This document provides an overview of next generation networks and big data challenges. It discusses how 5G networks will generate huge amounts of data from billions of wireless devices. Modern data analytics techniques like big data analytics will be needed to efficiently handle and extract insights from this large and diverse data. The document also outlines some of the key requirements for 5G networks, such as high data rates and low latency, and the underlying technologies being developed to achieve this, including millimeter wave spectrum and massive MIMO. It discusses open issues regarding security, privacy, and analyzing heterogeneous data sources.

1. BIG DATA AND
NEXT GENERATION
NETWORK
CHALLENGES
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Phdassistance
Group www.phdassistance.com
Email: info@phdassistance.com

2. Today's Outline
Introduction
Major Milestones of Next Generation Networks
Next-Generation Networks: Current Standards and Technology
Enablers
Data Analytics Perspective on Next Generation Networks
Current State-of-the-Art and Open Issues
Conclusion

3. With the advancement of next-generation
cellular networks, such as 5G, the attention
has switched to addressing increased data
rate needs, micro cell potential, and millimetre
wave spectrum.
High data speeds, minimal latency, and the
handling of large amounts of data are the
goals of next-generation networks.

4. These objectives will almost certainly
necessitate newer architecture designs,
upgraded technology with probable backward
compatibility, improved security algorithms, and
the ability to make intelligent decisions.
In this study, we identify the potential that 5G
networks can give, as well as the underlying
problems that must be overcome in order for 5G
to be implemented and realised.

5. Introduction
Big Data is defined as data whose dynamics, such
as volume, velocity, truthfulness, and diversity, are
substantially expanded and impossible to be handled
by typical data management systems.
Modern data analytics techniques are utilised to
manage such large amounts of data.
With the introduction of next-generation networks,
the number of wireless devices is fast expanding.

6. According to a CISCO index released in 2014,
the number of wireless devices now outnumbers
the world's population. The
Proliferation of data generated by such a varied
spectrum of linked devices is unsurprising.
Modern data analytics techniques(big data
analytics) will be used to efficiently handle and
extract meaningful insights from such a large
supply of data.

7. Discussions on next-generation networks (5G) have
gotten a lot of interest in the research community in
the previous few years.
Given that 4G is already a globally accepted
technology, possibilities and difficulties for 5G and
its underlying technologies are being investigated.
Over the previous few years, several advances
have been explored in the literature. Ultra-dense
networks, huge MIMO (Multiple-Input Multiple-
Output), and millimeter-waves (mmWaves) are the
most important of these

8. The number of wireless devices expected
to reach in the hundreds of billions with
the introduction of 5G.
As a result of the bandwidth-hungry
applications running on these devices, the
required data rates are also increasing.
Figure 1 shows a comparison of the
exponential development in data rate.

9. Figure: Evolution of mobile generation networks.

10. Major Milestones of
Next Generation
Networks
While there are various standards for 5G, not all of them
must be met at the same time and may vary depending
on the underlying conditions.
In video transmission, for example, a large data rate is
essential, but latency and dependability can be
disregarded.

11. Latency and dependability are required in
autonomous vehicles; however data rate
can be reduced marginally.
Figure 2 depicts a graphic representation
of the required 5G functionalities.
The next generation networks will have
near-zero latency and maximum
throughput.
These characteristics necessitate quick
and dependable inter-cell or intra-cell
hand-off.

12. Hand-off is the technique of exchanging mobile network dynamics such as frequency,
time slots, spreading code, or a combination of them without compromising service
quality.
As a result, for next-generation networks, hand-off management is crucial.
Accessing numerous radio access technologies makes handoff management more
challenging because of the basic requirements of next-generation networks such as
extreme densification and high mobility.
Figure 3 depicts a handoff. PhD Assistance experts has experience in handling
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13. Figure: Hand-off in next-generation networks (inspired from [8]).

14. Next-Generation Networks: Current
Standards and Technology Enablers
The developments in future generation networks (5G)
are driven by evolutionary technologies such as mm-
Wave spectrum, ultra-dense networking, massive
MIMO, and unique application requirements [3].
Table 2 summarises the industry standardizations
and aspirations that are now in the works.

15. Table 2.Standards and technology enablers for SG

16. Data Analytics
Perspective on Next
Generation Networks
Big Data and Machine Learning can rightfully
be referred to as the two pillars of 5G, given the
vast volume of data generated and the
sophisticated decision-making involved.

17. Congestion of mobile networks is unavoidable, given
the rapid improvement of cellular technology and
the rise in the number of mobile devices.
As a result, big data faces processing issues because
it differs greatly from ordinary data.
As previously stated, the number of devices in next-
generation networks will expand by a factor of 1,000
BIG DATA IN NEXT GENERATION NETWORKS

18. It is clear that the amount of data transferred in 5G networks will be huge and diverse.
As a result, strategies for handling such large amounts of data would need to be
investigated in order to optimise 5G networks.
The massive influx of data will make it difficult to meet the key needs and features of 5G
networks.
As a result, deploying 5G networks without dealing with huge data concerns is extremely
difficult. For dealing with this type of data, big data analytics approaches can be applied.

19. Artificial intelligence includes the field of machine learning. Machine learning is a
technique that allows computers to make judgments based on data input.
Data is the source of input for machine learning algorithms; this data can be
diverse and come from a variety of places.
As a result, machine learning can assist in predicting future events based on
historical data. [5] Figure depicts the many forms of machine learning and
accompanying algorithms.
OPTIMIZATION OF NEXT, GENERATION CELLULAR NETWORKS

20. Figure: Comparison of different types of machine learning.

21. Mobile networks are already complicated, and 5G networks are likely to be even more so
than their predecessors.
5G networks will be smarter and more intelligent in terms of network management,
resource allocation, load balancing, cost efficiency, power efficiency, and so on, in
addition to being more sophisticated.
Machine learning techniques can be used to implement these characteristics for 5G.
As a result, we may conclude that machine learning will play a significant role in the
deployment of 5G networks.

22. A neural network is a type of Artificial Intelligence that functions similarly to the
human brain.
Millions of neurons in the human brain make decisions after examining a
specific activity.
Similarly, there are many nodes in neural networks. The input layer, output
layer, and hidden layer are all connected to these nodes.
ROLE OF NEURAL NETWORKS - NEXT GENERATION NETWORKS

23. The organisation and weights of these connected nodes define the output of a
neural network.
Neural networks can be thought of as non-digital computers.
The field of neural networks research has been under consideration for
decades.
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25. Current State of the
Art and Open Issues
We will briefly cover some open topics in this
section.
This also provides guidance for future research at
the intersection of big data and 5G, as tackling
these difficulties can lead to substantial advances.

26. Modern big data analytics approaches can help reduce the cost of computing and
caching for next-generation networks.
Resources will be efficiently dispersed and exploited in this manner, resulting in a
balance of caching and computing overhead.
For example, interim and final results should only be saved if they are useful, as
storing all of the data is expensive.
PROACTIVE CACHING AND COMPUTING:

27. Big Data Analytics reveals hidden knowledge in massive amounts of data. As a result,
large-scale data analysis can raise security and privacy concerns.
Data should be well encrypted during the storage, administration, and processing
stages to ensure that it cannot be tampered or altered.
Furthermore, authorised entities should only be able to access the data through secure
channels.
As a result, security and privacy problems are important considerations for such a large-
scale data analysis and should be addressed thoughtfully.
SECURITY AND PRIVACY:

28. Different types of big data sources exist, each with its own data rate, mobility, and
packet loss. In wireless networks, analysing diverse data is difficult.
Spatial and temporal dynamics are brought by heterogeneous data.
As a result, for large spatiotemporal data analysis in mobile networks, unusual
methodologies are necessary.
BIG HETEROGENEOUS DATA:

29. In this Blog, we will provide an overview of the upcoming 5G communication
networks.
We also go over the many requirements, problems, and design issues that must
be addressed in order for 5G networks to be realised.
Ultra-dense networking, millimetre wave spectrum, and massive MIMO are among
the important technologies highlighted.
We outline the obstacles that must be solved, as well as viable architecture
designs for 5G deployment.

30. We also discuss the energy issue in 5G networks, and discover that it is
constantly at the top of the list of 5G network issues.
Service models for 5G are being considered during device development, and their
backward compatibility will be critical for both users and service providers.
We also provide a big data perspective on 5G, as well as the opportunity that
machine learning techniques provide for learning, inference, and decision making
on 5G data.

31. There are many more domains in 5G networks that have not been explored in
depth in this text but are crucial.
The security and privacy of prospective designs, hardware, and data transfer
protocols in 5G networks are key concerns that necessitate additional
investigation.

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