Wi-Fi has moved from a nascent technology to one that is widely accepted and become so commonplace that we wonder how we ever functioned without it.
It started from autonomous access points and was followed up by controller-based architecture (with a centralized controller and thin access points). And, as we learned from the challenges in deploying Wi-Fi and the ability of the environment to impact user experience, companies have constantly tried to innovate. Some focused on building dynamic channel or power planning, some built controller-less networks, and others tried to make it work in single channel. (Don't deploy single channel until you have read the challenges here.)
Of course, the discussion around the evolution of Wi-Fi architectures is not complete without talking about the adaptive beamforming antenna technologies. This technology automatically adjusts to changes in the radio frequency (RF) environment, providing stronger signals to the client. Wi-Fi standards also have been evolving constantly to enable greater user experience. (Read about 802.11ax.)
A lot of development has also happened around improving computing real-time location of Wi-Fi clients, as well as business applications using the indoor location. And, of course, with everything moving into cloud, Wi-Fi controllers and management moved into the cloud, too.
Time for autonomic wireless Wi-Fi network management
Yet, even after years of evolution and innovation, vendors avoid conversations centered around the guarantee of quality of experience for wireless users. Wireless networks today still requires hands and heads. The effort and time that goes into their support is significant. There is a need for an autonomic wireless management system that “watches” the network, “understands” normal functioning, “analyzes” real-time performance against norms and then “acts” to automatically solve known problems.
The problem is that the data source in a wireless network is huge. The data varies at every transmission level. There is a "data rate" of each message transmitted. There are "retries" for each message transmitted.
The reason for not being able to "construct" the received message is specific for each message. The manual classification and analysis of this data is infeasible and uneconomic. Hence, all data available by different vendors is plagued by averages. This is where I believe artificial intelligence has a role to play.
Deep neural nets can automate the analysis and make it possible to analyze every trend of wireless. Machine learning and algorithms can ensure the end user experience. Only the use of AI can change the center of focus from the evolution of wireless or adding value to wireless networks to automatically ensuring the experience.
Software-defined networks (SDN) will play a key role in enabling the programmability of the Wi-Fi network; however, SDN is not the end goal. The end goal of the programmability (or SDN-ization) of wireless networks is still the enablement of a self-managed wireless network—a network that proactively ensures the end user's mobility experience.
Of late, AI is graduating from science fiction to reality. Cheap computer processing power has made it real. I am convinced that by 2020, enterprise Wi-Fi will be significantly AI-based.
I invite Wi-Fi engineers to supplement their domain expertise with AI tools so that we can free up systems administrators from sitting at a console or waiting for alerts. That is the only way to drive down IT support and administration costs over time. The fully autonomic systems that I envision will take on more of the characteristics of human intelligence for managing Wi-Fi. Until we see that product, we can use this as a yardstick for measuring the evolution of Wi-Fi management products.
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