# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 2 5G Technology
### 5G Network Architecture
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**5G Network Technologies** & **Key Aspect** & **References** \\ \hline \multirow{6}{*}{Centralized architecture C-RAN (Cloud-RAN)} & 1. RAN as a Service (RAAS) capabilities. 2. Boosts the network performance and highly favorable in low latency. 3. Offers to reuse infrastructure, pool resources, support multiple technologies, decrease energy usage, create a heterogeneous and self-organizing structure, and reduce the costs associated with CAPEX and OPEX. 4. Also allows other network operations in a data center environment. & [29; 30; 31; 32; 33; 34; 35; 36], CAPEX and OPEX. 4. Also allows other network operations in a data center environment. & [37; 38; 39; 40; 41; 42; 43; 44; 45] \\ \hline \multirow{6}{*}{Network function virtualization (NFV)} & 1. Support for ultra-low latency, interoperability, virtualization, high bandwidth, augmented reality, strengthens security and compliance. & [37; 38; 39; 40; 41; 42; 43; 44; 45] \\  & 2. Optimized local content distribution and data caching. & [37; 38; 39; 40; 41; 42; 43; 44; 45] \\ \hline \multirow{6}{*}{Network function virtualization (NFV)} & 1. Supports longer life cycles for network hardware. 2. Reduced space needed for network hardware, power consumption, maintenance, and hardware costs. & [46; 47; 48; 49; 50; 51; 52; 53; 54] \\  & 3. Ease in network upgrades. & [37; 38; 39; 40; 41; 42; 43; 44; 45] \\ \hline \multirow{6}{*}{Network Slicing (NS)} & 1. Improve performance. 2. Protecting sensitive data. 3. CAPEX and OPEX can also be reduced. 4. Offers various services based on the requirements. 55; 56; 57; 58; 59; 60; 61] \\  & 5. Effective and efficient utilization of resources 6. Improves operational efficiency. 7. Overcomes all the drawbacks of DiffServ. & [39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 54; 53; 55; 56; 57; 58; 59; 60; 61] \\ \hline \multirow{6}{*}{Beamforming (BF)} & 1. Improves the spectral efficiency. 2. Boosts cell range and capacity. 3. mmWave offers a large bandwidth. & [62; 63; 64; 65; 66; 67; 68; 69] \\  & 4. Support for higher path loss and blockage scenarios. & [62; 63; 64; 65; 66; 67; 68; 69] \\ \hline \multirow{6}{*}{Enhanced Common Public Radio Interface (eCPRI)} & 1. Supports carrier aggregation, downlink CoMP, MIMO, uplink L1 Comp. 2. Reduces jitter and latency for high priority traffic. 3. Ease in troubleshooting at the lower layers. 4. Reduced bandwidth is required. & [70; 71; 72; 73; 74; 75; 76; 77] \\  & 5. Software upgradable interfaces. 6. Ethernet can carry eCPRI traffic. 7. Saves electricity. & [78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 99; 11; 11; 11; 11; 11; 11; 11; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126;  
Traits such as the extreme complexity, enhanced problem-solving solutions, and the heterogeneous nature of 5G can only be achieved by using these approaches. In other words, the existence of these characteristics transforms the 6G infrastructure into an innovative and intelligent network [86; 87; 88]. Both AI and ML are somehow correlated with each other, where AI is the program of study that includes intelligent machines, such as robotics, along with the perception of human intelligence, and ML involves the study of creating computer programs [89]. It is stated that AL is generally about finding solutions, facts, logical reasons, and intellectual learning, but ML is about learning from existing experiments and examples. On the other side, DL, a part of the ML family, is based on ANNs and enables computing models to perceive, learn, and represent data by following human brain stimulation. The model has multiple processing layers and contains interconnected artificial neurons such as the human body's nervous system [90]. As 4G did not provide the required platform for all these learning methods and technologies, 5G must consider the importance of these categories and prepare its internal infrastructure for experiencing smart and intelligent designs.