A Comprehensive MDP-Based Approach to Model and Optimize Discontinuous Reception (DRX) in Cellular IoT Networks

Due to the exponential growth of endpoints in the Internet of Things (IoT), new protocols have been proposed to utilize cellular infrastructures, allowing a large amount of IoT devices to communicate through them. These novel protocols make up the Cellular IoT (C-IoT). In C-IoT, the energy efficiency of endpoints is essential in order to reduce both operational cost and required maintenance. One method of energy reduction is discontinuous reception (DRX). DRX allows a device's radio frequency (RF) circuitry to turn off for brief periods of time. While off, the device experiences a tradeoff between saving energy and an increase in expected latency, which can be tuned by how long the device spends asleep. In this article, we model DRX as a Markov decision process (MDP). This MDP is solved using a low-complexity 'DRX-aware' value iteration algorithm, then verified through simulation and analytical analysis. Further, the energy-latency tradeoff is explored by varying the device's priority on either energy or latency in addition to varying the traffic intensity. Finally, a method of traffic estimation is applied, and the model's performance in an environment with time-varying traffic intensity is explored. This approach is compared with a reinforcement learning approach, showing that the traffic estimation approach is better suited to the problem of DRX optimization.