# Big Data Perspective and Challenges in Next Generation Networks
## 1 Introduction  
The data whose dynamics such as volume, velocity, veracity, variety are extended massively and unable to be handled by traditional data management system, is termed as big data. For management of such a massive data, modern data analytics techniques are used. With the advent of next generation networks, wireless devices are increasing rapidly in number. According to the indexed issued by CISCO, the number of wireless devices are now more in number than the world population in 2014 [1]. From such a diverse range of connected device, explosion in data is not unexpected. For efficient handling and extracting valuable insights from such massive source of data, modern data analytics algorithms (big data analytics) will be applied.  
During the last few years, discussions on next generation networks (5G) have gained widespread attention within the research community. Given that 4G is now a well-established technology across the globe, opportunities/challenges for 5G and the underlying technologies are being explored. Several breakthroughs have been discussed in the literature during the last few years. The most significant of these are the ultra-dense networks, massive MIMO (Multiple-Input Multiple-Output) and millimeter-waves (mmWaves) [2]. Beside these, some other technologies such as device to device communication, two-layer architecture, cognitive radio based architecture, and cloud based architecture [3] also have great importance in next generation networks. For these technologies of next generation networks, big data analytics and machine learning paradigm will perform a key enabling role. Due to rapidly growing wave of big data in wireless networks, big data analytics are becoming the major part of wireless communication as well as efficiently managing the network [4]. There have been some efforts to show the importance of big data analytics in next generation networks such asbig data based model for optimizing next generation networks [5], and the importance of machine learning in enabling next generation networks [6; 7]  
With the advent of 5G, the number of wireless devices is projected to be in hundreds of billions [2]. Consequently, the required data rates are also increasing because the applications running on these devices are bandwidth-hungry. A comparison of the exponential growth in data rate is shown in Figure 1.  
Challenges for 5G include high frequency spectrum (millimeter wave), much higher data rates (in Gigabytes) and ways of allocation and re-allocation of the bandwidth. The facts that 5G will open a new era in communication and that a major backbone for 5G is the Internet-of-Things (IoT) make 5G of great interest for the research community (IoT will connect billions of devices and will provide a high rate ubiquitous and low latency network). In summary, three major technologies which will contribute significantly to 5G are: _Ultra densification, Millimeter wave (mmWave)_ and _Massive MIMO_. Waveform transmission, underlying network infrastructure and energy efficiency of communication are also significant issues for 5G.  
5G networks will presumably be intelligent and proactive. Thus, artificial intelligence technologies such as machine learning, advanced machine learning techniques and big data analytics could perform a significant part in 5G networks. In this work, the significance of big data, big data analytics and machine learning's paradigm are discussed for making next generation networks from theory to reality. To the best of our knowledge, this is the first work that presents all these issues. These issues are discussed in the subsequent sections. This work is organized as follows. In Section 2, we present the key features required for a 5G network and major technologies that will be adapted in 5G. We then provide a useful insight on 5G from the perspective of big data analysis in Section 3. We briefly highlight some open issues in Section 4. Finally, we conclude the paper in Section 5.