# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 2 5G Technology
### Next-Generation Wireless Networks (NGWN)  
Regarding 5G and beyond, NGWN introduces various hypercritical challenges in the area of ultra-dense deployment heterogeneous networks (HetNets). Due to this factor, the upcoming wireless networks will be highly dynamic and complex [138]. Access points and low-cost end-user devices are growing drastically, and high mobility in this scenario results in a complicated and hectic communication process [139]. The propagation behavior of radio signals being broadcasted with exploded communication introduces spoofing and man-in-the-middle attacks. Nowadays, wireless data formation and utilization are progressively distributed, so the current paradigm is converging from people-centric to machine-oriented communications. To handle the future complex wireless operations, knowledge discovery, improved context-awareness, efficient data acquisition, and distributed computational resources using the adoption of AI principles and ML-based techniques are extremely important [140]. 5G technology is considered evolutionary, service-oriented, or revolutionary. As mentioned, the systems of 5G will be designed so that they can bear the worldwide wireless web (WWWW), commonly known as real-world wireless. This will provide seamless broadband connectivity and permit the support of flexible ad-hoc networks--i.e., dynamic ad hoc wireless networks (DAWN)--which would be a rich ecosystem of mobile applications and cloud-based services [141]. Fifth-generation networks use enhanced modulation schemes (OFDM uplink and downlink) for higher SE, and the subcarrier modulation may vary from QPSK, 16 QAM, and 64 QAM to 256 QAM, while the subcarrier spacing also varies from 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 240 kHz to 480 kHz; that is, depending upon 5 g numerology (\(\mu\) 0-4), carriers vary up to 3300. The massive intuitive antennas are critical factors in producing a high data rate and are committed to boosting 5G's capability for the new world [142]. Moreover, OFDM and its variants (COFDM, Flash OFDM, OFDMA, VOFDM, Vector OFDM, WOFDM), MC-CDMA, LAS-CDMA, Network-LMDS, UWB, and IPv6 play a significant role in supporting 5G networks [143]. Currently, 5G is providing advanced services and intelligent technologies such as AI, virtual reality (VR), augmented reality (AR), mixed reality (MR), and cloud computing which are being used to cater to the IoE behavior of the network. The 5G revolutionary view indicates that the existing infrastructure of previous generations (2G/3G/4G) is not suitable for advanced applications and services. Table 4 shows a comparison of 4G, 5G, and 6G with respect to network characteristics, applications, and use cases. To achieve the expected 5G vision, embedded architecture and intelligent designs are required in addition to emerging technologies; i.e., SDN, network slicing, and NFV located above the physical layer to support multiple services. NR wireless communication architecture is excellent for achieving ultra-reliable low latency communication, whereas other benefits from, i.e., eMBB and mMTC are also considered [144]. Low-density parity-check (LDP) codes and polar codes (PC) are regarded as the top-tier spectrum coding schemes for higher data rate networks such as 5G-NR [145]. The international telecommunication union (ITU) standardization group (ITU-T SG-12) endorsed new updating and defining recommendations to achieve QoS and QoE in the NGWN [146]. The ITU-T group has already been settled and named as a focus group on ML for future networks including 5G to mitigate and overcome the latest challenges of NGWN and to manage its operations [147]. In Figure 2, NGWN is presented under the consideration of the heterogeneous network architecture of 5G. Nonetheless, new methodologies are still required for the deployment of QoS management models using ML-based algorithms to identify optimal key performance indicators (KPIs) and key quality indicators (KQIs) to make efficient and optimized QoS frameworks.