Joint Lifetime-Outage Optimization in Relay-Enabled IoT Networks-A Deep Reinforcement Learning Approach

Network lifetime maximization in Internet of things (IoT) is of paramount importance to ensure uninterrupted data transmission and reduce the frequency of battery replacement. This letter deals with the joint lifetime-outage optimization in relay-enabled IoT networks employing a multiple relay selection (MRS) scheme. The considered MRS problem is essentially a general nonlinear 0-1 programming which is NP-hard. In this work, we use the application of the double deep Q network (DDQN) algorithm to solve the MRS problem. Our results reveal that the proposed DDQN-MRS scheme can achieve superior performance than the benchmark MRS schemes.

Automated summary

This letter addresses the joint lifetime-outage optimization in relay-enabled IoT networks using a multiple relay selection scheme. The proposed DDQN-MRS scheme achieves superior performance compared to benchmark MRS schemes, as shown by our results.