Benefits of AI-Driven Cloud RRM for an End User

With the introduction of AI-Driven Cloud 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 Cloud 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 Cloud 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 zone.

How AI-Driven Cloud RRM Works

AI-Driven Cloud 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 recommendations, the AI-Driven Cloud RRM recommendations include information such as priority, date, category, summary, scope, type, status, details, and actions. Recommendation details highlight the current configuration and the number of interfering links discovered in a zone. Clicking the (Recommendation Details) icon displays the explainable AI recommendation, where the network administrator can find more information: what is this recommendation, why is it being made, and what are some of the potential trade-offs. Available actions include Apply, Revert, and Mute.

When a network administrator clicks the (Apply) icon available in the Actions column against each 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 SmartZone API calls to the controller from RUCKUS Analytics, and the scheduled time is stored in the system and used for future changes to channel and channel width if required.

The View Details option displays performance comparison between the current configuration including the number of interfering links discovered in a zone and the expected value (when the recommended AI-Driven Cloud 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 Cloud RRM recommendation is applied, it is pinned in the list of recommendations. If the network administrator reverts the recommendation, the original configuration is restored. Once the recommendation is applied, the KPI panel moves into the monitoring state for 24 hours where AI-Driven Cloud RRM starts gathering feedback about the change that was recently applied. The AI-Driven Cloud RRM is always active and any subsequent changes to the RF network, if required, are made at the selected time.

AI-Driven Cloud RRM Considerations

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

AI-Driven Cloud 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 Cloud 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 Cloud 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 Cloud RRM Operation at Zone and AP Level

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

AI-Driven Cloud RRM Interoperability in a Mixed AP Deployment

The AI-Driven Cloud 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 presence of rogue access points or third-party access points, AI-Driven Cloud RRM delivers the most optimum solution for interfering AP links and co-channel interference possible.

AI-Driven Cloud 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 Cloud 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 Cloud RRM AP Transmit Power Recommendation to reduce the AP transmit power in two APs to reduce the interfering links from three to one.

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 Cloud RRM recommends channel, channel width, and transmit power of radios to holistically reduce co-channel interference and maximize network throughput.