无线电管理
瞻博网络 Mist AI 驱动型无线资源管理 (RRM) 介绍了瞻博网络接入点和瞻博网络 Mist 云中可用的机器学习技术。默认情况下,RRM 处于启用状态,大多数优化会在后台自动进行。
在云端,RRM 从 WLAN 或站点中的多个接入点收集数据,这些数据是 作为服务级别预期 (SLE) 的一部分收集的,例如 容量 SLE ,如以下视频所示。
Radio frequency environments are inherently complex and therefore challenging to control and optimize for the efficient transmission of data. Since the inception of radio frequency, or RF, radio resource management, also known as RRM, has been a long-standing technique used to optimize the RF radio waves that transmit network traffic in wireless LANs. However, multiple interference sources like walls, buildings, and people combined with the air servings of transmission medium make RRM a challenging technique to master.
Traditionally, site surveys have been used to determine the optimal placement of Wi-Fi access points and settings for transmit power, channels, and bandwidth. However, these manual approaches can't account for the dynamic nature of the environment when the wireless network is in use, with people and devices entering or leaving and moving about. Additionally, this challenge is compounded with random RF interferences from sources like microwave ovens, radios, and aircraft radar, to name a few.
But what if the wireless network itself could perform RRM on its own? What if it could detect and respond to both interference sources, as well as the movement of people and devices, and adjust the radio settings in real time to provide the best possible wireless service? That's exactly what Juniper has done with the AI-driven MIST wireless solution, using advanced machine learning techniques. Specifically, MIST uses reinforcement learning to perform RRM. In a nutshell, a reinforcement learning machine, or agent, learns through an iterative trial and error process in an effort to achieve the correct result.
It's rewarded for actions that lead to the correct result, while receiving penalties for actions leading to an incorrect result. The machine learns by favoring actions that result in rewards. With MIST wireless, the reinforcement learning machine's value function is based on three main factors that lead to a good user experience.
Coverage, capacity, and connectivity. A value function can be thought of as an expected return based on the actions taken. The machine can execute five different actions to optimize the value function.
These are adjusting the band setting between the two wireless bands of 2.4 GHz and 5 GHz, increasing or decreasing the transmit power of the AP's radios, switching to a different channel within the band, adjusting a channel's bandwidth, and switching the BSS color, which is a new knob available to 11 AX access points. RRM will select actions with maximum future rewards for a site. Future rewards are evaluated by a value function.
The various actions taken by the learning machine, such as the increase of transmit power or switching the band from 2.4 GHz to 5 GHz, together represent a policy, which is a map the machine builds based on multiple trial and error cycles as it collects rewards, modeling actions that maximize the value function. Again, keep in mind that the value function represents good wireless user experience. As time goes on, even if random changes occur in the environment, the machine learns as it strives to maximize the value function.
The benefits of using reinforcement learning are obvious. A MIST wireless network customizes the RRM policy per site, creating a unique wireless coverage environment akin to a well-tailored suit. While large organizations with multiple sites replicate their many locations as copy exact, these sites will naturally experience variances despite best efforts.
Reinforcement learning easily fixes this, delivering real-time, actively adjusting, custom wireless environments. We hope this episode helped to uncover some of the magic and mystery behind our AI-driven network solutions.
RRM 应用持续强化学习来分析多达 30 天的性能数据。因此,它可以识别一天、一周或一个月过程中发生的事件驱动趋势,例如,降低已观察到遇到来自某种相邻设备的频繁干扰的信道的优先级。除了创建较长的基线外,这种持续的观察和学习还可以防止静态Wi-Fi实施所继承的系统漂移和手动干预。
在单个接入点级别,RRM 通过对信道干扰等事件做出反应,确保最佳的信道优化。它还可以自动并立即对雷达命中做出反应,并调整发射功率或信道使用情况。
全局 RRM |
本地 RRM |
---|---|
计划按站点每晚自动运行。 |
对本地事件做出反应(相对于 AP) |
每个无线电频段触发手动 |
独立于云 |
使用强化学习 |
临时 – 根据需要运行 |
利用多日数据集做出明智的决策 |
包括以下事件:
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RRM 的另一个功能称为双频无线电管理。在这里,RRM 利用 AP 上的第三个(或第四个)无线电来识别不必要的 2.4 GHz 无线电,并自动将其转换为 5 GHz 频段(或 6 GHz)。这在高密度环境中特别有用,并且不会导致相邻AP增加其发射功率。
为了管理双频,本地 RRM 与相邻接入点合作,评估 2.4 GHz 无线电信号强度和密度(在给定区域内传输多少 2.4 GHz 无线电)。如果特定 AP 型号不支持双频,RRM 可以禁用 2.4 GHz 无线电,而不是将其转换为将流量驱动到 5 GHz 频段。请参阅无线电管理(双频)。
在组织级别和配置的站点级别,您可以通过手动配置要禁用的无线电、设置信道宽度和可用性等来覆盖自动设置。
如图 1 所示,某些默认值与国家/地区选择相关联。
