Augmented Wi-Fi: An AI-based Wi-Fi Management Framework for Wi-Fi/LTE Coexistence

Abstract

Recently, the operation of LTE in unlicensed bands has been proposed to cope with the ever-increasing mobile traffic demand. However, the deployment of LTE in such bands implies sharing spectrum with mature technologies such as Wi-Fi. Several studies have discussed this coexistence problem by suggesting that LTE implements different adaptation mechanisms that allow transmission possibilities to Wi-Fi. While such adaptation mechanisms exist, they still negatively impact Wi-Fi performance, mainly due to the lack of collaboration/coordination mechanisms that inform about the co-located networks' activities. In this paper, we propose a distributed spectrum management framework that enhances the performance of Wi-Fi, as a particular case, by detecting harmful co-located wireless networks and changes the Wi-Fi's operating central frequency to avoid them. The framework is based on a Convolutional Neural Network (CNN) that can identify different wireless technologies and provides spectrum usage statistics. Experiments were carried out in a real-life testbed, and the results show that Wi-Fi maintains its performance when using our framework. This translates in an increase of at least 40% on the overall throughput compared to a non-managed operation of Wi-Fi.

A General Approach for Traffic Classification in Wireless Networks Using Deep Learning

“…GWs. However, this can easily be extended to independent management domains where information exchange is allowed to enforce collaboration or/and cooperation among them as we demonstrated in our previous work [7].…”

“…One possible solution to the problem described above is that the GW 1 recognizes the radio technology that interferes and adapts its behavior accordingly [7], [32], [33]. However, this approach is not enough to increase spectrum efficiency as (i) the interfering/interfered technology may not include a mechanism to adapt or coexist, e.g., to change its frequency band to avoid the interference, and (ii) (iii) traffic prioritization can be only based on technology and not on application.…”

“…These assumptions limit the capabilities of TC systems in wireless networks using shared spectrum, e.g., in unlicensed bands. The users' traffic from one wireless network domain can be negatively impacted by users' traffic transmissions from other wireless networks without being noticed by the TC system as demonstrated in [7]. Contrary to a wired network, co-located wireless transmissions in the same spectrum band can generate Physical Layer (L1) packets that are not be detected by a receiver performing the TC task.…”

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

“…Recently, the authors of [190] study the Wi-Fi/LTE coexistence problem by assuming a legacy operation of the interfering technology (LTE), which degrades the throughput of the incumbent technology (Wi-Fi) due to the interference. They propose a distributed spectrum management framework based on ML techniques that provides a global view of the spectral resources in wireless environments and helps Wi-Fi identify which wireless technology is transmitting.…”

“…), non-adaptive modulation scheme, static non-application cognizant MAC, etc. • Wireless interference identification [128,169,[177][178][179][180][181][182][183][184][185][186][187][188][189][190][191][192][193] MAC analysis…”