# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 3 Physical Layer in 5G
### Signaling Techniques for 5G  
Initially, 5G works in conjunction with 4G infrastructure based on release-15 non-standalone (NSA) standards. The NSA standard proposes a 5G base station (gNB) that replaces the LTE infrastructure eNB and an evolved packet core. The network hierarchy is executed with eNB as the master and gNB as the secondary working node. In a dual connectivity structure, the user can access both 5G and 4G-LTE [205]. Telecommunication operators introduce many options, but the main options for 5G execution are 2, 3, 4, 5, and 7. Currently, option 2 (stand alone-SA) and option 3 (non-standalone-NSA) are standardized, in which the 5G architecture provides 5G services using LTE architecture [206]. Following the guidelines in [207], Figure 3 shows 5G deployment options with frequency division and the supported existing architecture. Signaling techniques are capable of transmitting lower to higher data rates while considering several issues; i.e., slow beam-forming, lethargic synchronization consisting of large gaps in time slots, and pitiable channel efficiency as compared to existing techniques and conventional OFDM [208]. Generalized frequency division multiplexing (GFDM) improves the SE and weak out-of-band emission compared to the 4G network. Orthogonal time-frequency-spread (OTFS) is essential in 5G mmWave to provide less BER, high reliability, and flexibility for pilot design in a noisy environment. OTFS transforms the fading medium and time-varying characteristics of the wireless channel into nonfading and time-dependent wireless channels [209; 210]. In 5G mmWave, factors affecting the signal's quality are meteorological conditions, environmental surface, operational frequency, fading effect, the distance between the antenna and user, and baseband signals attacked by the signal's impairments. The presence of linear and nonlinear compression brings abrupt changes in signal originality. In-phase and quadrature-phase (IQ) impairments, frequency errors, and phase noise are significant physical layer challenges that are mandatory to maintain signal quality. The fundamental reasons are high range frequencies and the wide range of bandwidth usage, the hardware execution of modulated transmitters, and the fact that receivers turn into the non-ideal state [211]. Sampling circuit operations near sampling velocity create irregular noises,distortion, and nonlinear performance. Similarly, amplifiers, converters, and switches also distort the signal between the transmitter and receiver [212]. In a wireless medium, the error vector magnitude (EVM) is usually required to measure the RF signal's performance, and it determines waveform distortion, deformation errors, linear and nonlinear compression [213]. AI, ML, and DL-based techniques are anticipated to meet the 5G vision in the real world. The viability of ML-based 5G wireless communications has already been ensured using mobile edge computing [214; 215], mobile edge caching [216], big data analysis [217], and context-aware networking [218] standards. Current radio resource management (RRM) algorithms cannot stand up to the primary expectations of 5G due to obscurity [219]. In this regard, ML-based solutions enable reliable communication and automated, efficient, and accurate decisions for analyzing and prediction, and 5G security is only as strong as its weakest links. Various techniques enhance the physical layer performance of 5G; for example, ref. [220] proposed a loss function to increase the performance of neural networks in a communication system, and [221] proposed a multi-antenna and multi-subcarrier channel state information (CSI)-based novel channel sounder architecture to achieve an accuracy better than 75 cm for line of sight (LoS) for indoor user positioning in three dimensions.