# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 1 Introduction  
Over recent years, the quantity of communicating devices and their consumers has been enormously growing, forcing cellular companies to expand their bandwidth to acquire higher data rates for wireless media [1]. In this regard, the advancement in mobile phone generations from 1G to 4G-LTE has brought about an improvement and numerous challenges [2]. Nowadays, internet speed is an eminent concern for both users and service providers; hence, cellular companies are in a battle for fifth-generation (5G) internet implementation so that they can provide a super fast and reliable connection to their users [3]. Fifth-generation networks are supposed to revamp the cellular industry from top to bottom by 2022, as their primitive focus is to enhance the transmission speed, capacity, and reliability of wireless channels. This task can be accomplished by using low power and low latency for the tactile internet, the Internet of Things (IoT), massive multiple-input and multiple-output (MIMO), robotics, autonomous vehicles, and industry [4]. Due to its super fast nature, it brings a latency rate of 1 ms, allowing problems to be solved instantaneously, which is essential for real-time applications and seamless connectivity [5]. 5G technology has enabled multiple networks to interact with each other irrespective of their distinct characteristics to establish a heterogeneous wireless network (HetNet). Although this is a standard of 4G-LTE, the primary infrastructure does not support it [6]. Fifth-generation devices consume low power, and Tx/Rx transmits signals at a data rate of 10 Gbps (gigabits-per-second). This technology provides an environment for the connection of millions of IoT devices. The combination of 5G and IoT can be used to execute the smart city concept. Smart devices are capable of exchanging data over millions of devices at a high transmission rate with low latency and low cost [7]. Increased system capacity, high spectral efficiency (SE), and energy efficiency are other advantages that can improve user experience and satisfaction. The primary goal of 5G is to connect living beings and intelligent devices (machines, robotics) at any instant to provide complete services [8]. The roadmap of 5G provides boundless possibilities from economic and societal perspectives. The lower site density in the sub-6 GHz region is crucial for high-definition video streaming and massive machine-type communications (mMTC) applications with ultra-reliable low latency communication (uRLLC). Fifth-generation technologies will surely help to tackle applications that consume high data rates and require massive bandwidth and low latency [9; 10]. In Figure 1, the heterogeneous network architecture of 5G new radio (NR) is presented, in which everything is connected with 5G internet. Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) techniques are envisioned to be the best techniques to make the 5G vision a reality. Considering novel use cases such as critical delay-sensitive applications, drone mobility, autonomous vehicles, mixed reality, and industrial automation [11]. From virtual personal assistants to social media services, ML is expected to solve many challenges in numerous applications. The ML-based solutions are effective for intrusion detection and prevention, video surveillance, email spam, and predictions, while reducing online commuting. ML has become mature enough to enhance the performance of the network and its services by learning from the wireless network traffic behaviors [12]. ML-based 5G wireless communication is possible using various modern networking standards; i.e., big data analysis [13], mobile edge caching [14], mobile edge computing [15], and context-aware networking [16]. The existing heuristic radio resource management (RRM) algorithms cannot confront the fundamental demands of 5G due to their complex procedure [17]. However, ML works incredibly well for traditional approaches to complex problems that require a great deal of human intervention.