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      Defining 5G Software, Bolstering Networks

      The demand on the wireless industry to absorb, process, and deliver data at an unprecedented scale requires a new kind of network. The world’s leading telcos are turning to NVIDIA technologies to optimize networks, innovate faster, and deliver new services.


      Advance Your Telecom Networks with AI

      Increase productivity. Improve network quality. Enhance customer service.

      •  <span>Edge Computing</span>

        Edge Computing

      •  <span>Ultra-Efficient 5G</span>

        Ultra-Efficient 5G

      • <span>Network Operations</span>

        Network Operations

      5G Edge Computing

      NVIDIA GPUs make it possible to deliver ultra low-latency 5G services from the cloud. This makes applications like cloud-based virtual reality (VR), smart cities, cloud gaming, 360-degree immersive video, connected drones, and autonomous vehicles possible. Open source containers and libraries also help accelerate speed to market.

      5G Edge Computing

      We’ll be collaborating with NVIDIA to experiment with new ways of delivering experiences over 5G and edge computing technology.

      Alisha Seam, AT&T Foundry in Palo Alto

      AI- Accelerated 5G

      NVIDIA GPUs and CUDA? technology let telcos accelerate compute-intensive applications and VNF workloads with the largest amount of parallelizable code. Applications will be able to take advantage of the growth in computing power with GPUs from generation to generation without having to change software functionalities.

      Ultra-Efficient 5G

      NVIDIA GPUs helped Ribbon deliver 9X better performance with half the power.

      Kevin Riley, Ribbon Communications

      AI-Based Network Operations

      Network operations primarily based on statistical algorithms or static policies can be turned into deep learning (DL) and AI-based models. Adopt AI and ML in network operations to gain the ability to manage exponentially increasing complexity, and the realtime optimization required by SDNs. Minimize labor-intensive operational expenses and increase end-user productivity.

      AI-Based Network Operations

      Verizon reduced computing time from 24 hours to 1 hour with equal or better accuracy than traditional methods.

      Bryan Larish, Verizon
      Predicting 4G Wireless Network Quality with Deep Learning Algorithm - GTC 2018

      Ultra-Efficient 5G


      Discover how telecom service providers can leverage AI to improve network quality, optimize resource planning, and enhance customer service.

      Date: Thursday, May 30, 2019
      Time: 9:00–10:00 a.m. PT

      BreadCrumb Navigation - Tabs

      Technologies transforming telecommunications

      Deep Learning for Wireless Communications

      Deep Learning for Network Operations

      NVIDIA GPUs accelerate the actual training of channel autoencoders to speeds comparable to compiling programs for desktop processors in minutes, and sometimes even seconds. Once the model has been trained, it can then be deployed into a low-power, high performance embedded systems like the NVIDIA Jetson? TX2 or AGX  Xavier?. This creates a design flow where communications engineers can easily build optimized physical layers and then deploy them to embedded devices at the edge.

      Data Science

      Data Science

      With vast amounts of data being stored, processed, and analyzed in telecoms, accelerated data science maximizes productivity, provides optimized AI models and reduces TCO. Learn more about how the GPU accelerated ecosystem provides end-to-end data science solutions for telecom.

      Accelerating Large-Scale Object Detection

      Accelerated Large-Scale Object Detection

      Accurately detecting the presence of humans is critical to a variety of applications. High-performance deep learning training makes it possible to create robust and generalizable models for objects, humans, animals, and machines for object detection over 5G using NVIDIA TensorRT. Maintaining real-time inference performance in production environments while preserving high accuracy is critical, whether in the data center or at the edge. Every percent increase in accuracy leads to exponential gains for organizations providing video-based services.

      Background Noise Suppression

      Background Noise Suppression

      Existing noise suppression solutions in today’s mobile phones aren’t perfect, but they do provide an improved user experience. NVIDIA’s deep learning solution requires just a single microphone with all the post-processing handled by software. This allows hardware designs to be simpler and more efficient, completely muting the background noise for more pleasant and intelligible communications.


      As we hurtle toward the era of 5G, where cars, MRI scanners, and even washing machines will be connected, telecommunications companies are challenged to serve the vast amounts of content and services it will generate. To manage data loads, process complex network functions, provide new services, and drive revenue growth, they need to build a new kind of network.

      Watch this talk from GTC 2019 to delve into this new era. It covers the challenges, strategies, a potential solution, and global case studies.

      After you watch the video, explore the other resources available in the link to see how AI is transforming the telecom industry.

      Sign up for the latest news and developments from NVIDIA.