DDQN based Routing Algorithm for IRS-Assisted MANET Without Explicit CSI

Intelligent reflecting surface (IRS) is a promising technology to reconfigure the wireless channel cost-effectively, thereby improving transmission reliability in mobile ad hoc networks (MANETs). Prior works related to IRS primarily rely on channel estimation for configuring IRS, which, however, will introduce additional overhead and impact the efficiency of IRS-assisted MANETs, leading to increased delay and energy consumption during data transmission. To overcome this difficulty, we propose a multi-IRS-assisted double deep Q-Network (MIRS-DDQN) routing algorithm to find paths with higher end-to-end data rate and lower energy consumption. Routing packets are designed to collect experience tuples for DDQN to optimize the joint routing and transmit power selection policy. Moreover, these packets are also used to execute a blind beamforming strategy to configure IRS without incurring additional communication overhead. In particular, the IRSs can effectively enhance the links related to the IRS-assisted nodes and thus provide better solutions for DDQN to find an energy-efficient path with higher end-to-end data rate. Simulation results are presented to demonstrate the advantages of the proposed algorithm as compared to benchmark schemes in terms of end-to-end delay, energy consumption and end-to-end data rate.