# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 1 Introduction
Regarding the joint design of active and passive beamforming, in [26], the DRL model was built to estimate the beamforming vectors of the transmit BS and design the IRS reflect beamforming, intending to increase the MISO system sum rate at a minimal complexity. The work in [27] focused on a wireless secure communication system aided by IRS. The system secrecy rate was improved by optimizing the joint beamforming under different UEs' QoS requirements and time-varying channels. Additionally, prioritized experience replay and post-decision state methods were applied to enhance the learning and secrecy performance. Note that most of the above studies were conducted under a basic communication system model with only a single reflecting surface deployed or a single active user served. They neglected the direct communication links between the source and the end-users. Moreover, the applicability of the conventional alternating algorithms to find the optimal beamforming configuration is restricted by high complexity. Furthermore, some works built and trained their ML-based models to predict their outputs based on fixed channel coefficients or selected their estimated beamforming, active or passive, from a pre-defined codebook. This can be unsuitable or not applicable to the deployed systems and even limit the system's performance due to the multiple antennas and IRSs. Thus, in this paper, we study a wireless communication system aided by multiple IRSs in which the deployed surfaces assist in communication between a source BS and multiple end-users, considering both the direct and indirect links. In wireless cellular networks, transmit power optimization is regarded as one of the most important factors in managing interference, extending the network lifetime, and maintaining connectivity [28]. Due to the battery life limitation at mobile stations, the transmit power has to be controlled to guarantee connectivity with the transmitting source while maintaining the UEs' QoS requirements. Hence, we aim to achieve the optimal configuration of the BS active beamforming and reflection beamforming of the IRSs that meets the UE's QoS while allowing the lowest transmit power consumption at the BS. Moreover, we design machine-learning-based approaches to find the optimal solution with lower complexity in the static UE and dynamic UE scenarios. The main contributions of this work are as follows. * To maintain a green wireless communication system, we formulate a minimization problem for the transmit power of the BS by jointly optimizing the beamforming of the BS and reflection phases of multiple IRSs under the QoS constraints of multiple UEs, while considering two scenarios: a static UE scenario and a dynamic UE scenario. * Under the case of the static UE scenario, we propose a new GRNN-based algorithm, while, under the case of the dynamic UE scenario, as the new GRNN-based algorithm is a supervised-learning-based approach that needs to collect a large data set to reach an optimal regression surface to predict solutions based on static locations for UEs. We then propose a DDPG-based algorithm that learns a policy for the joint beamforming prediction based on the dynamic surrounding environmental states, which is robust in the wireless environment. * Our numerical results demonstrate a power-saving system by adopting the proposed algorithms compared to the benchmark schemes. The time complexity of the algorithms is also presented, which shows the superiority of the proposed ones. Additionally, we show the effectiveness of increasing the number of reflecting elements in reducing the transmit power for the multiple-IRS-aided system. The remainder of this work is organized as follows: Section 2 describes the system model and the formulated optimization problem. Section 3 presents in detail the proposed algorithm for the static UE scenario, while Section 4 explains the proposed algorithm for the dynamic UE scenario. Then, Section 5 presents the simulation results. Finally, this work is concluded in Section 6.