# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 3 Physical Layer in 5G
### Massive MIMO and Beamforming
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Methodology** & **Advantages** & **Future Work** & **Related Work** \\ \hline Decreasing bit error rate & Approximate message passing algorithm for uplink detection & Efficient uplink detection and trade-off between complexity and performance & Large mMIMO & [265] \\ \cline{2-5}  & Training-based blind channel estimation & BER count & Complex algorithm & [266] \\ \hline Spectrum sensing & Direct localization algorithm based on source and location & Minimizes execution time and enhances spectrum accuracy & Higher computational complexity & [267] \\ \cline{2-5}  & Match filter pre-coding techniques for performance analysis of SE and BS antennas & Improves throughput and spectral efficiency and spectral efficiency & Enhanced channel information is required for the pilot signal & [268] \\ \hline Receiver design & Multi-user MIMO pre-coding schemes & Flexibility in system design & Limited to LOS environment only & [269] \\ \cline{2-5}  & TDD realization based on zero forcing and max ratio combining schemes for uplink M-MIMO system & Spectral efficiency improvement and design condition depends upon number of antennas and pilot reuse factor & Limited for small number of antennas & [270] \\ \cline{2-5}  & Virtual uniform linear array and uniform cylindrical array & Better performance close to that in i.i.d. fading rayleigh channels & Propagation delay should be included & [271] \\ \hline Channel modeling & Gauss-Markov Rayleigh fading channel model in time-selective channels & Aggregate-rate achieved optimum results & Interference effect is not considered & [272] \\ \cline{2-5}  & Designed mMIMO correlated channel using MATLAB for pilot contamination & Achieves better performance by increasing more antennas at BS & Correlated channels reduce the overall performance & [273] \\ \cline{2-5}  & Scheduling algorithm based on the downlink mMIMO system along with zero forcing (ZF) beamforming approach & Better results in terms of error performance, sum rate, throughput, and fairness & Need to test on more realistic model and for multi-antenna users & [274] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of studies on massive MIMO. Several efforts have been devoted to reducing the self-interference (SI) in the massive MU-MIMO system [275]. Zero-forcing (ZF) is assumed at the base station (BS), and maximum-ratio transmission (MRT) and maximum ratio combining (MRT/MRC) schemes are used to perform linear processing techniques. The authors proposed a precoded self-interference method by deploying orthogonal sequences and downlink precoding. This helps to convert the SI channel into the signal channel while having lower dimensions. Furthermore, by knowing the channel estimation, the SI suppression occurs through the combination of SI removal and the large-scale antenna linear processing (LALP) technique [276]. In [277], the authors performed pre-coding methods at a remote radio unit (RRU) to reduce the frequency hopping (FH) capacity. The spatial traffic and queuing model was used to achieve a relaxation in FH capacity through user traffic and statistical analysis. A double-layer decoding technique is used in [278] to remove inter-cell coherence and interference in massive MIMO systems. The algorithm determined the potential of large-scale fading to reduce the inter-cell interference in MIMO networks over a spatially correlated Rayleigh fading. Numerous SE expressions are derived from understanding the effective use of power resources and large-scale fading decoding (LSFD) vectors. Results show that LSFD can reduce pilot contamination while exploiting less computational complexity and an improved sum of SE vectors for all cells. Two-layer decoding provides better results than one-layer decoding [279].