Final Year Project
Group No. 02
Siddra Zafar (18-TE-34)
Ansar Bilal (18-TE-42)
Resource Management in D2D based 5G Cellular
Networks using Machine Learning
Resource Allocation
Definition:
In the literature, resource allocation refers to a process in which we manage and assign the
available resources of a company in such a way that the company’s goals are protected.
Resource Allocation in Telecommunication Engineering:
In the context of telecom engineering, resource allocation refers to the process of allocating
communication resources like Time, Frequency, and Space.
As the concentration of consumers increase day-by-day, there exists a challenge regarding the
available resources. Greater the number of users, greater the requirements of resources and
hence, greater the burden on the providers. This is why we need to come up with the ways of
managing the existing resources in such a way that the consumption does not exceed the
available resources. (Yi Su, 2013)
D2D Communication
Device-to-Device (D2D) communication in mobile networks is defined as direct conversation
between two mobile customers without traversing the Base Station (BS) or center community.
D2D is the best format of communication when compared to peer-to-peer (P2P)
Communication. It finds its best usage in 5G Network. It also enhances the system’s
performance.
Device to device Communication (D2D) is a fundamental part of IoT. In this paper, a device is
established between cell and device customers with a purpose to act as a gate among the two.
In order to have verbal exchange, gadgets want to undergo exclusive QoS obstacles and system
condition.
D2D communications require complicated Resource Management strategies, efficient tool
discovery mechanisms, clever mode selection algorithms, strong protection and security
protocols, and mobility management processes.
Network Slicing
Network Slicing Basics:
The fundamental idea of network slicing is to "slice up" the original network structure in a
couple of logical and independent networks that are configured to effectively meet the diverse
service requirements.
5G Network Slicing:
It is a network architecture that allows the multiplexing of virtualized and independent logical
networks at the same physical network infrastructure. Each slice of network is a separated endto-end Network tailored to fulfil diverse requirements asked with some selected applications.
Benefits and motivation of network slicing:
Task to improve
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Efficiency
Performance
Flexibility
Time Delay
Power
Reliability
Latency
Internet of Things (Iot)
Internet-of-Things (IoT) defines a massively heterogeneous network formed of smart gadgets
linked to the Internet. In the wake of disruptive IoT with a big amount and type of data,
Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal function to
deliver intelligence to the IoT networks. Besides other aspects, ML and DL can play an essential
function in addressing the demanding situations of resource management in large-scale IoT
networks.
Machine Learning:
Machine studying (ML) is a sort of artificial intelligence (AI) that permits software programs to
be more correct at predicting consequences without being explicitly programmed to do so. ML
utilizes algorithms use historical data as input to foresee new output values.
Machine Learning and 5G Technology:
By integrating ML into 5G, intelligent base stations might be able to make decisions their own,
and mobile gadgets can be capable of creating dynamically adaptable clusters based on learned
information. This will enhance the efficiency, latency, and reliability of network.
References
Yi Su, F. F. (2013). Resource Allocation in Communications and Computing. Journal of Electrical and
Computer Engineering, 2.