifies the best beam, it can start the random-access procedure to connect to the beam using timing and angular information.

Massive simply refers to the large number of antennas (32 or more logical antenna ports) in the base station antenna array. Massive MIMO enhances user experience by significantly increasing throughput, network capacity and coverage while reducing interference by: The weights for antenna elements for a massive MIMO 5G cell site are critical for maximizing the beamforming effect.

ML and AI can collect real time information for multidimensional analysis and construct a panoramic data map of each network slice based on: Different aspects where ML and AI can be leveraged include: With future heterogenous wireless networks implemented with varied technologies addressing different use cases providing connectivity to millions of users simultaneously requiring customization per slice and per service, involving large amounts of KPIs to maintain, ML and AI will be an essential and required methodology to be adopted by wireless operators in near future.

All of them address low latency use cases where the sensing and processing of data is time sensitive. These use cases include self-driving autonomous vehicles, time-critical industry automation and remote healthcare. 5G offers ultra-reliable low latency which is 10 times faster than 4G. However, to achieve even lower latencies, to enable event-driven analysis, real-time processing and decision making, there is a need for a paradigm shift from the current centralized and virtualized cloud-based AI towards a distributed AI architecture where the decision-making intelligence is closer to the edge of 5G networks.

The 5G mm-wave small cells require deep dense fiber networks and the cable industry is ideally placed to backhaul these small cells because of its already laid out fiber infrastructure which penetrates deep into the access network close to the end-user premises.