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Optimizing Qos In Self Organizing Heterogeneous Wireless Cellular Network Using Firefly Algorithm


Affiliations
1 Department of Electronics and Telecommunication Engineering, Kavayitri Bahinabai Chaudhari North Maharashtra University, India
     

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Capacity and energy efficiency are crucial for next-generation wireless networks. Due to the dense deployment of base stations (BSs) in a heterogeneous network (HetNets), the consumption is from 60% to 80% of the total energy causing accentuated costs. Therefore, industry and researchers work to reduce the energy consumption of HetNets. The power optimization problem in the network is taken care of by the proposed reward function in a distributed network. To increase energy efficiency, guaranteeing the QoS requirements, this paper proposes the use of a firefly optimization algorithm with BS shutdown. The simulation results demonstrate that the proposed algorithms have better energy efficiency performance than the maximum power-based user association mechanism.

Keywords

AWNs, Firefly Algorithm, Markov Decision Process, Q-learning, Greedy
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  • Optimizing Qos In Self Organizing Heterogeneous Wireless Cellular Network Using Firefly Algorithm

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Authors

Gajanan Uttam Patil
Department of Electronics and Telecommunication Engineering, Kavayitri Bahinabai Chaudhari North Maharashtra University, India
Girish Ashok Kulkarni
Department of Electronics and Telecommunication Engineering, Kavayitri Bahinabai Chaudhari North Maharashtra University, India

Abstract


Capacity and energy efficiency are crucial for next-generation wireless networks. Due to the dense deployment of base stations (BSs) in a heterogeneous network (HetNets), the consumption is from 60% to 80% of the total energy causing accentuated costs. Therefore, industry and researchers work to reduce the energy consumption of HetNets. The power optimization problem in the network is taken care of by the proposed reward function in a distributed network. To increase energy efficiency, guaranteeing the QoS requirements, this paper proposes the use of a firefly optimization algorithm with BS shutdown. The simulation results demonstrate that the proposed algorithms have better energy efficiency performance than the maximum power-based user association mechanism.

Keywords


AWNs, Firefly Algorithm, Markov Decision Process, Q-learning, Greedy

References