# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 2 5G Technology
### 5G Network Architecture  
The architectural model of 5G has four layers: a network layer, controller layer, management and orchestration layer, and service layer. Radio link control (RLC) and packet data convergence protocol (PDCP) layers are the sub-layers of the 5G protocol stack. The network architecture of 5G contains adaptable, virtual, and flexible radio access networks (RAN) points instead of base stations (BS) and a complex distributed design. These virtual RANs include new interfaces, elements, and compositions to create numerous data access points [25]. In 5G architecture design, the RANs can support WiFi, WI-Max, CDMA-One, UMTS, LTE, LTE-adv, EV-DO, GSM, GPRS, IS-95, and different RANs connected through a single aggregate network using a software-defined network (SDN) controller to route the traffic to a gateway. The hyper-critical aspects of the IP-network, such as the cost of the entire architecture, are significantly cut down by reducing the network elements. The nano-core network (CN) is yet another essential component that has three further categories: (1) nanotechnology (nano-tech), which is essential for the protection of daily life devices; (2) cloud computing, which determines the availability of remote services over the internet; (3) the all IP network (AIPN), which is essential for cellular growth [26]. Third-generation partnership project (3 GPP) enhancements also allow a more distributed architecture, which facilitates the usage of AI to assist network optimization through centralized data gathering and by processing the required intelligence. The current NR architecture has already introduced new functions in the core and the management domains--i.e., network data analytics function (NWDAF) and the management data analytics function (MDAF)--which can either run analytics on collected data or can enhance already supported network functions with statistics collection and prediction capabilities. This capability of 5G architecture either analyzes collected information or supports the existing procedure for analytics. ML will play a role in increasingly complex 5G networks; i.e., making adequately energy-efficient and economic policies for optimized network operations. Given the increased complexity of 5G networks, further leveraging 5G network data and ML will be necessary to derive optimum network-wide energy-efficient operation policies [27]. The core features of nanotechnology are a flexible structure, transparency, and environment sensor. Notably, molecular nanotechnology (MNT) uses the nanoscale range (0.1 to 100 nm) to control daily-life gadgets. This is essential for sensing, ensuring security, and cleaning the mobile phone from the unprotected environment. While the cloud computing layer is concerned with all internet services dealing with remote and real-time applications, the consumers can access their confidential data through private accounts. Flat AIPN is the advanced concept enabled by 3 GPP to meet future demands of cellular technology. This eases the access of real-time applications by ensuring enhanced edge computing technology and provides features including a lower latency rate, seamless connectivity, system efficiency, and cost-effectiveness. The 5G NR improvement allows the aptitude of ML to endorse network optimization for an expanded distributed or centralizedarchitecture [28]. In Table 2, we summarize the key attributes of 5G that allow the expected 5G performance to be achieved. AI, ML, and DL have been attracting increased attention in the past few years, and researchers are continuously developing applications using these techniques. These technologies deployed together improve QoE, reduce operational costs, and bring new paradigms for future needs. However, 4G did not take advantage of the potential of these approaches; therefore, 5G must involve AL, DL, and ML-based learning characteristics in its architecture.