GJR2393200R1210 88TK05 ABB总线耦合器模块

　　非 eMBB 应用程序（例如新的或现有的垂直行业）最显着的增强包括 RedCap、XR 以及国家安全和公共安全 (NSPS)。

　　RedCap UE 有望在未来的许多应用中发挥重要作用。基于 Rel-17，Rel-18 RedCap 解决方案将进一步降低设备成本和功耗。将研究支持能量收集的解决方案，例如节能唤醒无线电。

　　在 Rel-17 中，3GPP RAN 标准化团队正在研究各种形式的增强现实和虚拟现实服务，并它们在通过 5G 运行时的性能。主要挑战是同时提供非常高的数据速率和低/有限的延迟。在 Rel-18 中，3GPP RAN 小组将研究资源高效和低延迟无线电资源分配的流量管理、具有一致数据速率的移动性支持、与 XR 流量和延迟要求兼容的 UE 节能操作。

　　除了汽车和工业用例，NSPS 是使用 5GS 的最突出的新垂直领域。正在考虑对无人机远程控制和恶意无人机检测进行 RAN 增强，以提高急救人员的态势感知能力。Rel-18 还将通过 UE 到 UE 中继等技术进一步提高 5G 对覆盖范围外场景的支持。

　　我们还想强调针对 MBB 和非 MBB 用例的三个跨域功能：用于物理层 (PHY) 增强的 AI/ML、用于 RAN 增强的 AI/ML 和全双工。

　　人们普遍认为 AI/ML 可以显着提高 PHY 性能。因此，RAN 标准化将通过为 AI/ML 相关的 PHY 增强建立一个通用框架来探索机会，包括适当的 AI/ML 建模、方法和性能要求/测试。具体 AI/ML 增强的个领域可能是波束管理或信道估计/预测。

　　在 Rel-17 中，研究项目之一是为 RAN 确定合适的用例和相应的基于 AI/ML 的解决方案。在 Rel-18 中，对来自 Rel-17 的选择性用例的增强将进入规范阶段——即有效的流量转向和负载平衡。重点将放在对现有架构中当前接口的增强上。为了激励供应商的竞争力，一个目标是确保 AI 模型保持特定于实施。

　　尽管存在实际挑战和不明确的性能潜力，但还是有人提议研究全双工的可行性，其中 gNB 在 TDD 频段上同时发送和接收。该研究将调查可实现的增益及其对交叉链路干扰和自干扰缓解的依赖性。

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　　The most notable enhancements for non-eMBB applications (such as new or existing verticals) include RedCap, XR and national security and public safety (NSPS).

　　RedCap UEs are expected to play a significant role in many future applications. Based on Rel-17, Rel-18 RedCap solutions will further reduce device cost and power consumption. Solutions enabling energy harvesting, such as energy-efficient wake-up radios, will be investigated.

　　In Rel-17, the 3GPP RAN standardization team is studying various forms of augmented reality and virtual reality services and assessing their performance when operating through 5G. The main challenge is to simultaneously provide a very high data rate and low/bounded latency. In Rel-18, the 3GPP RAN group will look into traffic management for resource-efficient and low-latency radio resource allocation, mobility support with consistent data rates, UE energy-efficient operation compatible with XR traffic and latency requirements.

　　Aside from automotive and industrial use cases, NSPS is the most prominent new vertical using 5GS. RAN enhancements for the remote control of drones and rogue drone detection are being considered to improve the situational awareness of first responders. Rel-18 will also further improve 5G’s support for out-of-coverage scenarios by means of techniques such as UE-to-UE relaying.

　　Cross-domain functionalities for both MBB and non-MBB use cases

　　We also want to highlight three cross-domain functionalities that target both MBB and non-MBB use cases: AI/ML for physical layer (PHY) enhancements, AI/ML for RAN enhancements, and full duplex.

　　It is generally expected that AI/ML can significantly improve PHY performance. The RAN standardization will therefore explore the opportunities by setting up a general framework for AI/ML-related PHY enhancements, including proper AI/ML modeling, evaluation methodologies and performance requirements/testing. A first area for concrete AI/ML enhancement could be on beam management or channel estimation/prediction.

　　In Rel-17, one of the study items is to identify suitable use cases and corresponding AI/ML-based solutions for RAN. In Rel-18, enhancement for selective use cases from Rel-17 will be taken into the normative phase – that is, efficient traffic steering and load balancing. The focus will be on enhancements to current interfaces in the existing architecture. To incentivize vendor competitiveness, one goal is to ensure that AI models remain implementation-specific.

　　Despite the practical challenges and unclear performance potential, there is a proposal to study the feasibility of full duplex, where gNBs transmit and receive simultaneously on TDD frequency bands. The study will investigate the achievable gains and their dependency on cross-link interference and self-interference mitigation.