# Big Data Perspective and Challenges in Next Generation Networks
## 5 Conclusions  
In this survey, we present an introduction of the 5G communication networks of the future. We also present the various requirements and challenges and possible design issues which need to be addressed for the realization of 5G networks. The key technologies discussed include the ultra-dense networking, millimeter wave spectrum and massive MIMO. We identify the challenges which need to be overcome and the possible potential architecture design towards 5G implementation. We also outline the energy concern in 5G networks and find that it is always at the foremost of the challenges for 5G networks. During device design, service models are under consideration for 5G, and their backward compatibility will be of huge importance for both the users as well as for service providers. We also present a big data perspective on 5G and the opportunities that machine learning techniques have to offer for learning, inference and decision making on 5G data. There are even more domains in 5G networks which have not been covered in this text in detail, however, possess key importance. Security and privacy of the candidate architectures, hardware, and data transfer protocols in 5G networks pose major challenges and require further research. We hope that this survey will give readers a useful insight into the future generation networks and help the beginners to develop an understanding and realize the opportunities and challenges in 5G networks.  
Conceptualization, K.S. and H.A.; Methodology, K.S. and H.A.; Resources, H.A. and Z.Z.; Writing-Original Draft Preparation, K.S. and H.A.; Writing-Review & Editing, K.S., H.A. and Z.Z.; Supervision, H.A. and Z.Z.; Funding Acquisition, Z.Z.  
This work was supported by the key project of the National Natural Science Foundation of China (No. 61431001), Beijing Natural Science Foundation (L172026), Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (Guilin University of Electronic Technology), and the Foundation of Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
AL Active Learning  
BS Base Station  
CP Cyclic Prefix  
CDMA Code Division Multiple Access  
CoMP Coordinated Multipoint  
C-RAN Cloud-Radio Access Network  
CRN Cognitive Radio Network  
DRL Deep Reinforcement Learning  
EHF Extreme High Frequency  
FSPL Free Space Path Loss  
GFDM Generalized Frequency Division Multiplexing  
FFT Fast Fourier Transform  
HSPA high speed packet access  
IoT Internet of Things  
mbps Mega bit per second  
MBS Macro-Cell Base Station  
MIMO Multiple Input Multiple Output  
mmWave millimeter wave  
NFV Network Function Virtualization  
OFDM Orthogonal Frequency Division Multiplexing  
RAT Radio Access Technology  
RRH Remote Radio Head  
SBS Small-Cell Base Stations  
SDN Software Defined Networking  
SINR Signal-to-Interference-plus-Noise Ratio  
TDMA Time Division Multiple Access  
UE User Equipment  
UHF Ultra High Frequency