Ruchi Sachan

Sungkyunkwan University

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Chang Wook Ahn

Gwangju Institute of Science and Technology

Navrati Saxena

San Jose State University

Muhammad Zahid

Khwaja Fareed University of Engineering and Information Technology

Abhishek Roy

R. G. Kar Medical College and Hospital

Zahid Muhammad

Pir Mehr Ali Shah Arid Agriculture University

Bharat J. R. Sahu

Siksha O Anusandhan University

Shatarupa Dash

Siksha O Anusandhan University

Hee Yong Youn

Konkuk University

Tae Jong Choi

Chonnam National University

Jaehoon Jeong

Sungkyunkwan University

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Sungkyunkwan University

Samsung (South Korea)

Kyungpook National University

Siksha O Anusandhan University

The University of Texas at Arlington

Khwaja Fareed University of Engineering and Information Technology

Huazhong University of Science and Technology

Centre National de la Recherche Scientifique

R. G. Kar Medical College and Hospital

Baker Heart and Diabetes Institute

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A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks

Discrete Dynamics in Nature and Society (2016)

Ruchi Sachan Tae Jong Choi Chang Wook Ahn

The exponential growth in data traffic due to the modernization of smart devices has resulted in the need for a high-capacity wireless network in the future. To successfully deploy 5G network, it must be capable of handling the growth in the data traffic. The increasing amount of traffic volume puts excessive stress on the important factors of the resource allocation methods such as scalability and throughput. In this paper, we define a network planning as an optimization problem with the decision variables such as transmission power and transmitter (BS) location in 5G networks. The decision variables lent themselves to interesting implementation using several heuristic approaches, such as differential evolution (DE) algorithm and Real-coded Genetic Algorithm (RGA). The key contribution of this paper is that we modified RGA-based method to find the optimal configuration of BSs not only by just offering an optimal coverage of underutilized BSs but also by optimizing the amounts of power consumption. A comparison is also carried out to evaluate the performance of the conventional approach of DE and standard RGA with our modified RGA approach. The experimental results showed that our modified RGA can find the optimal configuration of 5G/LTE network planning problems, which is better performed than DE and standard RGA.

Differential Evolution

10.1155/2016/5348203

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A Novel Random Scheduling Algorithm based on Subregions Coverage for SET K-Cover Problem in Wireless Sensor Networks

KSII Transactions on Internet and Information Systems (2018)

Zahid Muhammad Abhishek Roy Chang Wook Ahn Ruchi Sachan Navrati Saxena

This paper proposes a novel Random Scheduling Algorithm based on Subregion Coverage (RSASC), to solve the SET K-cover problem (an NP-complete problem).SET K-cover problem distributes the set of sensors into the maximum number of mutually exclusive subsets (MESSs) in such a way that each of them can be scheduled for lifetime extension of WSN.Sensor coverage divides the target region into different subregions.RSASC first sorts the subregions in the ascending order concerning their sensor coverage.Then, it forms the subregion groups according to their similar sensor coverage.Lastly, RSASC ensures the K-coverage of each subregion from every group by randomly scheduling the sensors.We consider the target-coverage and area-coverage applications of WSN to analyze the usefulness of our proposed RSASC algorithm.The distinct quality of RSASC is that it utilizes less number of deployed sensors (33% less) to form the optimum number of MESSs with the higher computational speed (saves more than 93% of the time) as compared to the existing three algorithms.

Set cover problem

10.3837/tiis.2018.06.012

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UEs Power Reduction Evolution with Adaptive Mechanism over LTE Wireless Networks

Journal of Electronic Science and Technology (2018)

Ruchi Sachan Chang Wook Ahn

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Mobile phone

Idle

Dynamic demand

10.11989/jest.1674-862x.61012151

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Energy Efficient Base Station Location Optimization for Green B5G Networks

Lecture notes in electrical engineering (2022)

Ruchi Sachan Shatarupa Dash Bharat J. R. Sahu

10.1007/978-981-16-9488-2_41

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MABC: Power-Based Location Planning with a Modified ABC Algorithm for 5G Networks

Discrete Dynamics in Nature and Society (2017)

Ruchi Sachan Muhammad Zahid Jaehoon Jeong Chang Wook Ahn Hee Yong Youn

The modernization of smart devices has emerged in exponential growth in data traffic for a high-capacity wireless network. 5G networks must be capable of handling the excessive stress associated with resource allocation methods for its successful deployment. We also need to take care of the problem of causing energy consumption during the dense deployment process. The dense deployment results in severe power consumption because of fulfilling the demands of the increasing traffic load accommodated by base stations. This paper proposes an improved Artificial Bee Colony (ABC) algorithm which uses the set of variables such as the transmission power and location of each base station (BS) to improve the accuracy of localization of a user equipment (UE) for the efficient energy consumption at BSes. To estimate the optimal configuration of BSes and reduce the power requirement of connected UEs, we enhanced the ABC algorithm, which is named a Modified ABC (MABC) algorithm, and compared it with the latest work on Real-Coded Genetic Algorithm (RCGA) and Differential Evolution (DE) algorithm. The proposed algorithm not only determines the optimal coverage of underutilized BSes but also optimizes the power utilization considering the green networks. The performance comparisons of the modified algorithms were conducted to show that the proposed approach has better effectiveness than the legacy algorithms, ABC, RCGA, and DE.

Differential Evolution

10.1155/2017/4353612

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Clustering based power management for green LTE networks

International Conference on Computer Communication and Informatics (2014)

Ruchi Sachan Navrati Saxena

The power consumption of wireless access networks is becoming an important issue in the green cellular-technology. The concept of green 4G LTE system depends on enhanced design strategy of efficient LTE energy system in order to reduce greenhouse emission as well as operators energy bill. In this paper we have studied eNB sleep modes to identify the traffic awareness in LTE networks and subsequently design a cooperative communication framework for energy consumption reduction. We calculated the percentage of energy which can be saved with eNB sleep modes, proving that it can be close to 30% of the total network energy consumption.

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LTE Advanced

10.1109/iccci.2014.6921819

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Citations (3)