A Machine Learning Enabled MAC Framework for Heterogeneous Internet-of-Things Networks

Nowadays, an Internet-of-Things (IoT) connected system brings a tremendous paradigm shift into the medium access control (MAC) design. In this paper, we present a distributed MAC framework assisted by machine learning for the Heterogeneous IoT system, where the IoT devices coexist with the WiFi users in the unlicensed industrial, scientific, and medical (ISM) spectrum. Specifically, the superframe is divided into two phases: a rendezvous phase and a transmission phase. During the rendezvous phase, the gateway that is capable of machine learning predicts the number of WiFi users and the IoT devices by performing a triangular handshake on the primary channel. The prediction takes advantage of the deep neural network (DNN) model which is pretrained on our universal software radio peripheral (USRP2) testbed offline. The gateway allocates the frequency channels to the WiFi and IoT systems based on the inference results. Then, the IoT devices and WiFi users initiate data transmissions during the transmission phase. Furthermore, system throughput is analyzed and optimized in two typical scenarios, respectively. An optimized MAC framework is proposed to maximize the total system throughput by finding the key design parameters. The analytical and simulation results that are conducted using the ns-2 demonstrate the effectiveness of the proposed MAC framework.