## 4 Optimizing EE- and SE-Based Beam Training
### A Low-Complexity AB-HP  
To further mitigate the remaining interference at the DB precoder in a wireless cellular network's downlink transmission, the channel estimation is performed by the user transmitting a pilot signal of \(\sqrt{p}\) power to the BS. The power of the pilot signal, denoted by \(p\), helps in determining the quality of the channel. It is hard to figure out the right channel because the ADCs do not give very detailed information and are not very accurate. The corresponding channel estimation is obstructed by the inaccurately quantized output of the ADCs with low resolution. After that, a fully connected DNN is used to construct the objective AB-HP in DNN to investigate how to jointly optimize energy harvesting and SE in response to changes in the wireless channel to reduce overall beam training overhead while achieving high levels of energy or spectral efficiency performance. One approach to achieve this goal is to use DRL to select potential beams for beam training, as suggested in [28]. Depending on whether performance metric is used, the EE or SE values are \(V_{n}\in R^{M+1}\) for beam training. However, in mm-wave systems, the state vector in [28] and the action space in [27] both scale with the number of antenna elements, which can significantly lengthen the DNN's training time. Thankfully, at the BS, the correlation between the received signals from each antenna is introduced by the restricted number of scattering and the constrained physical space between the antenna \(\varphi\). Then, the almost undistorted quantization signals can provide additional channel information in the spatial domain for \(\beta\) to improve the channel accuracy. \[\hat{\text{h}}=\sqrt{\frac{p_{l}}{N_{tr}}}\sum_{n=1}^{M_{tr}}\left\{ \begin{array} []{ll}F^{rx}_{u,\,\varphi}&(\theta_{l,n,zoA},\,\,\varphi_{l,n,AoA})\\ F^{rx}_{u,\,\varphi}&(\theta_{l,n,zoA},\,\,\varphi_{l,n,AoA})\end{array} \right.. \left[ \begin{array}{ll}e^{j\varphi_{l,n}}&\sqrt{\kappa_{l,n}}\\ \sqrt{K_{l,n}}&e^{j\varphi_{l,n}}\end{array} \right]\left[ \begin{array}{ll}F^{ tx}_{u,\,\varphi}&(\theta_{l,n,zoD},\,\,\varphi_{l,n,AoD})\\ F^{tx}_{u,\,\,\varphi}&(\theta_{l,n,zoD},\,\,\varphi_{l,n,AoD})\end{array} \right], \tag{1}\]  
where h is the estimation of h, \(p_{l}\) is the power of the \(l\)-th cluster, the vectors \([F^{rx}_{u,\,\varphi}\,\,F^{tx}_{u,\,\varphi}]\)T and \([F^{tx}_{u,\,\varphi},\,F^{tx}_{u,\,\varphi}]\) represent the receive antenna and transmit antenna, respectively, to minimize  
environment because it does not utilize the way things are right now. On the other hand, based on the predictions from the high-resolution channels, the DNN is constructed by carefully selecting the reliable observations that correspond to the high-resolution ADC antennas [68]. The use of DNN in DRL allows for the creation of more intelligent beam training algorithms by extending the capabilities of conventional methods. Based on the targeted user's uplink received power, DRL is employed in [64] to jointly assign the best BS and beam resources to that user. As per reference [66], DRL can instantly assimilate information from its environment to choose the optimal beam pair for data transmission. A DL approach for a fully connected hybrid architecture in a multiuser situation is proposed by [29], demonstrating the ongoing research on using ML for hybrid precoder design. For a fully connected hybrid structure, [30] proposes unsupervised DL precoding and it suggests using CNN to create a partially connected hybrid design [31, 65]. Still, compared to the focus on fully linked hybrid architectures, there is not much study on partially connected ones. Consider employing DRL to select appropriate beam candidates for beam training to lower algorithm complexity. By balancing exploration and exploitation without retraining, the RBT-DRL approach efficiently optimizes EE and SE in mm-wave multi-user mMIMO systems, outperforming CNN-based HP and LSTM-driven RA approaches in terms of convergence, computational complexity, and adaptability [52, 69].