Optimizing qos in self organizing heterogeneous wireless cellular network using firefly algorithm
Publication details: Chennai ICT Academy 2022Edition: Vol.13(1) - MarchDescription: 2627-2634pSubject(s): Online resources: In: ICTACT journal on communication technologySummary: 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| Item type | Current library | Status | Barcode | |
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School of Engineering & Technology Archieval Section | Not for loan | 2022-1049 |
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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