Benefits of AI-Driven RRM for an End User

With the introduction of AI-Driven RRM, customer Wi-Fi client devices can expect to operate in interference-free RF conditions, when possible, and experience higher throughput and higher reliability with fewer retries and errors. This results in higher user satisfaction when connecting wirelessly to the network. Maximizing the channel bandwidth allocation in the interference-free environment also ensures optimal performance to support high-throughput and low latency applications.

Advantages of AI-Driven RRM for a Network Administrator

While professional wireless engineers routinely optimize their network performance by selecting channel and power settings in addition to tuning other available configuration knobs, this task is getting more difficult with the advent of 6 GHz spectrum. A glance at the newly introduced spectrum and the available channel and channel width options make it tedious to manually optimize channel and channel width parameters required for a properly tuned Wi-Fi network. Not all enterprises have the wireless RF professionals available to tune these settings across the network. For a busy network administrator, sub-optimal conditions often go undiscovered until an end-user escalation.

With AI-Driven RRM, network conditions are continuously monitored. When there is an opportunity to improve upon a sub-optimal configuration, the network administrator is presented with an optimized choice of channel and channel width in the form of an AI recommendation. With a single click, the network administrator can make changes to the network and apply the most optimal parameters to all the APs in a venue.

Prerequisites for AI-Driven RRM

How AI-Driven RRM Works

AI-Driven RRM analyzes interference information received from every AP in the network, the user configuration hierarchy, access point capability, historical data about access point radio activity, rogue access points, and traffic patterns to jointly optimize channel, channel width, and AP transmit power.

Like other AI functionalities, the AI-Driven RRM page includes basic information such as the current Venue RRM Health, date of the recommendation, summary of the recommendation, target zone, and status of the recommendation, as well as the ability for the network administrator to apply, revert, and mute a specific recommendation. Clicking the Date attribute for a specific recommendation displays the explainable AI recommendation in the details page. Shown here are the current configuration and the number of interfering links discovered in the venue. Additional information aids the network administrator in choosing whether to apply or revert the recommendation:

• What is the recommendation?
• Why is this recommendation being made?
• What are some of the potential trade-offs?
• What is the history of actions taken on this recommendation?

When a network administrator applies a recommendation, a scheduling calender is displayed to select a date and time for the channel and channel width changes to be applied. This gives control to the network administrator to pick off-peak hours when the network is less busy to make the change. Changes are applied at the chosen time using API calls to the controller from RUCKUS AI, and the scheduled time is stored in the system and used for future changes to channel and channel width if required.

After an AI-Driven RRM recommendation is scheduled and applied successfully for the first time, the system continuously monitors the network environment for any changes that may cause co-channel interference. Upon detection of such changes, the AI-Driven RRM algorithm automatically applies subsequent recommendations persistently. This automatic process continues indefinitely, unless the user chooses to revert the recommendation manually by selecting the Revert option. If the user reverts the recommendation, the original configuration is restored.

The More details option displays performance comparison between the current configuration including the number of interfering links discovered in a venue and the expected value (when the recommended AI-Driven RRM is applied) in a graph data structure. The below illustration displays 117 intefering links, that are reduced to zero after the RRM feature is applied.

After the AI-Driven RRM recommendation is applied, it is pinned in the list of recommendations. Once the recommendation is applied, the KPI panel moves into the monitoring state for 24 hours where AI-Driven RRM starts gathering feedback about the change that was recently applied. The AI-Driven RRM is always active and any subsequent changes to the RF network, if required, are made at the selected time.

Full Optimization and Partial Optimization with AI-Driven RRM

AI-Driven RRM provides the option to choose between full optimization and partial optimization of network venues depending on your configuration requirements. By default, AI-Driven RRM is enabled with full optimization criteria that consider a combination of channel plan, channel bandwidth, and AP Tx power while generating an RRM recommendation to optimize the venue. However, you can choose partial optimization option in which only the channel plan is optimized. The RRM recommendation with partial optimization will not consider channel bandwidth and AP Tx power. When you enable partial optimization, a new recommendation is generated within the next 24 hours only if a better channel plan for the venue is available. For more information on how to enable full optimization and partial optimization, refer to AI-Driven RRM.

AI-Driven RRM Considerations

AI-Driven RRM Behavior in 2.4 GHz

It is generally accepted and understood that 2.4 GHz is a crowded spectrum with only 3 non-overlapping channels. However, it is important because several clients still only support 2.4 GHz band. RF propagation characteristics unique to 2.4 GHz make it a useful choice due to its increased range.

Since 2.4 GHz band has only 3 non-overlapping channels (1, 6, and 11), it is likely that APs in this band will hear other APs and zero interfering links solution does not exist in dense AP deployments in 2.4 GHz. In this scenario, AI-Driven RRM will still guarantee lowest possible co-channel interference. It does not take action to turn off AP radios in 2.4 GHz band.

AI-Driven RRM with Dynamic Frequency Selection (DFS) Channels

AI-Driven RRM is aware of the constraints that DFS channels pose in 5 GHz spectrum usage. While the actual decision to operate in DFS channel is still done at an AP radio level after radar detection measures have been applied, AI-Driven RRM keeps track of radar activity on different DFS channels and intelligently crowd source this information across multiple APs within the same physical proximity. Based on this crowd sourced information, AI-Driven RRM may restrict the use of some of these DFS channels to avoid disruptions to end users. Of course, optimality in terms of zero interfering links and channel bandwidth selection will still be maintained.

AI-Driven RRM Operation at Venue and AP Level

The key insight fundamental to arriving at a zero interfering links solution (when possible) is to jointly optimize channels and channel widths. Computationally, this is a non-deterministic polynomial-time hard (NP-hard) problem that can be solved using RUCKUS patented graph algorithms.

AI-Driven RRM Interoperability in a Mixed AP Deployment

The AI-Driven RRM algorithm works on the information it receives from RUCKUS access points. Any third-party access point is treated as a rogue AP. These data points are fed into the computation to search for the best option for channel and channel width. These changes are recommended to the network administrator using the AI recommendation mechanism. There is no deauthentication action taken against rogue APs because the algorithms have built-in rogue AP avoidance. Even in the presence of rogue access points or third-party access points, AI-Driven RRM delivers the most optimum solution for interfering AP links and co-channel interference possible.

AI-Driven RRM AP Transmit Power

In high-density deployment scenarios, an ideal channel plan with no interfering links may not be achievable. To address this, the AI-driven RRM automatically detects the residual co-channel interference and provides recommendations for further reducing the interference by reducing the AP transmit power, while at the same time, ensuring that no coverage holes will be created. This reduces the possibility of neighboring APs interfering with each other's signals, resulting in an enhanced Wi-Fi end-user experience.

The image below displays an example of RRM AP Transmit Power Recommendation to reduce the AP transmit power in three APs to reduce the interfering links from five to two.

To download the RRM comparison report, refer to Downloading the RRM Comparison.

There are two types of recommendations that offer Tx power guidance to the user. The Wi-Fi client experience category of recommendations guides a user to adjust the Tx power setting for 2.4 and 5 GHz radios, mainly to encourage clients to move to a 5 GHz radio instead of a 2.4 GHz radio. This recommendation is also useful when APs have mesh networking enabled. While both recommendations may offer guidance about Tx Power, AI-Driven RRM recommends channel, channel width, and transmit power of radios to holistically reduce co-channel interference and maximize network throughput.

AI-Driven RRM with Automated Frequency Coordination

AI-Driven RRM is enhanced with configuration from Automated Frequency Coordination (AFC) for 6 GHz channel allocation in the US region.

AFC is a system designed to manage spectrum use in the 6 GHz band, maximizing spectrum access and minimizing interference between unlicensed Wi-Fi 6e/7 devices and licensed devices. AFC involves a registered database that contains information about all licensed users currently operating in the 6 GHz band in a specific area. When a new Wi-Fi device, such as an AP, wants to operate in the 6 GHz band, it must register with the AFC system, and thereafter must check in with the system every 24 hours, to obtain a current list of available channels on which to operate. These periodic checks ensure its operation will not interfere with registered devices already using that band. Standard power APs, especially when used outdoors, have a higher potential to interfere with existing 6 GHz users. Therefore, these APs must use the AFC system to protect incumbent operations from RF interference. The AFC system is crucial for maintaining harmony in the spectrum usage, allowing new and existing technologies to coexist without disrupting each other’s services. Without AFC registration, indoor APs operate in low power mode and outdoor APs cannot operate in the 6 Ghz spectrum at all.

AI-Driven RRM solves this by integrating AFC (based on AFC’s channel list) to provide better optimized network configurations. When the channel list is selected for RRM, instead of using only the channels defined in RUCKUS One, RRM will consider the AFC response data from the APs and compare the AFC channel list with the controller-configured channels. The overlapping or common channels identified through this comparison will be utilized to provide an optimized configuration recommendation.

Prerequisites for AI-Driven RRM with AFC