Lte and wi-fi share unlicensed spectrum using coordinated learning
Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework
Networking and Internet Architecture
Summary
As more devices connect to the internet through cellular networks, there's not enough licensed radio space to handle all the data. To fix this, some cellular networks use the same frequencies as Wi-Fi, which can cause interference. The authors studied how multiple cellular and Wi-Fi spots can work together fairly in this shared space. They designed a smart computer system that uses learning to improve data speeds for both kinds of networks, even when they interfere a lot. Their computer tests showed better overall internet speeds and better experiences for users.
What this means in practice
- •For network operators: Optimize coexistence strategies between cellular LTE-U and Wi-Fi networks to increase overall data throughput in crowded urban areas using reinforcement learning coordination.
- •For iot system integrators: Improve broadband IoT deployments by managing interference between LTE-U and Wi-Fi to ensure higher data rates and reliability in shared spectrum environments.
Authors
José M. de C. Neto, Sildolfo F. G. Neto, Pedro M. de Santana, Vicente A. de Sousa
Abstract
Cellular broadband Internet of Things (IoT) applications are expected to keep growing year-by-year, generating demands from high throughput services. Since some of these applications are deployed over licensed mobile networks, as long term evolution (LTE), one already common problem is faced: the scarcity of licensed spectrum to cope with the increasing demand for data rate. The LTE-Unlicensed (LTE-U) forum, aiming to tackle this problem, proposed LTE-U to operate in the 5 GHz unlicensed spectrum. However, Wi-Fi is already the consolidated technology operating in this portion of the spectrum, besides the fact that new technologies for unlicensed band need mechanisms to promote fair coexistence with the legacy ones. In this work, we extend the literature by analyzing a multi-cell LTE-U/Wi-Fi coexistence scenario, with a high interference profile and data rates targeting a cellular broadband IoT deployment. Then, we propose a centralized, coordinated reinforcement learning framework to improve LTE-U/Wi-Fi aggregate data rates. The added value of the proposed solution is assessed by a ns-3 simulator, showing improvements not only in the overall system data rate but also in average user data rate, even with the high interference of a multi-cell environment.