Srinivasa Rao Bendi

Work place: Department of Computer Science and Engineering, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh-530045, India

E-mail: sbendi@gitam.edu

Website:

Research Interests: Artificial intelligent in learning

Biography

Srinivasa Rao Bendi is an Associate Professor in the Department of Computer Science and Engineering at GITAM (Deemed to be) University, Visakhapatnam, India. He received a doctoral degree in 2018, specializing in Cloud Computing. He has amassed 18 years of teaching experience. He is interested in researching Cloud Security, Artificial Intelligence, and Machine Learning.

Author Articles
Traffic Adaptive Small Cell Planning in Heterogeneous Networks

By Kuna Venkateswararao Tejas M. Modi Pravati Swain Srinivasa Rao Bendi

DOI: https://doi.org/10.5815/ijcnis.2025.02.06, Pub. Date: 8 Apr. 2025

Small cell is a key enabler for massive connectivity and higher data rate in the future generation of a cellular communication system. Few challenges in heterogeneous networks (HetNets) are effective resource utilization and de- ployment of optimal small base stations (SBSs) under dynamic mobile traffic patterns. In this paper, we design a traffic adaptive small cell planning (TASCP) schema to minimize the deployment of SBSs, enhancing the network energy efficiency without compromising the user equipment’s QoS (UEs). The proposed TASCP consists of two phases: small cell formation (SCF) and small Cell optimization (SCO). SCF creates the initial association between the UEs and SBS. The SCF operates the modes (active/sleep) of SBSs according to the dynamic traffic load. Changing the mode of SBS from an active mode to a sleep mode is based on the traffic load shared by other neighboring SBSs, cooperatively. The proposed TASCP method is compared with state-of-the-art algorithms, i.e., the Self-organized SBS Deployment Strategy (SSDS) and UE Association and SBS On/Off (USOF) algorithm. The network performance is calculated in terms of network energy efficiency, throughput, convergence time, and active small base stations. The performance of the proposed TASCP significantly increases as compared to state-of-the-art algorithms.

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