Forthcoming and Online First Articles

International Journal of Networking and Virtual Organisations

International Journal of Networking and Virtual Organisations (IJNVO)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Networking and Virtual Organisations (One paper in press)

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  • Efficient Call Admission Control in LTE Networks with Cascade Feed Forward Network   Order a copy of this article
    by Vaishali Jadhav, Pallavi Vasant Sapkale, Moresh M. Mukhedkar 
    Abstract: This research proposes a Cascade Feed Forward network (Cascade FFN) for the optimal Call Admission Control (CAC) over networks. Initially, the long-term evolution (LTE) network is simulated and the incoming call is recognized as a new call or else handoff call. Likewise, the handoff call is identified by various channel parameters. Finally, the CAC is performed using the Cascade FFN, which is obtained by incorporating the Deep Feed forward Network (DFN) and Cascaded Deep Neuro-Fuzzy Network (DNFN). After the admission of the new user, number of active users in the individual cell is updated. The simulation is performed based on various parameters and the proposed model has gained throughput of 444058bps, cell power of 54.267dBm, delay of 0.081sec, user call drop of 940, call blocking probabilities of handoff and new user as 0.693, and 0.677; and the call dropping probabilities of handoff and new user is 0.733, and 0.740.
    Keywords: Long Term Evolution (LTE) networks; Call admission control (CAC); Quality-of-Service (QoS); Deep learning (DL); Feed Forward network (FFN).
    DOI: 10.1504/IJNVO.2025.10070016
     

 

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