Optimal Mobile IRS Deployment with Reinforcement Learning Encoder Decoders

Cellular deployment of new generations faces a coverage challenge due to the non-line-of-sight (NLOS) between clients' devices and base station (BS). Therefore, relaying on using the emerging technology; intelligent reflective surface (IRS) to reconfigure wireless signal propagation is considered the best solution that can address the mentioned challenge. Additionally, choosing the position of the IRS is not an easy task as the clients are mobile. Hence, there is a need for an efficient model to elect the best positions of the IRSs for a better network performance. In this work, two fold model is proposed to provide an automated solution to optimize IRS positions. The first one is the mixed integer linear programming (MILP) that solves the IRS positions problem in a classical way. Whereas the second one is based on the reinforcement learning optimization (RLO) with complex encoder and decoder network architecture to provide fast learning of the MILP results with a low mean square error. The proposed RLO model's validity is studied using 10 days of mobile dataset and actual cellular BSs' positions in the city of Rome (Italy). This study is based on the use of long short term memory (LSTM) and gated recurrent unit (GRU). The results show a significant performance of the proposed model based on LSTM compared to GRU.