Paper Title
Machine Learning in Wireless Communication: Network Performance
Article Identifiers
Registration ID: IJNRD_226640
Published ID: IJNRD2110005
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Keywords
Machine Learning, Wireless Communication, Network Performance, Deep Learning, 5G Networks, Signal Processing, Spectrum Management, Resource Allocation, Predictive Analytics, Network Optimization, AI, Wireless Networks, Data Analytics, Network Security, Adaptive Systems
Abstract
Wireless communication has evolved dramatically over the past few decades, playing a crucial role in connecting people and devices across the globe. As wireless networks become more complex, driven by the proliferation of mobile devices, the Internet of Things (IoT), and the imminent deployment of 5G and beyond, the demand for efficient network management and optimization is greater than ever. Machine learning (ML), with its powerful data-driven approaches, offers promising solutions to enhance network performance, address challenges, and enable adaptive, intelligent wireless communication systems. Machine learning encompasses a range of techniques, including supervised learning, unsupervised learning, reinforcement learning, and deep learning, each offering unique capabilities for addressing various aspects of wireless communication. These techniques enable wireless networks to adaptively manage resources, predict network conditions, optimize signal processing, and enhance security, leading to improved performance metrics such as throughput, latency, energy efficiency, and reliability. One of the primary applications of machine learning in wireless communication is in dynamic spectrum management. As the radio spectrum becomes increasingly congested, efficient spectrum utilization is essential. ML algorithms can analyze historical data and real-time conditions to predict spectrum availability, enabling cognitive radios to dynamically access underutilized bands. This enhances spectral efficiency and reduces interference, thereby improving overall network throughput. In addition to spectrum management, machine learning plays a significant role in optimizing resource allocation within wireless networks. ML algorithms can predict user demand patterns, traffic loads, and mobility, allowing networks to dynamically allocate resources such as bandwidth, power, and time slots. This adaptability ensures optimal quality of service (QoS) for users, particularly in high-demand scenarios, while minimizing energy consumption and operational costs. Machine learning also contributes to the advancement of beamforming and multiple-input multiple-output (MIMO) technologies, which are crucial for enhancing network capacity and coverage. By learning from historical channel state information (CSI) and environmental factors, ML models can optimize beamforming patterns and antenna configurations in real-time, improving signal quality and user experience. This capability is particularly beneficial in massive MIMO systems, where the complexity of antenna arrays requires efficient management to maximize performance.
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How To Cite (APA)
VENKATA RAMANAIAH CHINTHA, PROF.(DR.) PUNIT GOEL, & PROF.(DR.) ARPIT JAIN (August-2024). Machine Learning in Wireless Communication: Network Performance. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), 27-47. https://ijnrd.org/papers/IJNRD2110005.pdf
Issue
Volume 9 Issue 8, August-2024
Pages : 27-47
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Paper Reg. ID: IJNRD_226640
Published Paper Id: IJNRD2110005
Research Area: Engineering
Author Type: Indian Author
Country: -, -, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2110005.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2110005
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