Joint resource allocation for emotional 5G IoT systems using deep reinforcement learning

In emotional computing related IoT system, emotional sensors, as the IoT devices, are usually deployed to collect the emotional data from humans. The IoT devices need wireless connections to send the collected data to the server, that conducts the prediction to give user instructions. Mobile edge computing (MEC) is a promising technology to fit into this scenario. However, the IoT devices are usually short of energy supply and the local computation gives less accurate emotional computing results. To solve the problem, this paper intends to maximize the total energy efficiency of communication and computation within the MEC servers and sensors by jointly optimizing the allocation of channels and computing resources. The formulated problem is non-convex and usually solved through the successive convex approximation (SCA) method. Compared to SCA, deep Q network (DQN) method is used in this paper, which involves less computation cost to be more practically deployed. The simulation results show that the DQN solution outperforms the other benchmarking solutions, and the total energy consumption of the system is effectively reduced with a guaranteed emotional computing accuracy.