5G is driving the evolution of AI by enabling more distributed computing environments and the rapid adoption of mobile, edge, and embedded devices. 5G networks have much lower latency and faster speeds than 4G, allowing for massive device integration and ultra-fast, high-volume streaming for AI applications. AI will also benefit 5G networks by ensuring they are optimized through techniques like embedded machine learning models for tasks like traffic routing and performance management. Two-way benefits are seen between 5G and AI as they continue to converge at the network edge.

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5G Driving the Evolution of AI
venkat vajradhar
May 29 · 4 min read
The future of the 5G edge. 5G is an important part of the evolution of cloud-computing
ecosystems towards more distributed environments, even though it is still many years
away from widespread expansion. Between now and 2025, the networking industry is
investing $ 5 trillion globally in 5G, supporting the rapid adoption of mobile, edge, and
embedded devices in every sphere of our lives.
5G is a major catalyst for the trend, under which more workload is being implemented
and data is on edge devices. This proves to the next generation of Artificial
Intelligence (AI) that data-driven algorithms provide an environment that guides each
cloud-centric process, device, and experience. AI will play an important part in
ensuring that 5G networks are optimized 24 × 7 end-to-end.
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How 5G Can Use AI
AI hybrid lives on every edge of the clouds, multi-cloud, and future mesh networks.
Already, we have seen major AI platform vendors make significant investments in 5G-
based services for 5 Mobility, Internet of Things (IoT), and other edge environments.
To better understand how 5G empowers the online economy, let’s consider how this
emerging wireless architecture delivers value across the AI in telecom toolchain:
Next-Generation Edge Convergence with AI Systems on the Chip
5G combines digital cellular technology with wireless long-term evolution and Wi-Fi
interface. When deployed on cross-technology network interfaces, 5G allows each edge
device to rotate smoothly between indoor and wide-area environments.
The adoption of technology someday meets the radio spectra for these asymmetric
radio channels and network interfaces to single chips that are active in maintaining
seamless connections across multiple radio access technologies. These same 5G
interfaces will undoubtedly be converted into low-power, low-cost systems on the chip
for many mass-market AI applications with a neural network processing circuit.
To Know more:Top Seven (7)Types of Artificial
Intelligence
Massive device integration in real-time filling AI data lakes: 5G can support one million
simultaneous edge devices per square kilometer, the order of magnitude greater
synchronization than 4G technology.
Ultra-fast, high-volume streaming for low-latency AI: 5G connections have much lower
latencies than 4G, less than 1 milliseconds versus 50 milliseconds that are
characteristic of 4G. As a result, 5G has a much faster download and upload speed than
4G: 20 gigabits per second, which is 4 times the rate of 5–12 megabits per second.
Most of the application of 5G arises from the ability to transmit data over multiple
bitstreams simultaneously between the bandwidth and the transmission capacity
connection base station and edge devices.
How AI can benefit 5G
AI is also a key component of infrastructure to ensure that 5G networks, in all their
complexity, can support AI and other application workloads. Recently published

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research shows that many wireless operators worldwide are well-equipped to
implement AI for their 5G and other networks.
For the next generation of distributed AI applications to work effectively, 5G networks
need to be constantly self-healing, self-managing, self-protecting, self-repairing, and
self-optimizing.
It relies on embedding machine learning and other AI models to automate application-
level traffic routing, service quality assurance, performance management, root cause
analysis, and other operational tasks. More efficient than manual methods.
At the very least, AI-based controls support changing RF’s channels and other
infrastructure resources dynamically and accurately to meet quality service
requirements, traffic patterns, and application workloads. They support the continual
assessment of alarm management, configuration and healing, and subscriber
experience optimization.
USM is capable to enhance the dynamic RF-channel allocation features of 5G. 5G has
smaller cells than 4G, uses frequency more aggressively, and must constantly retarget the
“beamformed” base station phase-range millimeter-wave antennas on each edge device.
To ensure the quality of service, 5G base stations dynamically evaluate and provide the
best wireless path for each device. They do this while constantly counting on the
difficulties facing 5G’s millimeter waves as they pass through walls and other solid
objects. AI-driven closed-loop real-time analytics is required to perform these
calculations in real-time on wireless local loops that are changing dynamically.
In fact, all of this AI in the 5G network creates demand for data management
infrastructure. Dedicated data lakes, auto ml tooling, This data/model management
infrastructure is implemented in cloud-to-edge configurations that align with complex
public / private federated environments that are characteristic of 5G.
For all these reasons, it’s time for service providers and enterprise IT, professionals, to
explore the critical role AI plays in their 5G and edge-computing plans.
Arti cial Intelligence 5g Technology Telecom

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