# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Optimization and Design of IRSs
#### 3.3.4 Multiuser MISO/SIMO Scenarios
Furthermore, the authors propose an implicit channel estimation, which is generalizable and leads to an efficient learning to maximize the sum rate by considering a small number of pilots. In [74] a downlink multiuser IRS-assisted MISO system is considered. Both perfect and imperfect CSI scenarios are addressed under the assumption of LOS between the common AP and the users. For the perfect CSI setup, an algorithm is proposed to maximize the weighted sum rate of the system by utilizing the fractional programming technique. This algorithm is then extended for the imperfect CSI setup. The simulation results are presented in terms of the weighted sum rate and show the performance gain offered by the proposed algorithms over benchmark strategies without IRS. However, these algorithms present high computational complexity. In [75], a multiuser MISO multi-IRS-assisted system with unavailable direct link conditions between the users and the BS (NLOS conditions). The IRS phase-shift matrices are configured by exploiting the statistical CSI in an imperfect CSI scenario. In particular, two ML algorithms have been proposed, which learn the statistical CSI from the historical channel observations. Numerical results show the largest gains in terms of rate when the channel randomness is low. In [76] the authors investigate the IRS design under imperfect cascaded BS-IRS-user CSI in a multiuser MISO system with LOS between both ends. They aim to minimize the transmit power subject to the rate outage probability constraints by considering a statistical CSI error model. The minimization problem is reformulated by following a Bernstein-type inequality and has been solved via an AO framework. In [77], a cognitive radio (CR) MISO downlink IRS-system is considered. Specifically, a single secondary user is coexisting with multiple primary users and multiple IRSs are deployed to enhance the EE and the SE of the system. The authors aim to maximize the achievable rate of the secondary user subject to a total transmit power constraint by considering imperfect CSI. Simulation results show the system performance improvement by including the IRS technology in the CR network. The authors in [78] propose a DL method for online reconfiguration of the IRS in a SIMO complex indoor environment. The IRS phase-shift matrix configuration is set according to the receiver position to maximize the rate. The simulation results are presented in terms of the rate and the MSE between the output of the NN (the IRS phase-shift matrix) and the optimal phase-shift matrix for a given user position. The authors in [79] consider an IRS-aided multiuser MISO downlink system, where the transmit beamforming and the IRS phase-shift matrix are jointly designed to maximize the system sum rate. The solution is a DL-based approach to perform the joint design. Specifically, a two-stage NN is implemented and trained offline in an unsupervised manner, and it is then deployed online for real-time predictions. Simulation results exhibit substantial reductions in the computational complexity with satisfactory performance compared to conventional iterative optimization algorithms as in [74] (see [79], Table 1). The work in [80] considers the use of an IRS to enhance anti-jamming communication performance and mitigate jamming interference by properly adjusting the IRS elements in a multiuser MISO system under NLOS conditions between the users and the BS. The problem formulation is performed as a joint optimization of the power allocation at the BS and the IRS phase-shift matrix while considering quality of service (QoS) requirements for legitimate users. A fuzzy win or learn fast-policy hill-climbing (FWoL-FPHC) approach is proposed to jointly optimize the anti-jamming power allocation and the IRS phase-shift matrix. Simulation results show that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system performance (measured in terms of rate) and the transmission protection level compared with the mmWave massive MIMO approach presented in [81].