# Big Data Perspective and Challenges in Next Generation Networks
## 3 Data Analytics Perspective on Next Generation Networks
### Some Advanced Learning Methods
#### 3.3.7 Role of Neural Networks in Next Generation Networks  
Neural network is a branch of artificial intelligence that works in a similar way as human brain works. In human brain, there are millions of neurons that take decision after analyzing a particular action. In the same way, in neural networks, there are many nodes. These nodes are connected with input layer, output layer and hidden layer. The output of neural network is determined by organization and weights of these connected nodes. We can say that neural networks work as non-digital computers. The field of study of neural networks is under consideration from last decades. The success of neural networks is widely reported for computer vision, speech recognition, control science and robotics system, medical and health care system and data analytics systems. Recently, [PERSON] et al. [88] presented the importance of neural networks in wireless system. The applications of neural networks in wireless networks, as highlighted in [88], include: _modeling of non-linearity, localization issues, improving quality of service and quality of experience, efficient routing, load sharing, efficient security and privacy policy_, \begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **References** & **Solutions** \\ \hline Active learning & [67; 69] & Incomplete mobile data processing \\ Representation learning & [70; 71] & Multi dimensional feature data processing \\ Transfer learning & [72; 73] & Data processing of different source domains \\ Deep learning & [74; 78; 79] & Complex data processing, automatically learn hierarchical representations \\ Distributed and Parallel learning & [82; 83] & Allocating the learning process among several workstations \\ Online learning & [84; 85; 86; 87] & On-line mobility predictions \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Advanced Machine Learning Algorithms. observing-spectral sensing, signal detection and classification, pattern based communication, improved wireless sensor network structure, network optimization by event prediction, and fault detection._  
To achieve the required features of 5G such as close to zero latency and maximum throughput, the 5G networks must be intelligent, smarter and proactive. From the literature [88], it could be anticipated that neural network could perform a major role for fulfilling the 5G networks. [PERSON] et al. [88] discussed different types of artificial neural networks such as feedback neural networks, deep neural network, recurrent neural networks, fuzzy neural networks, wavelet neural networks and random neural networks. The authors in [88] also presented how these neural networks can be applied in wireless networks and how they can improve the efficiency of wireless networks. After the evolution of 3G networks, i.e., wideband code division multiple access (WCDMA) and universal mobile telecommunication service (UMTS), the modulation schemes need changes. These modulation schemes have non-linearity. Such non-linearity has created problem for designing power amplifier. Neural networks are adapted to overcome such non-linearity. [PERSON] et al. [89] suggested that non-linearity such as inter-symbol interference and multipath fading in core division multiple access (CDMA) technique can be overcome by using multilayer feed forward neural networks (ML-FFNN). The authors in [89] described that ML-FFNN can efficiently reconstruct the signal removing non-linearity. Similarly, for 4G LTE networks, MIMO-OFDM has been adapted. In MIMO-OFDM, channel estimation and channel equalization is needed for minimizing signal fading. Previously, least mean square error method was used for channel estimation, but the drawback of this method is that it increases mean square error. [PERSON] et al. [90] suggested that ML-FFNN can be used for channel estimation in MIMO-OFDM. The authors in [90] described that ML-FFNN improved channel estimation compared to least square method for MIMO-OFDM.