网络优化白皮书

5G应用场景白皮书一、智能制造领域在智能制造中，5G 技术能够实现工业设备的智能化连接和控制。

通过 5G 网络，工厂内的机器人、数控机床、传感器等设备可以实时、高效地进行数据传输和交互。

这使得生产过程更加灵活和自动化，提高了生产效率和产品质量。

例如，在汽车制造工厂中，5G 可以支持无人驾驶的运输车辆在车间内准确无误地运输零部件，同时能够对生产线上的设备进行实时监控和故障预警。

一旦某个设备出现异常，相关数据会立即通过 5G 网络传输到控制中心，技术人员可以迅速采取措施进行维修，大大减少了生产中断的时间。

此外，5G 还能实现远程操控和虚拟工厂。

技术人员可以在千里之外通过 5G 网络对工厂内的设备进行精准操控，就如同在现场一样。

虚拟工厂则利用 5G 带来的高速数据传输，对整个生产流程进行模拟和优化，提前发现潜在问题，降低生产成本。

二、智能交通领域5G 在智能交通领域的应用将极大地改善交通状况和出行体验。

首先，5G 支持车联网技术的发展，使车辆之间能够实时通信和共享信息。

车辆可以获取周边车辆的速度、位置、行驶方向等信息，从而提前做出预警和决策，避免交通事故的发生。

同时，车辆与道路基础设施之间的通信也变得更加顺畅，交通信号灯可以根据实时交通流量自动调整时长，提高道路通行效率。

其次，5G 助力自动驾驶技术的实现。

自动驾驶车辆需要大量的数据来感知周围环境和做出决策，5G 的低延迟和高速率能够确保这些数据的快速传输和处理，使车辆能够及时响应各种复杂的路况。

再者，5G 还可以用于智能公交系统。

乘客可以通过手机实时获取公交车辆的位置和预计到达时间，合理安排出行。

公交公司也可以根据实时客流量数据，灵活调整车辆的发车频率和线路，提高公交服务的质量和效率。

三、医疗健康领域在医疗健康领域，5G 技术为远程医疗、医疗物联网和医疗大数据等方面带来了新的突破。

远程医疗借助 5G 网络的高速和低延迟，专家可以远程对患者进行诊断和治疗。

5G-Advanced网络技术演进白皮书（2021）——面向万物智联新时代从产业发展驱动角度看，键，全球的主要经济体均明确要求将5G作为长期产业发展的重要一环。

从业务上5G将要进入千行百业，从技术上5G需要进一步融合DOICT等技术。

因此本白皮书提出需要对5G 网络的后续演进—5G-Advanced进行持续研究, 并充分考虑架构演进及功能增强。

本白皮书首先分析了5G-Advanced的网络演进架构方向，包括云原生、边缘网络和网络即服务，同时阐述了5G-Advanced的技术发展方向包括智慧、融合与使能三个特征。

其中智慧代表网络智能化，包括充分利用机器学习、数字孪生、认知网络与意图网络等关键技术提升网络的智能运维运营能力，打造内生智能网络；融合包括行业网络融合、家庭网络融合、天地一体化网络融合等，实现5G与行业网协同组网、融合发展；使能则包括对5G交互式通信和确定性通信能力的增强，以及网络切片、定位等现有技术的增强，更好赋能行业数智化转型。

，华为，爱立信（中国），上海诺基亚贝尔，中兴，中国信科，三星，亚信，vivo，联想，IPLOOK，紫光展锐，OPPO，腾讯，小米（排名不分先后）1 产业进展概述 (01)1.1 5G产业发展现状 (01)1.2 5G网络演进驱动力 (01)1.2.1 产业发展驱动力 (01)1.2.2 网络技术驱动力 (02)2 5G-Advanced网络演进架构趋势和技术方向 (04)3 5G-Advanced关键技术 (06)3.1 网络智能化 (06)3.1.1 网络智能化关键技术 (06)3.1.2 智能网络应用场景 (08)3.2 行业网融合 (08)3.3 家庭网络融合 (09)3.4 天地一体化网络融合 (10)3.5 交互式通信能力增强 (11)3.6 确定性通信能力增强 (11)3.7 用户面演进 (12)3.8 网络切片增强 (12)3.9 定位测距与感知增强 (13)3.10 组播广播增强 (13)3.11 策略控制增强 (13)4 总结和展望 (14)5G网络的全球商用部署如火如荼。

中国移动网络技术白皮书（2020年）目录一、网络技术发展之势 (4)二、网络技术发展之策 (6)(一)求解最大值问题（Maximization），追求极致网络 (6)1.性能提升 (6)2.能力增强 (7)(二)求解最小值问题（Minimization），追求极简网络 (9)1.简化制式 (9)2.节能降本 (9)3.降复杂度 (10)(三)求解化学方程式（Fusion），追求融合创新 (11)1.云网融合 (11)2.网智融合 (12)3.行业融通 (13)三、结束语 (16)缩略语列表 (17)一、网络技术发展之势伴随新一轮科技革命和产业变革进入爆发拐点，5G、云计算、人工智能等新一代信息技术已深度融入经济社会民生，造福于广大用户的日常生活。

加快推进5G 为代表的国家新基建战略，引领网络技术创新和网络基础设施建设，已成为支撑经济社会数字化、网络化、智能化转型的关键。

面向近中期网络技术发展，中国移动认为以下技术发展趋势值得关注：性能极致化：随着移动通信每十年一代的快速发展，产业各方共同努力不断提升通信网络速率、时延、可靠性等性能，延伸网络覆盖，提供差异化服务能力，以更好地满足万物互联多样化通信需求。

算网一体化：从云计算、边缘计算到泛在计算发展的大趋势下，通过无处不在的网络为用户提供各类个性化的算力服务。

算网一体化已经成为ICT发展趋势，云和网络正在打破彼此的界限，通过云边网端链五维协同，相互融合，形成可一键式订购和智能化调度的算网一体化服务。

平台原生化：在企业数字化转型、5G云化的浪潮下，产业融合速度加快、网络业务迭代周期缩短。

云原生理念及其相关技术提供了极致的弹性能力和故障自愈能力，获得业界认可。

未来云平台将向云原生演进，为电信网元及应用提供更加灵活、敏捷和便捷的开发和管理能力。

网络智能化：人工智能正在从感知智能向认知智能发展，其应用范围不断扩大。

人工智能的完善成熟促使其与网络的融合不再是简单的网络智能叠加，而是实现网络智能的内生化，切实提升网络运维效率和运营智能化水平，达到降本增效的实际效果。

引言5G网络：挑战与机遇5G网络架构设计5G网络代表性服务能力5G网络标准化建议总结和展望主要贡献单位P1 P2 P4 P8 P15 P17 P18目录1随着5G研究的全面展开并逐步深入，业界就5G场景形成基本共识：面向增强的移动互联网应用场景，5G提供更高体验速率和更大带宽的接入能力，支持解析度更高、体验更鲜活的多媒体内容；面向物联网设备互联场景，5G提供更高连接密度时优化的信令控制能力，支持大规模、低成本、低能耗IoT设备的高效接入和管理；面向车联网、应急通信、工业互联网等垂直行业应用场景，5G提供低时延和高可靠的信息交互能力，支持互联实体间高度实时、高度精密和高度安全的业务协作。

面对5G极致的体验、效率和性能要求，以及“万物互联”的愿景，网络面临全新的挑战与机遇。

5G网络将遵循网络业务融合和按需服务提供的核心理念，引入更丰富的无线接入网拓扑，提供更灵活的无线控制、业务感知和协议栈定制能力；重构网络控制和转发机制，改变单一管道和固化的服务模式；利用友好开放的信息基础设施引言环境，为不同用户和垂直行业提供高度可定制化的网络服务，构建资源全共享、功能易编排、业务紧耦合的综合信息化服务使能平台。

5G国际标准化工作现已全面展开，需要尽快细化5G网络架构设计方案并聚焦关键技术方向，以指导后续产业发展。

本白皮书从逻辑功能和平台部署的角度，以四维功能视图的方式呈现了新型5G网络架构设计，并提炼了网络切片、移动边缘计算、按需重构的移动网络、以用户为中心的无线接入网和能力开放等5G网络代表性服务能力。

白皮书最后提出了5G网络架构和技术标准化工作的推进建议。

21. 极致性能指标带来全面挑战首先，为了满足移动互联网用户极致的视频及增强现实等业务体验需要，5G系统提出了随时随地提供100Mbps—1Gbps的体验速率的指标要求，甚至在500km/h的高速运动过程中，也要求具备基本服务能力和必要的业务连续性。

第二，为了支持移动互联网和物联网场景设备高效接入的要求，5G系统需同时满足Tbps/km 2的流量密度和百万/km 2连接密度要求，而现有网络流量中心汇聚和单一控制机制5G 网络: 挑战与机遇在高吞吐量和大连接场景下容易导致流量过载和信令拥塞。

White PaperMobile Networks:Automation forOptimized PerformanceMobile networks are becoming increasingly important worldwide as people transition to a more transient lifestyle. People now use mobile networks to work remotely, stream video, and access social media applications. Soon, mobile networks will play a major role in areas such as the Internet of Things (IoT), cloud computing, and vehicle communication.This dependency on mobile networks has increased Quality of Experience (QoE) pressures on service providers at a time when bandwidth demands are also at an all-time high. How can service providers keep up with bandwidth needs and keep QoE at high levels?Service providers are doing their best to meet these demands by making macro level adjustments to networks to achieve incremental improvements in performance. But this has come at a cost. Service providers are seeing profits decline as more money and staff are needed to keep networks running in this new, complex environment. Even with the increase in operating expenditures (OpEx), traditional network optimization is not enough to keep up with the dynamic nature of today’s network traffic.What is needed is a way to automate network performance to create major leaps in optimization on a granular level, while also decreasing OpEx and freeing up staff to maintain the infrastructure and plan for expanding the network to deliver greater capacities. Major advancements have been made in recent months to make automated optimization a reality. Let’s take a closer look at the limitations of current network optimization methods, how automated optimization can overcome these limitations, and how this new method of optimizing networks can create a strategic advantage for service providers when the time comes to deploy 5G.Challenges Facing NetworksAs mobile networks continue to evolve, there are three main challenges that service providers face: interdependency, non-uniformity, and complexity. Each is a problem on its own, but together they create a network environment that is nearly impossible to optimize using traditional methods.Many of the metrics used to optimize networks are now interdependent. Changing a parameter, or parameters, to enhance the characteristics in one part of the network can have implications on other characteristics in other parts of the network. For instance, trying to increase data throughput in a certain area could affect voice traffic – either positively or negatively – in the network.This could also have a detrimental effect on design. Current designs that focus on one Key Performance Indicator (KPI) differ from designs that focus on other KPIs. This means that designs that focus on a specific KPI in isolation may or may not be the right choice for the overall performance of the network – especially as networks become increasingly non-uniform.Extreme non-uniformity is the new normal for mobile networks as regular users become power users and the overall subscriber population becomes more mobile. According to the VIAVI Mobile Data Trends report, 50 percent of data is consumed by only one percent of users. In addition, 50 percent of data is consumed in less than one percent of a network area, and this area is constantly changing. This change can be dramatic. In extreme cases,the amount of data that a cell is expected to support can increase by orders of magnitude over a period of a few minutes.This last data point is an important one. Not only has it become increasingly difficult to optimize networks because of non-uniformity, the non-uniformity is now dynamic. As this trend continues to grow, it will make it impossible to manually optimize networks in the future as this method cannot keep pace with the dynamic changes taking place. This leads to the overall problem with optimizing mobile networks: complexity. Not only are subscribers using networks in new and dynamic ways, technologies such as L TE, VoL TE, and heterogeneous networks (HetNets) have added layers of complexity that mean that changes to a network layer will not only change how that layer responds to the traffic it must convey, but it will also change the way that layer interacts with other layers. For example, changing an L TE layer may make it more or less attractive at a given location to traffic on the 3G network, and vice-versa.The number of tunable parameters is now enormous. For example, tuning just two parameters on each of 100 cells – where each parameter has 10 possible values – creates 10200 different ways these cells could be configured. That’s more than the number of atoms in the observable universe!Limitations of Network-Centric OptimizationThe three main challenges put a spotlight on the limitations of current optimization methods. While networks have become increasingly complex and dynamic, most optimization efforts are still primarily network-centric: a problem is located using network statistics and then adjustments are made to the network parameters to solve the problem. This network-centric approach of characterizing a problem using network statistics and then making macro site parameter adjustments no longer works when optimization is needed on a more granular level. This approach is also less effective when the intention is to change the configuration such that the performance is improved, rather than solve a specific problem.T aking this a step further, most macro-based adjustments create and maintain a baseline for overall network performance, but do little to optimize performance for specific locations within the network at any given time. For example, workers based in an office might tend to use voice services during the morning but then leave their office during lunch hour and go outside. While outside, their usage might migrate away from voice to data services. This illustrates the changing nature of the services demanded from the network and where they need to be delivered. An effective optimization would have to configure the network to deliver an acceptable user experience for this cohort of users, not just during the work hours and lunch break, but also during the commute time, evenings,and weekends. At each of these times the usage profile will be different and the locations will generally change. T aking automation to the limit sees the network able to adapt its configuration as the day progresses in response to the changes in the demands placed on it.But current optimization methods can only see macro locations based on overall network metrics. This creates “blind optimization” where multiple types of users at the various locations around the network are blended into one as the network tries to optimize an entire area. Doing so creates an imbalance where some users will have more resources than they need, while others will experience impaired usability.Another limitation is the iterative approach toward optimization – making small, incremental changes over time – due to the inter-dependent nature of today’s networks. This ensures that changes will not have an adverse effect on the network, but it also means that improvements are small with no major step changes in optimization. Most of these changes are use case driven and analyzed in isolation. If there is a problem with VoL TE performance for instance, current methods typically try to solve the problem in isolation without considering how it will affect other parameters such as data performance or energy consumption.Drive testing is often used in network optimization. However, drive testing uses synthetic data and is OpEx heavy. It can also take a considerable amount of time and effort to come to a network design optimized for the drive test traffic rather than the commercial users of the network.Most of all, today’s network-centric methods only focus on the network itself and have limited ability to measure or enhance the subscriber experience of using a network.Benefits of Automated Subscriber-Centric OptimizationNew methods of optimization take the focus from the network to the subscriber. Subscriber-centric optimization considers where subscribers are located, how are they using the network, and what their current QoE is at any given time. But what must happen behind the scenes to make this happen?Several advancements have made subscriber-centric optimization possible. Solutions can now collect, locate, store, and analyze data from mobile connection events, creating a repository of location intelligence from all subscribers throughout a network. This location intelligence is then transformed to deliver subscriber-centric performance engineering and Radio Access Network (RAN) planning information.Most recently, subscriber-centric performance has been taken one step further by automating network performance optimization. This new automated subscriber-centric optimization addresses the network challenges created by interdependency, non-uniformity and complexity, and can keep up with increasingly dynamic traffic patterns.Where traditional network optimization is a manual process and can take up to two weeks per site, automated optimization can optimize multiple sites at a time within hours rather than days. Where the focus of manual optimization must be a single site or a small group of sites, automated network optimization can focus on much larger clusters of hundreds of sites. Not only is the focus on larger clusters of sites possible with an automated approach, it is desirable since the exponential growth in possible parameterizations gives the optimization more scope to find configurations that maximize the performance for the mix of subscribers and applications in that region of the network. Once the area for optimization is selected, goals and success criteria are then established. KPI constraints and trade-off levels are then selected.The optimization task is then scheduled – typically processing tens of millions of events based on subscriber data with granular location intelligence. If the results create the intended improvement, the changes can be actuated into the network. The result is a fast turnaround with major step improvements in optimization without adversely affecting other parts of the network.Because this approach is automated, it also greatly reduces the staffing and OpEx needed to optimize a network.Engineers are typically able to turn around optimized designs for large areas in a very short time. In addition, automated subscriber-centric optimization directly maps revenue to QoE to keep service providers profitable and subscribers happy.In addition, the problems of interdependency and non-uniformity are overcome. Automated optimization can analyze KPIs in parallel and predict the impact of planned changes to make sure other parameters of the network will not be negatively affected. Algorithms calculate effects by predicting gains and the net costs of those gains to the network before any changes are made; and predictive decision making can resolve contradictions before they happen.This more proactive approach saves time and prevents subscribers from experiencing negative events that are common using traditional, reactionary optimization methods. As an added benefit, the ability to use granular data at the subscriber level also allows network optimization to prioritize specific subscriber groups such as VIPs or high-net individuals.In summary , traditional methods focus on network and synthetic data, are OpEx heavy , and take considerable effort and time to come to a conclusion that does not necessarily end up addressing the QoE and capacity issues. However, using subscriber-centric data ensures optimization is aligned with subscriber QoE, is OpEx light, and delivers network designs in a significantly shorter timeframe.Automated Subscriber-Centric Optimization in ActionAutomated optimization sounds good in theory , but does it work with real network traffic? Let’s look at a few real-life examples.A major mobile provider wanted to maximize data coverage and throughput by reducing the number of L TE data users on 3G. The goal was to improve data traffic volumes on an already optimized network while maintaining 3G voice service. The network had 233 cells across two Radio Network Controllers (RNCs).CollectReview areasDrive test prioritized sites Collect PM statsCollectSelect ClustersEstablish Goals & Constraints Schedule T askTIME = 1 HOUR AnalyzeOvershootersDrop Calls, Congestion, Load BalancingAnalyzeProcess Milllions of events Granular Subscriber Data Automatic analysisTIME = 1 HOURActuateOnce manually correlated Then fix all sectors that have the issuesActuateActuate optimization Design into the networkTIME = 1 HOUR ConfirmRe-drive problem areas Make final a djustments Check PM statsConfirmCompare results with predictionsTIME = 1 HOURTraditionalAutomated OptimizationAutomated optimization used subscriber-centric intelligence to analyze the current subscriber usage. Based on this intelligence, power changes were made to 67 cells, and 63 cells received antenna e-tilt changes. The result was a 1.3-point improvement in the L TE quality index and a 24 percent increase in data traffic volume – all without affecting 3G voice services. See diagram on left of page.Another service provider wanted to maximize retainability of VoL TE calls and improve VoL TE throughput while maintaining accessibility. They also wanted to make sure the changes wouldn’t impact data services. Automated subscriber-centric optimization maintained VoL TE accessibility at 99.82 percent while improvingVoL TE retainability from 97.48 percent to 98.03 percent. At the same time, the mean throughput improved by more than 13 percent. This was a major step change improvement without impacting data services. See diagram on right of page.Voice and data are not the only uses of automated optimization. Service providers can also use it to optimize energy consumption to reduce OpEx without affecting subscriber services.One service provider wanted to reduce energy consumption on their 3G network at major sites in a city while ensuring service availability. Automated optimization analyzed subscriber usage at key sites outside of normal hours and analyzed handset carrier capability. The solution also determined the optimal carrier configuration per site to optimize energy consumption while maintaining service levels. The result was a reduction in energy costs by 25 percent, saving the provider an estimated $2.4 million annually.These step changes in optimization were all possible because real subscriber-centric intelligence was being used instead of traditional synthetic data. This allowed the service providers to see what the true results would be once the changes were actuated. Automated optimization allows engineers to establish specific goals to optimize aspects such as capacity, throughput, service drops or energy savings. Service providers can also focus on a select set of parameters for the most cost effective improvements such as only changing power or e-tilt parameters.Automated Optimization and 5GSubscriber-centric automation will become even more important as mobile networks become more complex.An analysis of a number of third-party industry resources shows that networks will see several major changes by 2025:y 720 percent increase in video trafficy 700 Billion things will be connected to the Internet y 66 times increase in wireless traffic y 2000 times increase in cloud objectsy 620 times increase in data analyzed in the cloudFor mobile networks, service providers are looking to 5G to keep up with this changing demand. Although a lot of progress has been made, the standards for 5G have not been finalized. But the capabilities 5G must have to keep up with demand are staggering. According to the GSMA, 5G must accomplish: y 1G to 10G connections to end points in the field y Have 99.999 percent availability y Reduce energy usage by 90 percentA key characteristic of 5G is the expectation that it will be able to deliver connectivity to an even wider range of devices than are seen today. This will include public safety , and a plethora of Io T devices such as connected cars, smart meters and asset trackers. These devices will have a vast range of different requirements in terms of bandwidth, latency , jitter, reliability , and dynamics that will require a network to tailor the service to each set of subscribers and devices. The specific requirements for each group further compounds the problem of network-centric optimization as it’s unable to discern the impact on each device and how it needs to change to meet QoE targets.There will also be a trend towards RAN centralization and virtualization with the functionality of a traditional base station being split between centralized units and distributed units. In many cases these will need to be configured, managed and optimized in the context of their topology and transport constraints, and the subscribers they are serving. Advanced, coordinated radio transmission and reception schemes will be available which will provide better resilience to adverse radio conditions such as poor coverage and interference, but will come at a cost by placing more demands on the transport network.10 10 10101010WIRELESS FIBER© 2017 VIAVI Solutions Inc.Product specifications and descriptions in this document are subject to change without notice.mobilenetworks-wp-maa-nse-ae 30186254 900 1017Contact Us +1 844 GO VIAVI (+1 844 468 4284)To reach the VIAVI office nearest you, visit /contacts.The advent of 5G will also bring more use of Network Function Virtualization (NFV) and Software Defined Networks (SDN) to deliver network infrastructure. This will also require configuration, management andoptimization. Other inflections such as Mobile Edge Computing will mean that functionality can be distributed and configured to meet constraints such as service latency and usage of transmission bandwidth.5G will need to coexist and interwork with older technologies such as 2, 3 and 4G. Networks will gain another layer that must work optimally with the older technologies so that devices are still able to achieve their QoE targets. Any system that automates network optimization must perform effectively by taking advantage of all the layers, managing the selection of each layer, and transitions between them such that it sweats the assets and drives performance.T aken together, these various developments make tomorrow’s network more powerful by allowing devices more ways to achieve their various QoE needs. But this also creates a problem for management and optimization since there will be many more parameters to tune, the number of possible configurations explodes exponentially , and finding the optimal configurations becomes much harder.The other impact of this increased configurability is the interdependency between different parts of the network. If changes are made in the RAN to address an interference problem, this may change the backhaul demands on a network. This issue is further compounded as some subscribers may derive service from different cells. The relationship between a 5G network and the 2/3/4G layers may change as subscribers derive a service from these other layers in addition to – or instead of – the 5G layer. In addition, more devices may be attracted to the 5G layer. The network load could change as a result and place more demands on virtualized core elements. Any optimization solution must be able to consider the holistic impact of configuration changes that are under consideration, as well as their ability to deliver the variety of QoE required by the different devices. Doing this effectively in the complex and configurable network will require advanced modelling of radio, RAN, transport and core elements along with mature configuration optimization capability to optimize the infrastructure and spectrum assets while delivering the required service.The only way for this to happen is to automate optimization using subscriber-centric methods as a starting point and then add more automated features as they become available. Eventually , networks will need to have the capabilities of self-configuration, self-optimization and self-healing to keep up with subscriber demand and maintain a high level of QoE.This may sound like science fiction, but it must happen and time is not on the industry’s side. Currently , most service providers are planning mass deployments of 5G by 2020. Some service providers are already planning to make smaller deployments in 2018 and 2019. This means that automated subscriber-centric optimization is not a “nice to have” feature, but a vital step toward future networks. It’s the only way service providers will be able to keep up with the complexity of networks and the dynamic traffic patterns of the future.。

目 录概述0101Wi-Fi 7 是什么 02Wi-Fi 7 的修订进度 02Wi-Fi 7 的技术目标0302技术白皮书Wi-Fi 7Wi-Fi 7 的新特性 0403总结 1404更快更高速 05 ·PPDU 改进 05 ·支持更大的带宽 07 ·更高的调制阶数 07 ·更高的空间流 08 ·小结08更高效、更灵活 09 ·多 RU 机制 09 ·多链路操作 10 ·多 AP 间协同调度 11 ·增强的重传机制 12 ·时间敏感网络13概述本技术白皮书主要介绍 Wi-Fi 7 引入的新技术和新功能。

名词解释Wi-Fi 7 是什么IEEE 802. 11be(Extremely High Throughput，简称 EHT)是修订中的下一代 Wi-Fi 协议， 即“Wi-Fi 7” 。

作为 Wi-Fi 6 的继任者，在协议修订之初，工作组定下最高吞吐速率超过 30Gbps、时延低于 5ms 的工作目标。

因此 Wi-Fi 7 引入更大的无线带宽(320MHz) ，更高阶的调制方式(4K-QAM) ，更灵 活的频谱利用方式(Multi-RU) ，更高的时空复用(16X16 MIMO) ，更多的链路操作(MLO) ，以及多 AP 协作等等新技术，使得 Wi-Fi 7 能够提供更高的数据传输速率和更低的时延。

各协议版本的信息Wi-Fi 7 的修订进度IEEE 802. 11be EHT 工作组已于 2019 年 5 月成立，802. 11be (Wi-Fi 7) 的开发工作仍在进行中。

目前， 第一版草案 Draft1.0 已经在 2021 年 3 月发布；Draft2.0 预计在 2022 年底发布；在 2024 年底 完成最终标准定稿。

TGbe 当前的进展P802. 11be PARWi-Fi 7 的技术目标下图是 802. 11be 项目授权请求 (Project Authentication Request, PAR) 的截图，指出 802.11be 功能目标：The main candidate features that have been discussed are:-320 MHz bandwidth and more efficient utilization of non-contiguous spectrum.- Multi-band/multi-channel aggregation and operation.-16 spatial streams and Multiple Input Multiple Output (MIMO) protocols enhancements.-Multi-Access Point (AP) Coordination (e.g. coordinated and joint transmission).-Enhanced link adaptation and retransmission protocol(e.g. Hybrid Automatic Repeat Request (HARQ).-If needed, adaptation to regulatory rules specific to 6 GHz spectrum.• 320MHz 的信号带宽，更高效的使用非连续频谱• 多频段、多信道聚合操作• 16 条空间流 MIMO • 多 AP 协同工作• 链路自适应增强和 HARQ 重传协议•新开放的 6GHz 频段(国内未授权)Wi-Fi 7 的新特性Wi-Fi 7 协议的目标是将 WLAN 网络的吞吐率提升到 30Gbps，并且提供低时延的接入保障。

面向VoLTE的TD-LTE技术白皮书（2013版）中国移动2013年6月目录1.前言 (5)2.发展愿景 (5)3.面向VoLTE的TD-LTE相关要求 (6)3.1无线网络方面 (6)3.1.1多频段组网 (6)3.1.2连续及深度覆盖 (8)3.1.3基站建设 (9)3.1.4网络性能 (10)3.1.5语音及数据业务互操作 (11)3.1.6 TDD和FDD融合组网 (11)3.2核心网方面 (12)3.2.1 EPC融合核心网 (12)3.2.2 融合用户数据HLR/HSS (12)3.2.3 IMS支持VoLTE/eSRVCC (13)3.2.4 DRA信令网 (13)3.2.5 电路域支持eMSC (14)3.2.6 LTE回传方案 (14)3.2.7 LTE流量服务 (15)3.3终端方面 (16)3.3.1 多模多频段 (16)3.3.2 VoLTE手机总体要求 (16)3.3.3终端互操作要求 (18)3.3.4终端国漫业务要求 (19)3.3.5逐步支持LTE-A部分功能 (19)3.3.6 用户卡 (19)3.4国际漫游方面 (20)3.5运营方面 (20)3.5.1告警管理 (20)3.5.2 安全管理 (21)3.5.3系统升级 (22)3.5.4设备维护重点功能 (22)3.5.5网络自组织 (22)3.5.6 网管北向接口方案 (23)3.5.7 OMC重点功能要求 (24)3.5.8 MR数据要求 (24)3.5.9 信令软采功能要求 (25)4.结束语 (25)附录1：技术要求汇总 (26)附录2：缩略语表 (37)1.前言结合产业和市场发展，中国移动发布近两年面向VoLTE的TD-LTE 网络发展技术要求，涵盖TD-LTE网络建设、终端、业务、用户发展等方面所需的端到端主要技术要求1，旨在高效推进TD-LTE产业端到端设备开发以更好的契合中国移动TD-LTE发展需求。

中国联通5G网络切片白皮书中国联合网络通信有限公司网络技术研究院2018年6月目录1引言 (1)25G网络切片需求及挑战 (1)2.1 市场发展需求 (1)2.2 网络挑战分析 (2)35G网络切片关键技术要求及解决方案 (3)3.1 5G网络切片整体架构 (3)3.2 E2E网络切片技术要求 (4)3.2.1 核心网子切片技术要求 (4)3.2.2 无线网子切片技术要求 (6)3.2.3 传输网子切片技术要求 (8)3.2.4 切片编排管理系统技术要求 (9)45G网络切片商业形态重构分析 (11)4.1 5G网络切片对业务及商业形态的影响 (11)4.2 5G网络切片典型业务场景及需求 (12)4.2.1 自动驾驶 (12)4.2.2 增强现实 (13)4.3 面向5G网络切片的网络演进及业务需求对接规划 (14)5总结和展望 (16)I 版权所有©中国联通网络技术研究院，2018中国联通5G网络切片白皮书1 引言5G时代，移动通信技术将成为社会数字化发展的强力催化剂，未来的移动通信将进一步发展并触及各种垂直行业，如自动驾驶、制造业、能源行业等，并持续在金融业、健康护理等目前移动通信已有涉及的行业进一步深入发挥作用。

移动通信网络潜力的进一步挖掘就取决于这些垂直行业提出的多样化的业务需求。

但业务需求的多样性同样为运营商带来了巨大的挑战，如果运营商遵循传统网络的建设思路，仅通过一张网络来满足这些彼此之间差异巨大的业务需求，那么对于运营商来说将是一笔成本巨大同时效率低下的投资。

基于这样的需求，网络切片技术应运而生，通过网络切片，使得运营商能够在一个通用的物理平台之上构建多个专用的、虚拟化的、互相隔离的逻辑网络，来满足不同客户对网络能力的不同要求。

由此，通过基于5G服务化架构的网络切片技术，运营商将能够最大程度地提升网络对外部环境、客户需求、业务场景的适应性，提升网络资源使用效率，最优化运营商的网络建设投资，构建灵活和敏捷的5G网络。

Wi-Fi 7 技术白皮书目录1 概述 (1)1.1 简介 (1)1.2 产生背景 (1)1.3 技术优点 (1)2 关键技术介绍 (3)2.1 物理层提升 (3)2.1.1 320MHz带宽 (3)2.1.2 4096-QAM调制 (4)2.1.3 MIMO 16X16 (5)2.2 多链路设备(MLD) (6)2.3 OFDMA增强 (8)2.3.1 Multi-RU (8)2.3.2 Preamble Puncturing (9)2.4 多AP协同 (10)2.4.1 协同空间重用(CSR) (10)2.4.2 联合传输(JXT) (10)2.4.3 协同正交频分多址(C-OFDMA) (11)2.4.4 协同波束赋形(CBF) (11)3 总结 (13)3.1 更高吞吐速率 (13)3.2 更低时延保障 (13)3.3 更强高密能力 (13)4 缩略语 (14)i1 概述1.1 简介当前全球有近200 亿的Wi-Fi 设备正在使用，Wi-Fi 已成为生活、工作中不可或缺的一部分。

在实际应用中，Wi-Fi 协议所传输无线流量，已占到无线总流量的90%。

海量数据快速、安全传输受益于巨量Wi-Fi 设备高效、安全、可靠地工作，而Wi-Fi 设备高效安全工作的灵魂在于802.11 协议的全面支撑。

1.2 产生背景回顾802.11 协议发展历程，初版802.11 协议速率仅为2Mbps。

802.11b 使用新的编码形式，将速率提升到11Mbps。

802.11a 利用新的5GHz 频段，引入OFDM 技术并采用64-QAM 调制将无线速率提升到54Mbps。

802.11g 将802.11a 的技术同步推广到2.4GHz 频段，2.4GHz 频段也能到达54Mbps 的速率。

802.11n 时代，MIMO 作为一项重大技术被引入WLAN 协议，同时采用更宽的40MHz 带宽，将WLAN 速率提升到了600Mbps。