The fifth-generation (5G) network has revolutionized wireless
communication with its promise of ultra-high-speed data transfer,
reduced latency, and massive connectivity. However, one of the critical
challenges remains efficient resource allocation, especially in Peer-to-
Peer (P2P) communications during data transmission. In traditional
centralized systems, resource allocation is handled by a base station,
but P2P communication in 5G networks can help minimize network
congestion and improve resource utilization by directly connecting
users. This paper investigates a novel approach to resource allocation
in P2P communications, optimizing data transmission through
dynamic resource assignment strategies. The method utilizes machine
learning algorithms to predict traffic patterns and demand, adjusting
resource distribution in real-time to ensure fair and efficient bandwidth
allocation. The proposed system prioritizes users based on the Quality
of Service (QoS) requirements and the network's current load. The
simulation results show that this approach improves throughput by
30%, reduces latency by 25%, and increases overall system efficiency
by 18%. In comparison to conventional methods, the proposed model
achieves better load balancing and minimizes data packet loss. The
system's effectiveness was validated through extensive simulations in a
5G testbed, highlighting its scalability and adaptability in high-density
user environments. The results demonstrate that P2P communications,
when combined with smart resource allocation, can play a pivotal role
in realizing the full potential of 5G networks for data transmission.
Pattem Sampath Kumar1, Poomagal Adhinarayanan2
Malla Reddy College of Engineering, India1, Sri Ramachandra Institute of Higher Education and Research, India2
5G Networks, Peer-to-Peer Communication, Resource Allocation, Data Transmission, Machine Learning
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 15 , Issue: 4 , Pages: 3400 - 3404 )
Date of Publication :
December 2024
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66
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