How RUCKUS Uses AI to Transform Network Performance
Since 2022, Artificial Intelligence has become a prominent tool in society, providing swift and substantial answers to assist with tasks such as reasoning, decision making, pattern recognition or problem solving.
In its infancy, it was unreliable and only useful for smaller requests. It would often struggle giving the correct answer and would create images that from afar looked accurate but after a brief examination, you could tell it was made by AI.
Nowadays however, AI has naturally evolved and whilst it gives accurate answers to questions and can generate images which are far more realistic, it is also capable of more complex tasks such as creating videos and music that take more than a brief glance to know it’s AI.
For RUCKUS, AI is invaluable, several of the products and services are known to use AI or more specifically, machine learning to improve efficiency and speed.
Machine Learning
First of all, let’s look at the value of machine learning:
As a part of AI, machine learning is not independent. AI is the overall umbrella of which ML is only a part, it looks at patterns in data and improves decisions or predictions on computers without being programmed to do so and will work out situations independently.
As modern networks become increasingly complex and dynamic, RUCKUS has adopted machine learning as part of its strategy to deliver future‑proof technology and remain a leading connectivity provider. These are some of the ways ML supports RUCKUS:
- Smarter Wi-Fi Performance
- ML watches and reacts to traffic patterns much faster than a human and adjusts channels to optimise power levels and efficiency
- Supports adaptive antenna technology
- Learns thousands of patterns from antennas and works out which pattern works best for each client
- Improved User Experience
- Instead of focusing on only throughput, ML helps RUCKUS focus on real user experience
Wi-Fi is complicated and good performance from Wi-Fi is the most important aspect to a user. A user wants to have as few dropouts as possible as well as a fast, consistent connection. Machine learning is critical for performance because Wi-Fi environments are constantly changing and too complex for manual tuning by human control. They are capable of directly improving end-user experience even under heavy load. From using the data of real-world usage patterns, machine learning acts on these patterns swiftly to optimise radio settings and adapt in real time. The change from being a reactive system managed by an IT team to a proactive self-optimising platform is crucial for many environments. ML continuously optimises itself, removing the need for constant manual adjustment therefore environments such as education and healthcare can stay on top of time-conscious extreme loads where it’s known for being more unpredictable. The scale in which ML can operate in is not limited either – whether it’s a large campus or multi-site hospitals, consistent Wi-Fi performance is available without increasing operational overheads.
ML supports adaptive antenna technology by letting the system learn and adapt as it goes along, optimising signal transmission depending on what’s happening in the real-world rather than being rigid and stuck with certain rules. Algorithms analyse continuous feedback from every transmitted packet looking at various points: signal quality, interference, error rates and client location. From this data, ML will then determine which antenna pattern delivers the best performance for each device individually. As most environments don’t remain the same, for instance new devices connecting to servers or an increase in interference, the model adapts in real time, making decisions based on the environment it finds itself in, selecting the antenna patterns which reduce the noise, maximise signal strength and will be most reliable. The longer ML has been enhancing the antenna, the faster and more accurate the antenna will become in complex, high-density environments resulting in better coverage, higher capacity and reliable Wi-Fi performance without manual tuning.
For the end-user, they wouldn’t even notice if the antenna switched, all of the elements ML takes into consideration are done so quickly and proactively that the connection for the user remains constant, smooth and fast even when there is a busy or unpredictable environment around them. Essentially, ML takes away the friction putting the user at ease and instilling confidence in the technology allowing them to focus on what they need to instead of dealing with technical problems.
What RUCKUS Products use Machine Learning?
RUCKUS uses ML across several of its products and services including: access points, RF optimisation and cloud network management platforms. These are what deliver the self-optimising Wi-Fi and proactive connectivity mentioned previously.
Across the access points range available there are – indoor R-Series, outdoor T-Series and hospitality H-Series. All modern access points include BeamFlex+ adaptive antenna technology meaning AI can keep the connection secure and smooth whilst users connect to the network, ML selects the best antenna pattern per client in real time enabling the best performance in the high-density environments.
RUCKUS One, the cloud management platform, also uses this technology to provide intelligent, proactive cloud network management. By observing the data produced from access points and switches, ML learns what makes the performance optimal and works to always maintain that level. With all this information, RUCKUS One can detect any anomalies and other issues before they are even flagged and act on them. From the AI-driven recommendations and automation also executed by ML, RF settings as well as troubleshooting are optimised and the result is, the platform just like the antennas, is self-learning and significantly less complex to run as a result.
Conclusion
In summary, machine learning has become a key development for modern networking and is fundamental to how RUCKUS delivers intelligent, high‑performance Wi‑Fi. By embedding ML across access points, adaptive antennas, RF optimisation and cloud management platforms like RUCKUS One, networks can learn, adapt and optimise themselves in real time. This results in more reliable connectivity, reduced operational complexity and a consistently strong user experience even in busy and unpredictable environments. Ultimately, machine learning allows RUCKUS to provide future‑proof, self‑optimising networks that work seamlessly in the background while users focus on what matters most.
If you would like to discuss anything RUCKUS related, get in touch with us sales@purdi.com or give us a call 01488 647 647.