Combining Q-Learning and Multi-Layer Perceptron Models on Wireless Channel Quality Prediction

Abstract

One of the most complex challenges that wireless communication systems will face in the coming years is the management of the radio resource. In the next years, the growth of mobile devices, forecast (CISCO, 2020), will lead to the coexistence of about 8.8 billion mobile devices with a growing trend for the following years. This scenario makes the reuse of the radio resource particularly critical, which for its part will not undergo significant changes in terms of bandwidth availability. One of the biggest problems to be faced will be to identify solutions that optimize its use. This work shows how a combined approach of a Reinforcement Learning model and a Supervised Learning model (Multi-Layer Perceptron) can provide good performance in the prediction of the channel behavior and on the overall performance of the transmission chain, even for Cognitive Radio with limited computational power, such as NB-IoT, LoRaWan, Sigfox.

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“…Ultra-low power consumption as well as ubiquitous outdoor and indoor connectivity are fundamental aspects to ensure that the network of IoT devices is reliable over the years. To ensure smooth operations on IoT network, it is necessary to take into account various elements such as network topology, modulation techniques, complexity of the hardware, the use of the radio spectrum and regulations [10]. From this point of view it follows that context-awareness is a key element in IoT applications.…”