Paper Title
Machine Learning in Wireless Communication: Network Performance
Article Identifiers
: 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.
Downloads
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
Other Publication Details
Paper Reg. ID: IJNRD_226640
Published Paper Id: IJNRD2110005
Downloads: 000122253
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
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
UGC CARE JOURNAL PUBLICATION | ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG
Copyright & License
© 2025 — Authors hold the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License. and The Open Definition.
You are free to share, adapt, and redistribute the material, provided proper credit is given to the original author. 🛡️ Disclaimer: The content, data, and findings in this article are based on the authors’ research and have been peer-reviewed for academic purposes only. Readers are advised to verify all information before practical or commercial use. The journal and its editorial board are not liable for any errors, losses, or consequences arising from its use.
Publication Timeline
Article Preview: View Full Paper
Call For Paper
IJNRD is a Scholarly Open Access, Peer-Reviewed, Refereed, and UGC CARE Journal Publication with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost, and Transparent Peer Review Journal Publication that adheres to the UGC CARE 2025 Peer-Reviewed Journal Policy and aligns with Scopus Journal Publication standards to ensure the highest level of research quality and credibility.
IJNRD offers comprehensive Journal Publication Services including indexing in all major databases and metadata repositories, Digital Object Identifier (Crossref DOI) assignment for each published article with additional fees, citation generation tools, and full Open Access visibility to enhance global research reach and citation impact.
The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse academic and professional fields. The journal promotes global knowledge exchange among researchers, developers, academicians, engineers, and practitioners, serving as a trusted platform for innovative, peer-reviewed journal publication and scientific collaboration.
Indexing Coverage: Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many other recognized academic repositories.
How to submit the paper?
By Our website
Click Here to Submit Paper Online
You can now publish your research in IJNRD. IJNRD is a Transparent Peer-Reviewed Open Access Journal Publication (Refereed Journal), aligning with New UGC and UGC CARE recommendations.
For more details, refer to the official notice: UGC Public Notice
Submit Paper Online
Important Dates for Current issue
Paper Submission Open For: December 2025
Current Issue: Volume 10 | Issue 12 | December 2025
Impact Factor: 8.76
Last Date for Paper Submission: Till 31-Dec-2025
Notification of Review Result: Within 1-2 Days after Submitting paper.
Publication of Paper: Within 01-02 Days after Submititng documents.
Frequency: Monthly (12 issue Annually).
Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.
Subject Category: Research Area
Call for Paper: More Details
Approval, Licenses and Indexing: More Details
