# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 3 Physical Layer in 5G
### Massive MIMO and Beamforming
Beamforming techniques could be conventional methods: fixed (switched beamformer) and phased array (adaptive beamformer) [257]. In a phased array, there are two modes: (1) maximization mode (desired signal) and (2) minimization mode (interference signal). In analog beamforming, the first step is the conversion of the signal from digital to analog, with the implication of pre-coding to an analog signal, and then signal transmission over the air. Contrary to this, the implication of pre-coding to a digital signal, digital to analog conversion, and RF conversion is performed in series, and then the signal is transmitted over the air. In contrast, in hybrid beamforming, the precoding step is adapted in both domains (analog and digital) at both frequencies (radio and baseband), respectively, and so hybrid beamforming can benefit from the advantages of both analog and digital beamforming. Beam-forming technology enhances and boosts the SE by delivering an improved signal-to-noise ratio (SNR) [258]. A combination of other 5G antenna technologies--i.e., massive MIMO with beamforming--increases the coverage area, boosts the power of beams in the desired direction, and reduces the power of the beam for nearby subscribers to mitigate the interference issues. Besides this, it increases the carrier-to-noise ratio of the signal, helping in a noisy and attenuating channel environment and increasing the overall bandwidth capacity. Hence, the users can be served with higher quality and stronger signals [259]. On the other hand, hardware complexity (due to a large number of antennas), high processing in terms of digital signal processing chips and the design of mathematical algorithms, hardware costs, and increased power are required due to increased resource consumption. Scientists also present different Tx analyses to perform the multi-casting of the physical layer in the MISO downlink channel [260]. In Table 8, we summarize the related work for massive MIMO. Stochastic beamforming and the grouping of transmit beamforming with space-time diversity--i.e., Alamoudi space-time coding--create a rank two software-defined radio (SDR) beamforming scheme. According to the simulation results, the SNR of the SDR-based Alamouti scheme gives more effective results than conventional SDR (semidefinite relaxation) beamforming. Thus, a combination with the Alamouti scheme can improve the scale of the multicast rate in MISO [261]. In [262], researchers achieved full diversity through optimized quantizers  
Figure 5: 5G massive MIMO system. that are capable of beamforming in a multiple inputs, single output multicast medium. The considered quantizers for a two RX system, which can approach outage probability, lies under the complete channel state information. However as the number of receivers increases, it becomes difficult to achieve the same output and optimized beamforming. In another research work [263], the authors discussed the issues related to multicast beamforming in impaired channel state information (CSI) and their limited feedback results. The amount of feedback of CSI significantly increased from the receiver to transmitter in these networks. The authors suggested that lowering the number of cooperative aerials at the transmission side can improve the outcome through simulations. The authors also proposed a solution using principal component analysis (PCA), which acts as a reducing scheme that can compress the dimension matrix of CSI and reduce the amount of feedback. Furthermore, it is also capable of eradicating the complexity of the codebook search and achieving a tradeoff between structure performance and feedback overhead [264].