# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 4 Results of the Proposed Algorithms
### MISO Systems  
According to the results of [74], the PPO-based algorithm is used for joint active and passive beamforming for RIS-assisted MU MISO systems. They use S-CSI plots at the beamforming vectors of the BS and at the phase shifts at the RIS. According totheir simulation results for the low and moderate SNR, the S-CSI models achieve equal performance to the I-CSI models and quickly converge to compare with others of the same category. In addition, the PPO-based method outperforms the asynchronous advantage actor-critic (A2C)-based method [92] and is robust against receiving many random actions, resulting from the use of the clip function. The studying time for the S-CSI PPO-based is considerably lower than that of the I-CSI PPO-based algorithm. Regarding the transmission power effect of the BS, the performance of the S-CSI PPO-based approach is compared with the alternating direction method of multipliers (ADMMs) suggested in [75]. Furthermore, the I-CSI-based method validates its operation against the algorithm proposed in [93]. Although the performance of all algorithms is the same at the low SNR, when the SNR rises, the I-CSI PPO-based algorithm outperforms the S-CSI PPO-based algorithms, the A2C and ADMM-based algorithms. Furthermore, the above algorithms perform significantly better than the RIS random phase shifts and the no-RIS case. For the effect of the Rician factor on the average sum, the I-CSI PPO-based algorithm is compared with the algorithm in [93]. According to the results of [74], by increasing the Rician factor, the performance of all algorithms with S-CSI and I-CSI enhances. Specifically, for the S-CSI approach, the LoS link becomes dominant as it increases, the BS-RIS-UE link turns out to be more deterministic.  
In [92], the results show that in the proposed framework, it is possible to robustly train neural networks over RL with both value-based and policy-based techniques, non-policy, as well as policy methods and discrete and continuous domains. One of the main conclusions is that the use of peer actor-learners to inform a common model has a steadying effect on the learning process of the three value-based methods examined. Although this indicates that stable online Q-learning is possible without experience repetition, which was used for this objective in the deep Q-Networks (DQN) algorithm, it does not mean that experience repetition is not beneficial. Combining experience repetition into the framework of asynchronous RL could significantly increase the data efficiency of these methods by reprocessing old data. This leads to much quicker training times in areas, such as the open racing car simulator (TORCS), where cooperating with the environment is more costly than renewing the model for the architecture used.  
In [72], the problem of planning the transmission joint beam and phase shifts in an RIS-assisted MISO communication system was addressed. Only S-CSI is available at the transmitter and efficient algorithms maximize the system performance. For the case of the Rician fading, an iterative algorithm is used, and the convergence of the algorithm is established. For the Rayleigh fading case, closed-form designs are used. Finally, the proposed S-CSI-based algorithm achieves a similar performance to the algorithm that requires I-CSI.