Deep Learning for IoT Communications : Invited Presentation

In recent years there has been a rapid evolution in research in the technology known as the Internet of Things or IoT. Consequently, this development has caused an increase in connected devices. According to Statista, the amount of IoT connected devices by the year 2025 will be 75.44 billion. Given this expected exponential rise in connected devices, this will cause an increase in the transmitted data by the year 2025 as well. The Data Management Solutions Review states that data creation will reach 163 zettabytes by 2025. These conditions will cause an escalation in data transmission which will cause problems such as latency, data rates, congestion, nonlinearities, and other complexities. While communication systems have performed well based on traditional mathematical transforms, there is a need to present new solutions to mitigate these problems. One potential solution is to resort to advanced Machine Learning (ML) techniques to help manage the rise in data volumes and algorithm-driven applications. The recent success of Deep Learning (DL) underpins new and powerful tools that tackle problems in this space. The unique parameters of DL techniques are capable of properly characterizing and categorizing complex signals being transmitted and received. This paper will investigate the optimization of communication systems at the physical layer (PHY) with future applications in IoT hardware implementation using a 1D Convolutional Neural Networks (CNN).