# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 3 Physical Layer in 5G
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Key Aspect** & **Related Work** \\ \hline mmWave channel characterization & 1. Power delay profile. & \\  & 2. Doppler effect. & \\  & 3. Multi-path and propagation. & [157; 158; 159; 160; 161; 162; 163; 164] \\  & 4. LOS and N-LOS communication. & \\ \hline Adaptive beamforming. & 1. Angle of arrival. & \\  & 2. Antenna training. & [165; 166; 167; 168; 169; 170; 171] \\  & 3. Adaptive beamforming. & \\ \hline  & 1. Overlapping sector. & \\  & 2. Cost effective. & [172; 173; 174; 175; 176; 177; 178; 179] \\  & 3. Sectorized antenna model. & \\ \hline  & 1. MIMO small cell combination. & \\  & 2. Inexpensive low-power component. & \\  & 3. High number of antennas per BS. & \\  & 4. Coherent superposition of waveforms. & \\ \hline  & 1. Active and passive SI cancellation. & \\  & 2. Improved spectral efficiency. & \\  Full duplex radio technology & 3. Decreased self interference (SI) and pathloss. & [187; 188; 189; 190; 191; 192; 193; 194] \\  & 4. Decreased crosstalk between Tx and Rx. & \\  & 5. Improved feedback and latency & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the related work on the physical layer in 5G wireless networks. In the recent past, the physical layer became an eye-catching topic for time-sensitive services due to its potential to provide more effective approaches for 5G and beyond networks [195]. ML-based techniques enhance the properties of the physical layer and make the process efficient and optimized. For instance, in [196], the authors proposed a CNN-based solution for intelligent communication in different situations, and [197] discussed channel estimation and detection using DNN, outperforming the minimum MSE-based joint approach in OFDM systems. In [198], the authors proposed an ML-based antenna design method to ensure the security of the IoT communication system, and [199] proposed a CNN-based technique to classify several key CSI parameters to adjust the physical layer parameters in noisy environment. In addition, ref. [200] proposed a DL model (CNN-DMA) to detect malware attacks based on a classifier--the Convolution Neural Network (CNN)--and [201] proposed a multi-scale convolutional neural network framework for wireless technique classification to improve the classification accuracy and obtain a higher convergence speed. Furthermore, in [202], the authors proposed a CNN-based equivalent channel hybrid precoding approach in mmWave massive MIMO systems to reduce complexity and improve performance, and [203] proposed a CNN-based multi-user authentication system to distinguish spoofers/attackers using CSIs and improve the authentication accuracy of MIMO-OFDM systems. ML-based algorithms can be used to estimate and predict channel behavior, the outcome of the encoding and decoding process, and suitable coding schemes accordingly. Based on these features and classifications, ML-based algorithms, ranging from simple to complex, can obtain the desired output and estimate the required performance. These efficient techniques can optimize the channel conditions and system loads, scheduling, transmitting, signal-to-noise ratio, and sub-code lengths [204].