Five Takeaways from NVIDIA 6G Developer Day 2024
NVIDIA 6G Developer Day 2024
brought together members of the 6G research and development community to share insights and learn new ways of engaging with NVIDIA 6G research tools. More than 1,300 academic and industry researchers from across the world attended the virtual event. It featured presentations from NVIDIA, ETH Zürich, Keysight, Northeastern University, Samsung, Softbank, and University of Oulu. This post explores five key takeaways from the event.
1. 6G will be AI-native and implement AI-RAN
It’s expected that 6G will ride the AI wave to unlock new potential for both consumers and enterprises, and transform the telecommunications infrastructure. This was the key message of the
keynote presented by NVIDIA SVP Ronnie Vasishta
. With the rapidly growing adoption of generative AI and AI applications, AI-enhanced endpoints are interacting and making decisions on the move, creating huge volumes of voice, video, data, and AI traffic on the telecommunications network.
The emergence of AI traffic, generated from AI applications at the edge, and requiring differing levels of cost economics, energy economics, latency, reliability, security and data sovereignty, provides new opportunities and challenges for the telecommunications infrastructure. This requires the underlying infrastructure to be designed and built to be AI-native, natively leveraging AI capabilities and supporting AI traffic.
The keynote unveiled the strategic and technical drivers for an AI-native 6G infrastructure. Telcos want to maximize infrastructure efficiency with higher spectral efficiency, throughput, and capacity. Equally, telcos seek to maximize return on investment with better monetization, agility to introduce new features, and support for growth of traffic and new services on the RAN.
AI-RAN offers a pathway to realize the benefits of the AI-native infrastructure with an AI-With-RAN and an AI-For-RAN implementation (Figure 1). AI-With-RAN encompasses what the AI-RAN Alliance describes as AI-on-RAN and AI-and-RAN. It enables telcos to move from single purpose to multipurpose networks that can dynamically run AI and RAN workloads on a software-defined, unified, and accelerated infrastructure.
With AI-For-RAN, RAN-specific AI algorithms are deployed on the same infrastructure to drive RAN performance and improvement. AI-RAN will revolutionize the telecommunications industry, enabling telcos to unlock new revenue streams, and deliver enhanced experiences through generative AI, robotics, and automation tools.
Figure 1. AI-RAN is the technology framework for AI-native 6G
2. AI-RAN models the three computer problems of AI for the physical world
AI-RAN is the technology framework to build the AI-native 6G​ and is a good model of how AI is integral to developing, simulating, and deploying solutions for the physical world. This aligns with the classic three computer problems:
Creating AI models using huge amounts of data
Testing and improving network behavior with large-scale simulations, especially for site-specific data
Deploying and operating a live network
For 6G, this means creating and developing AI models for 6G; simulating, modeling, and improving an AI-native 6G; and deploying and operating an AI-native 6G.
The session on
Inventing 6G with NVIDIA AI Aerial Platform
introduced
NVIDIA AI Aerial
as a platform for implementing AI-RAN and with three components for addressing the three computer problems:
NVIDIA Aerial AI radio frameworks
for developing and training 6G algorithms. This is suited for datacenter scale computing platforms such as
NVIDIA DGX
.
NVIDIA Aerial Omniverse Digital Twin (AODT)
for large city-scale simulations with accurate physical properties and high degree of fidelity. It is used for simulating and fine-tuning 6G algorithms and is deployed on
NVIDIA OVX systems
.
NVIDIA Aerial CUDA Accelerated RAN
, a carrier-grade, GPU-accelerated, software-defined vRAN and is deployed on the
Aerial RAN Computer-1
platform.
NVIDIA AI Aerial provides a set of tools for algorithm development, system-level integration and benchmarking, as well as production-level integration and benchmarking. Introduced for 5G, these tools cover 5G-advanced and lead the path towards 6G.
Figure 2. NVIDIA AI Aerial addresses the three computer problems for delivering 6G
3. GPU-based accelerated computing is best suited for deploying 6G
6G AI-RAN will continue the trend from 5G towards a software-defined, high-performance RAN running on COTS infrastructure. It will also be fully AI-native, O-RAN based, with hardware/software disaggregation, and multipurpose in nature to support both AI and RAN workloads. With these, it is increasingly clear that new approaches will be needed for the computer where the 6G AI-RAN is deployed to realize new opportunities for telcos and handle vRAN baseband challenges.
Among all the competing solutions to match these industry requirements, there are three key reasons GPU acceleration is the best computer platform for deploying 6G, as explained in the session on
CUDA/GPU System for Low-Latency RAN Compute
. Specifically, GPUs:
Deliver very high throughput to handle heavy traffic. This is possible because, thanks to parallel computing, the GPU excels at managing multiple data streams simultaneously, better utilization of multiple Physical Resource Blocks (PRBs) and handling complex algorithms for tasks such as beamforming.
Run low-latency and real-time critical workloads efficiently. GPU SIMT (single instruction, multiple threads) architecture is optimized for linear algebra operations. With additional CUDA features, this enables a software-defined digital signal processing compute machine to run physical layer workloads efficiently.
Are ideal for the multipurpose platform for AI and RAN, thanks to its well-established suitability for AI workloads. This makes it significantly better at delivering a profitable AI-RAN and provides a platform that offers a sustainable Gbps/watt energy efficiency. For more details from the Softbank announcement in November 2024, see
AI-RAN Goes Live and Unlocks a New AI Opportunity for Telcos
.
NVIDIA Aerial CUDA Accelerated RAN
running on the
Aerial RAN Computer-1
offers a high-performance and scalable GPU-based solution for the 6G AI-RAN. It includes a set of software-defined RAN libraries (cuPHY, cuMAC, pyAerial) optimized to run on multiple GPU-accelerated computing configurations.
Figure 3 shows the different strategies required to scale such a system from low density to medium density and high density configurations and across CPU, GPU, and NIC subsystems. It highlights strategies to minimize data movement, synchronization and execution overhead; maximize concurrency and asynchrony; tools for optimization, pipelining, prioritization, and resource partitions for QoS.
Figure 3. Scaling a low-latency system​ for CUDA/GPU for low-latency RAN compute​
4. Digital twins will be an integral part of 6G AI-RAN
At NVIDIA GTC 2024, NVIDIA CEO Jensen Huang said in a keynote that “we believe that everything manufactured will have digital twins.” This is increasingly a reality across many industrial sectors. For telecommunications, 6G will be the first cellular technology generation to be first created and simulated as a digital twin. This will create a continuum among the design, deployment, and operations phases for 6G RAN products.
The session on
Building a RAN Digital Twin
explored how to build a RAN digital twin using
NVIDIA Aerial Omniverse Digital Twin (AODT)
and how this enables new AI techniques and algorithms for 6G (Figure 4). AODT is a next-generation, system-level simulation platform for 5G/6G. It is based on, and benefits from the richness of, the
NVIDIA Omniverse
platform. In addition, Keysight showcased how they are using AODT as part of their RF Raytracing Digital Twin Solution in the session,
6G Developer Spotlight Session 1
.
Both sessions showcased how aspects of the physical world (including 5G/6G RAN, user devices, and radio frequency signals) and the digital twin world (including the electromagnetic engine, mobility model for user devices, geospatial data, antenna data, and channel emulator) are combined to create and simulate a RAN digital twin. In doing so, the RAN digital twin becomes a tool to benchmark system performance and explore machine learning-based wireless communication algorithms in real-world conditions.
Figure 4. Scene creation from the NVIDIA Aerial Omniverse Digital Twin
5. The industry needs platforms for AI training for 6G AI-RAN
As AI becomes integral to 6G design and development, the need for training platforms and testbeds is important for the industry. Such a platform and testbed provides an opportunity to take 6G AI/ML from simulation to reality. Ongoing research areas for native AI include waveform learning, MAC acceleration, ​site-specific optimizations, beamforming​, spectrum sensing,​ and semantic communication.
The session on
AI and Radio Frameworks for 6G
explored how AI and radio frameworks can be used for 6G R&D with
NVIDIA Aerial AI Radio Frameworks
and its tools
pyAerial
,
Aerial Data Lake
, and
NVIDIA Sionna
.
pyAerial is a Python library of physical layer components that can be used as part of the workflow in taking a design from simulation to real-time operation. It provides end-to-end verification of a neural network integration into a physical layer pipeline and helps bridge the gap from the world of training and simulation in TensorFlow and PyTorch to real-time operation in an over-the-air testbed.
Aerial Data Lake is a data capture platform supporting the capture of OTA radio frequency (RF) data from vRAN networks built on the Aerial CUDA-Accelerated RAN. It consists of a data capture application running on the base station distributed unit, a database of samples collected by the app, and an API for accessing the database.
Sionna is a GPU-accelerated open-source library for link-level simulations. It enables rapid prototyping of complex communication system architectures and provides native support for the integration of machine learning in 6G signal processing.
The session on
Setting Up 6G Research Testbeds
explored how to set up a 6G research testbed to accelerate innovation, drive standardization, and provide real-world testing and performance benchmarking with the NVIDIA Aerial RAN CoLab (ARC-OTA).
In the
6G Developer Spotlight Session 1
and
6G Developer Spotlight Session 2
, Softbank, Samsung, University of Oulu, Northeastern University (NEU), and ETH Zurich showcased how they are working with the NVIDIA AI and radio frameworks plus ARC-OTA to accelerate their 6G research (Table 1). Most commonly, these research groups presented how they are using AI for the complex and challenging problem of channel estimation and on how they are using ARC-OTA to close the “reality gap” between simulation and real-world OTA.
Organization​
Project​s
NVIDIA tools used​
Samsung​
AI channel estimation​
Lab-to-field methodology
​Site-specific optimizations
Close reality gap between sim and real-world OTA
ARC-OTA, pyAerial, ADL, Sionna, SionnaRT, AODT in the future​
ETH​
NN PUSCH​
Deep-unfolding for iterative detector-decoder​
ARC-OTA, pyAerial, ADL, Sionna, SionnaRT, AODT in the future​
NEU​
x5G 8-node ARC-OTA testbed​
RIC and real-time apps (dApps)​
Deployment automation on OpenShift
O-RAN intelligent orchestrator
ARC-OTA, pyAerial, ADL, Sionna, SionnaRT, AODT ​
University of Oulu​
Sub-THz​
Sionna, ARC-OTA in the future​
Keysight​
Product development: Deterministic channel modeling for 6GAI-assisted channel modeling​​; Physical digital twin for 6G applications​
AODT: GIS + mobility model + RT​
Softbank​
AI-for-RAN project: ML channel estimation and interpolation​
ARC-OTA, pyAerial​
Table 1. Summary of the spotlight presentations from various research groups using NVIDIA assets for 6G research
What’s next?
The 6G Developer Day is one of the channels to engage with the 6G research and development community and will become a feature of the NVIDIA event calendar. Check out the
NVIDIA 6G Developer Day playlist
to view all the sessions presented at the event on demand. Reference the
NVIDIA Aerial FAQ
based on the event Q&A for more information. To engage and connect with 6G researchers, join the
NVIDIA 6G Developer Program
.
2024 年 NVIDIA 6G 开发者日的 5 大关键收获
2024 年
NVIDIA 6G 开发者日让 6G 研发社区成员共聚一堂
，分享见解，并学习使用 NVIDIA 6G 研究工具的新方式。来自世界各地的 1,300 多名学术和行业研究人员参加了此次虚拟活动。会上，NVIDIA、ETH Zürich、Keysight、Northeastern University、Samsung、Softbank 和 University of Oulu 发表了演讲。本文将探讨本次活动的五个要点。
1、6G 将采用 AI 原生技术并实施 AI-RAN
预计 6G 将驾 AI 浪潮，为消费者和企业释放新的潜力，并改变电信基础设施。这是 NVIDIA
高级副总裁 Ronnie Vasishta 发表的主题演讲
的关键信息。随着生成式 AI 和 AI 应用的采用率迅速增长，AI 增强型端点正在进行交互并在移动中做出决策，从而在电信网络上产生大量语音、视频、数据和 AI 流量。
AI 流量由边缘的 AI 应用生成，需要不同级别的成本经济性、能源经济性、延迟、可靠性、安全性和数据主权，这为电信基础设施带来了新的机遇和挑战。这需要设计和构建原生 AI 的底层基础设施，利用原生 AI 功能并支持 AI 流量。
主题演讲揭示了 AI-native 6G 基础设施的战略和技术驱动力。电信公司希望通过提高频谱效率、吞吐量和容量，更大限度地提高基础设施效率。同样，电信公司寻求通过更好的货币化、引入新功能的灵活性以及对 RAN 流量增长和新服务的支持来更大限度地提高投资回报。
AI-RAN 通过 AI-With-RAN 和 AI-For-RAN 实施提供了实现 AI 原生基础设施优势的途径 (图 1)。AI-With-RAN 包含 AI-RAN 联盟所说的 AI-on-RAN 和 AI-and-RAN。它使电信公司能够从单一用途网络转向多用途网络，这些网络可以在软件定义、统一和加速的基础设施上动态运行 AI 和 RAN 工作负载。
借助 AI-For-RAN，RAN 特定的 AI 算法部署在同一基础架构上，以推动 RAN 的性能和改进。AI-RAN 将彻底改变电信行业，使电信公司能够挖掘新的收入来源，并通过生成式 AI、机器人和自动化工具提供增强的体验。
图 1、AI-RAN 是 AI 原生 6G 的技术框架
2. AI-RAN 为物理世界的三个计算机问题建模
AI-RAN 是构建 AI 原生 6G 的技术框架，也是一个很好的模型，可以说明 AI 是物理世界开发、仿真和部署解决方案不可或缺的一部分。这与经典的三个计算机问题一致：
使用海量数据创建 AI 模型
通过大规模模拟测试和改进网络行为，尤其是针对特定站点的数据
部署和运营实时网络
对于 6G，这意味着创建和开发适用于 6G 的 AI 模型；仿真、建模和改进 AI 原生 6G；以及部署和运行 AI 原生 6G。
在关于使用
NVIDIA AI Aerial
平台发明 6G 的会议上，NVIDIA AI Aerial 作为实施 AI-RAN 的平台，并包含解决三个计算机问题的三个组件：
用于开发和训练 6G 算法的
NVIDIA Aerial AI 无线电框架
。这适用于
NVIDIA DGX 等数据中心级计算平台
。
NVIDIA Aerial Omniverse 数字孪生 (AODT)
具有准确的物理属性和高保真度，适用于大规模城市仿真。它用于仿真和微调 6G 算法，并部署在
NVIDIA OVX 系统上
。
NVIDIA Aerial CUDA Accelerated RAN 是电信级 GPU 加速的软件定义 vRAN
，
部署在 Aerial RAN Computer-1 平台上
。
NVIDIA AI Aerial 为算法开发、系统级集成和基准测试以及生产级集成和基准测试提供了一套工具。这些工具专为 5G 推出，涵盖 5G 先进技术，引领 6G 发展。
图 2、NVIDIA AI Aerial 解决了提供 6G 的三个计算机问题
3. 基于 GPU 的加速计算非常适合部署 6G
6G AI-RAN 将延续从 5G 到在 COTS 基础设施上运行软件定义的高性能 RAN 的趋势。它还将完全采用 AI 原生，基于 O-RAN，具有硬件/软件分解和多用途的性质，以支持 AI 和 RAN 工作负载。有了这些，我们越来越清楚，部署 6G AI-RAN 的计算机将需要新的方法来实现电信公司的新机遇并应对 vRAN 基带挑战。
在满足这些行业需求的所有竞争解决方案中，GPU 加速成为部署 6G 的最佳计算机平台的主要原因有三个，这在关于低延迟 RAN 计算的
CUDA/GPU 系统的会议中有所解释
。具体来说，GPU：
提供极高的吞吐量，以处理繁忙的交通。之所以能够做到这一点，是因为得益于并行计算，GPU 擅长同时管理多个数据流、更好地利用多个物理资源块 (PRBs) 并处理波束成形等任务的复杂算法。
高效运行低延迟的实时关键工作负载。GPU SIMT（单指令多线程）架构针对线性代数运算进行了优化。借助额外的 CUDA 功能，这使得软件定义的数字信号处理计算机器能够高效运行物理层工作负载。
凭借其成熟的 AI 工作负载适应性，非常适合用于 AI 和 RAN 的多用途平台。这使得
AI-RAN 在提供盈利性 AI-RAN 方面表现显著，并提供了一个可持续提供 Gbps/瓦能效的平台。
有关 2024 年 11 月 Softbank 公告的更多详细信息，请参阅 AI-RAN Goes Live 和 Unlocks a New AI Opportunity for Telcos。
在
Aerial RAN Computer-1
上运行的
NVIDIA Aerial CUDA Accelerated RAN
为 6G AI-RAN 提供了基于 GPU 的高性能和可扩展解决方案。它包含一组经过优化的软件定义 RAN 库 (cuPHY、cuMAC、pyAerial)，可在多个 GPU 加速计算配置上运行。
图 3 显示了跨 CPU、GPU 和 NIC 子系统将此类系统从低密度扩展到中密度和高密度配置所需的不同策略。它重点介绍了以下策略：最大限度地减少数据移动、同步和执行开销；最大限度地提高并发和异步；用于优化、流水线、优先级和 QoS 资源分区的工具。
图 3、扩展低延迟系统​ for CUDA/GPU for 低延迟 RAN 计算
4. 数字孪生将成为 6G AI-RAN 不可或缺的一部分
在 2024 年 NVIDIA GTC 大会上，NVIDIA 首席执行官 Jensen Huang 先生在主题演讲中表示：“我们相信，所有制造业都将拥有数字孪生。”这在许多工业领域正日益成为现实。在电信领域，6G 将是率先作为数字孪生创建和模拟的蜂窝技术。这将在 6G RAN 产品的设计、部署和运营阶段之间形成一个连续统一体。
关于
构建 RAN 数字孪生
的会议探讨了如何使用 NVIDIA Aerial Omniverse Digital Twin (AODT) 构建 RAN 数字孪生，以及这如何为 6G 实现新的 AI 技术和算法 (图 4)
。AODT 是面向 5G/6G 的新一代系统级仿真平台。
它基于 NVIDIA Omniverse 平台的丰富内容，并从中受益。此外，
在 6G 开发者聚焦会议 1 中
，Keysight 展示了他们如何将 AODT 用作其 RF Raytracing Digital Twin Solution 的一部分。
两场会议都展示了如何结合物理世界 (包括 5G/6G RAN、用户设备和射频信号) 和数字孪生世界 (包括电磁引擎、用户设备的移动模型、地理空间数据、天线数据和信道仿真器) 的各个方面来创建和模拟 RAN 数字孪生。如此一来，RAN 数字孪生便成为了一款工具，用于在真实环境中对系统性能进行基准测试，并探索基于机器学习的无线通信算法。
图 4、通过 NVIDIA Aerial Omniverse 数字孪生创建场景
5. 该行业需要为 6G AI-RAN 提供 AI 训练平台
随着 AI 成为 6G 设计和开发不可或缺的一部分，该行业对训练平台和测试平台的需求变得尤为重要。这样的平台和测试平台提供了将 6G AI/ML 从仿真变为现实的机会。原生 AI 的持续研究领域包括波形学习、MAC 加速、站点特定优化、波束赋形、频谱感知和语义通信。
面向 6G 的 AI 和无线电框架
会议探讨了如何使用 AI 和无线电框架进行 6G 研发，以及 NVIDIA Aerial AI 无线电框架及其工具
pyAerial
、Aerial Data Lake 和 NVIDIA Sionna。
pyAerial 是一个物理层组件的 Python 库，可作为工作流的一部分，将设计从模拟变为实时操作。它为物理层管道中的神经网络集成提供端到端验证，并有助于弥合从 TensorFlow 和 PyTorch 中的训练和模拟到实时操作在一个无线测试平台之间的差距。
Aerial Data Lake 是一个数据捕获平台，支持从基于 Aerial CUDA 加速 RAN 的 vRAN 网络中捕获 OTA 射频 (RF) 数据。它由在基站分布式单元上运行的数据捕获应用、应用收集的样本数据库以及用于访问数据库的 API 组成。
Sionna 是一个 GPU 加速的开源库，用于链路级模拟。它支持复杂通信系统架构的快速原型设计，并为在 6G 信号处理中集成机器学习提供原生支持。
关于
设置 6G 研究测试平台
的会议探讨了如何设置 6G 研究测试平台，以加速创新、推动标准化，并利用 NVIDIA Aerial RAN CoLab (ARC-OTA) 提供真实的测试和性能基准测试。
在
6G 开发者聚焦会议 1
和
6G 开发者聚焦会议 2
中，Softbank、Samsung、University of Oulu、Northeastern University (NEU) 和 ETH Zurich 展示了他们如何使用 NVIDIA AI 和 radio 框架以及 ARC-OTA 来加速其 6G 研究 (表 1)。最常见的情况是，这些研究小组介绍了他们如何使用 AI 来解决复杂且具有挑战性的信道估计问题，以及他们如何使用 ARC-OTA 来缩小仿真与现实 OTA 之间的“现实差距”。
组织*
项目
使用的 NVIDIA 工具*
Samsung®
AI 信道估计实验室到现场的方法特定站点的优化缩小模拟与现实 OTA 之间的现实差距
未来的 ARC-OTA、pyAerial、ADL、Sionna、SionnaRT、AODT
ETH*
NN PUSCH​Deep-unfolding for iterative detector-decoder
未来的 ARC-OTA、pyAerial、ADL、Sionna、SionnaRT、AODT
东北大学*
x5G 8-node ARC-OTA testbed​RIC 和实时应用 (dApps)​Deployment automation on OpenShiftO-RAN intelligent orchestrator
ARC-OTA、pyAerial、ADL、Sionna、SionnaRT、AODT
奥卢大学*
Sub-THz*
Sionna，未来的 ARC-OTA
是德科技
产品开发：适用于 6G 的确定性信道建模和 AI 辅助信道建模​​；适用于 6G 应用的物理数字孪生
AODT：GIS 移动模型 RT
软银
AI-for-RAN 项目：ML 信道估计和插值
ARC-OTA、pyAerial
表 1、各研究团队使用 NVIDIA 资产进行 6G 研究的聚焦演讲摘要
接下来该怎么做？
6G 开发者日是与 6G 研发社区互动的渠道之一，并将成为 NVIDIA 活动日历中的一项内容。查看
NVIDIA 6G 开发者日播放列表
，点播观看活动中呈现的所有会话。有关更多信息，请参阅基于活动问答的 NVIDIA Aerial 常见问题解答。要与 6G 研究人员互动和交流，请加入
NVIDIA 6G 开发者计划
。