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In today's digital world, having a reliable and efficient network is critical for businesses to stay competitive. With the increasing demand for bandwidth to support various data-intensive applications such as video streaming and cloud-based applications, managing radio resources has become a complex task for IT. Traditional methods of radio resource management (RRM) can no longer keep up with the demands of modern networks. Fortunately, Artificial Intelligence (AI) and cloud technologies are now offering an effective solution to this challenge. Let's explore the benefits of AI-driven cloud-based RRM and how it is revolutionizing the way networks are managed.
Traditional Radio Resource Management
Wi-Fi radio resource management (RRM) is responsible for efficiently allocating and managing radio resources in Wi-Fi networks. It is crucial for optimizing network performance, ensuring that end-users receive a consistent quality of service (QoS). However, traditional RRM methods have their own shortcomings. Among others, interference, channel congestion, inability to handle dynamic environments, lack of coordination, load balancing among neighboring Wi-Fi access points come to mind. Typically, traditional RRM solutions base their decisions on fixed thresholds and rules, which may not account for changing network conditions. This has led to suboptimal network performance and increased operational costs.
What is AI-Driven Cloud RRM?
AI-Driven Cloud Radio Resource Management is a technology that combines the power of artificial intelligence (AI) and cloud computing to optimize the allocation and management of radio resources in wireless communication networks. It leverages machine learning algorithms to analyze copious amounts of data and make informed decisions on how to allocate radio resources, increase capacity, and reduce interference. Cloud-based infrastructure provides a scalable, resilient, and cost-effective way to accomplish the same.
Benefits of AI-Driven Cloud RRM for IT
AI-Driven Cloud RRM offers numerous benefits for IT. 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.
Let us take one example - In the world of 2.4 GHz, it is easy, there are only 3 channels that have non-overlapping frequency space. How difficult can it be to plan? You do not have to be a professional wireless engineer to get your channel planning right. Now, look at 6 GHz and call me later! Without being funny, a glance at that from the perspectives of available channels (59 at 20 MHz wide) and channel width options (6 different channel widths) reveals that it is impossible to manually optimize channel and channel width parameters required for a finely tuned Wi-Fi network.
Moreover, not all enterprises have the wireless RF professionals available to tune these settings across the network. For a busy Wi-Fi administrator, sub-optimal conditions often go undiscovered until an end-user escalation. With AI-Driven RRM, IT can optimize network performance by making real-time decisions based on changing network conditions. This leads to increased network efficiency, reduced latency, and improved QoS. As mentioned above, by leveraging Cloud infrastructure, IT can benefit from scalability and resilience, allowing them to handle more traffic at a fraction of the cost.
Benefits of AI-Driven Cloud RRM for end users
With the introduction of AI-Driven Cloud RRM, end user 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 end 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.
How It Works?
Broadly speaking, AI-Driven Cloud RRM follows a four-step process: data collection, analysis, decision-making, and action. The system collects data from various sources, including network traffic, user behavior, RF topology, and device performance. 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, unknown neighbor access points, and traffic patterns to jointly optimize channel and channel width.
The system leverages Machine Learning algorithms, graph algorithms, and statistical models that run in the cloud to jointly model and predict optimized channel and channel widths to minimize co-channel interference to the lowest level possible level and guaranteeing zero interfering links for RUCKUS access points (APs) whenever theoretically achievable.
Futureproofing with AI-Driven Cloud RRM
Let’s be honest, how many times in a week/month/quarter/year does a Wi-Fi engineer think about “optimizing” their network from the perspective of radio resource management? The typical answer is zero. They do not tinker with it if it is working. Contrary to that, with AI-Driven Cloud RRM, network conditions are continuously monitored.
When there is an opportunity to improve upon a sub-optimal configuration, the IT 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 an AP pool. In layperson's words, AI-Driven Cloud RRM is like a 24x7x365 CT scan of your entire network from RF perspective. Conversely, IT administrators can choose to roll back to a previous channel configuration for any reason, ensuring that the control of the radio network is always with the IT staff.
Future of AI-Driven Cloud RRM
The future of AI-Driven Cloud RRM looks promising as more and more wireless administrators are adopting this technology to manage their resources. As Machine Learning algorithms continue to improve, AI-driven RRM will become even more accurate and efficient. This will lead to better network performance, improved QoS, and reduced operational costs. Additionally, AI-Driven Cloud RRM can be applied to other areas of network management, such as predictive maintenance and security.
AI-Driven Cloud RRM is revolutionizing the way we manage radio resources in modern networks. It offers numerous benefits, including improved network performance, increased capacity, and reduced operational costs. By leveraging Machine Learning algorithms and Cloud infrastructure, AI-Driven RRM provides a scalable, resilient, and efficient solution for managing radio resources. As technology continues to evolve, we can expect to see more network operators adopt this solution to optimize their networks.
Don’t believe me? Give RUCKUS AI-Driven RRM a try today. If you are not ready to try it yet, consider scheduling a demo of RUCKUS AI and our AI-Driven RRM and find out for yourself.