ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

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
     

   Subscribe/Renew Journal


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
Subscription Login to verify subscription
User
Notifications
Font Size

  • R. Amiri, H. Mehrpouyan, L. Fridman and D. Matolak, “A Machine Learning Approach for Power Allocation in HetNets Considering QoS”, Proceedings of IEEE International Conference on Communications, pp. 1-7, 2018.

  • O.G. Aliu, A. Imran, M.A. Imran and B. Evans, “A Survey of Self Organisation in Future Cellular Networks”, IEEE Communications Surveys and Tutorials, Vol. 15, No. 1, pp. 336-361, 2012.

  • J. Moysen and L. Giupponi, “From 4G to 5G: SelfOrganized Network Management Meets Machine Learning”, Computer Communications, Vol. 129, pp. 248268, 2018.

  • J.G. Andrews, S. Buzzi, W. Choi and J.C. Zhang, “What will 5G Be?”, IEEE Journal on Selected Areas in Communications, Vol. 32, No. 6, pp. 1065-1082, 2014.

  • M. Peng, D. Liang and H.H. Chen, “Self-Configuration and Self-Optimization in LTE-Advanced Heterogeneous Networks”, IEEE Communications Magazine, Vol. 51, No. 5, pp. 36-45, 2013.

  • M. Agiwal, A. Roy and N. Saxena, “Next Generation 5G Wireless Networks: A Comprehensive Survey”, IEEE Communications Surveys and Tutorials, Vol. 18, No. 3, pp. 1617-1655, 2016.

  • C. Venkatesan, P. Karthigaikumar and R. Varatharajan, “A Novel LMS Algorithm for ECG Signal Preprocessing and KNN Classifier based Abnormality Detection”, Multimedia Tools and Applications, Vol. 77, No. 8, pp. 10365-10374, 2018.

  • C. Mohanapriya and M. Ramkumar, “A Trusted Data Governance Model for Big Data Analytics”, International Journal for Innovative Research in Science and Technology, Vol. 1, No. 7, pp. 1-13. 2014.

  • R. Li, Z. Zhao, X. Zhou and H. Zhang, “5G: When Cellular Networks meet Artificial Intelligence”, IEEE Wireless Communications, Vol. 24, No. 5, pp. 175-183, 2017.

  • V. Chandrasekhar, J.G. Andrews and A. Gatherer, “Power Control in Two-Tier Femtocell Networks”, IEEE Transactions on Wireless Communications, Vol. 8, No. 8, pp. 4316-4328, 2009.

  • M. Ramkumar, M. Manikandan, K.S. Kumar and R.K. Kumar, “Intrusion Detection in Manets using Support Vector Machine with Ant Colony Optimization”, ICTACT Journal on Data Science and Machine Learning, Vol. 1, No. 1, pp. 37-42, 2019.

  • H. Claussen, “Performance of Macro-and Co-Channel Femtocells in a Hierarchical Cell Structure”, Proceedings of International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1-5, 2007.

  • T. Thamaraimanalan, “Multi Biometric Authentication using SVM and ANN Classifiers”, Irish Interdisciplinary Journal of Science and Research, Vol. 8, No. 2, pp. 1-14, 2021.

  • G. Dhiman, A.V. Kumar, R. Nirmalan and K. Srihari, “Multi-Modal Active Learning with Deep Reinforcement Learning for Target Feature Extraction in Multi-Media Image Processing Applications”, Multimedia Tools and Applications, Vol. 2022, pp. 1-25, 2022.

  • S. Hannah, A.J. Deepa, V.S. Chooralil and S. Brilly Sangeetha, “Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data”, BioMed Research International, Vol. 2022, pp. 1-7, 2022.

  • M. Yousefvand, T. Han, N. Ansari and A. Khreishah, “Distributed Energy-Spectrum Trading in Green Cognitive Radio Cellular Networks”, IEEE Transactions on Green Communications and Networking, Vol. 1, No. 3, pp. 253263, 2017.

  • T. Thamaraimanalan, D. Naveena and M. Madhubala, “Prediction and Classification of Fouls in Soccer Game using Deep Learning”, Irish Interdisciplinary Journal of Science and Research, Vol. 4, No. 3, pp. 66-78, 2020.

  • S. Satheeskumaran, C. Venkatesan and S. Saravanan, “RealTime ECG Signal Pre-Processing and Neuro Fuzzy-Based CHD Risk Prediction”, International Journal of Computational Science and Engineering, Vol. 24, No. 4, pp. 323-330, 2021.

  • K. Praghash and T. Karthikeyan, “Binary Flower Pollination (BFP) Approach to Handle the Dynamic Networking Conditions to Deliver Uninterrupted Connectivity”, Wireless Personal Communications, Vol. 82, No. 4, pp. 3383-3402, 2021.

  • H. Saad, A. Mohamed and T. El-Batt, “Distributed Cooperative Q-Learning for Power Allocation in Cognitive Femtocell Networks”, Proceedings of IEEE International Conference on Vehicular Technology, pp. 1-5, 2012.

  • J.R. Tefft and N.J. Kirsch, “A Proximity-Based Q-Learning Reward Function for Femtocell Networks”, Proceedings of IEEE International Conference on Vehicular Technology, pp. 1-5, 2013.

  • M. Bennis, S.M., Perlaza, P. Blasco, Z. Han and H.V. Poor, “Self-Organization in Small Cell Networks: A Reinforcement Learning Approach”, IEEE Transactions on Wireless Communications, Vol. 12, No. 7, pp. 3202-3212, 2013.

  • B. Wen, Z. Gao and H. Cai, “A Q-Learning-Based Downlink Resource Scheduling Method for Capacity Optimization in LTE Femtocells”, Proceedings of 9th International Conference on Computer Science and Education, pp. 625628, 2014.

  • Z. Gao, B. Wen and Z. Su, “Q-Learning-Based Power Control for LTE Enterprise Femtocell Networks”, IEEE Systems Journal, Vol. 11, No. 4, pp. 2699-2707, 2016.

  • M. Miozzo and P. Dini, “Distributed Q-Learning for Energy Harvesting Heterogeneous Networks”, IEEE Proceedings of International Conference on Communication Workshop, pp. 2006-2011, 2015.

  • B. Narmadha, M. Ramkumar and M. Srinivasan, “Household Safety based on IoT”, International Journal of Engineering Development and Research¸ Vol. 8, No. 2, pp. 1-14, 2017.

  • G. Alnwaimi, S. Vahid and K. Moessner, “Dynamic Heterogeneous Learning Games for Opportunistic Access in LTE-Based Macro/Femtocell Deployments”, IEEE Transactions on Wireless Communications, Vol. 14, No. 4, pp. 2294-2308, 2014.

  • K.L.A. Yau and P.D.Teal, “Reinforcement Learning for Context Awareness and Intelligence in Wireless Networks: Review, New Features and Open Issues”, Journal of Network and Computer Applications, Vol. 35, No. 1, pp. 253-267 2012.

  • C. Venkatesan, P. Karthigaikumar, A. Paul, S. Satheeskumaran and R. Kumar, “ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications”, IEEE Access, Vol. 6, pp. 9767-9773, 2018.

  • L. Matignon, G.J Laurent and N.L. Fort-Piat, “Reward Function and Initial Values: Better Choices for Accelerated Goal-Directed Reinforcement Learning”, Proceedings of International Conference on Artificial Neural Networks, pp. 840-849, 2006.

  • R. Amiri, M.A. Almasi and H. Mehrpouyan, “Reinforcement Learning for Self-Organization and Power

  • Control of Two-Tier Heterogeneous Networks”, IEEE Transactions on Wireless Communications, Vol. 18, No. 8, pp. 3933-3947, 2019.

  • S. Arora and S. Singh, “The Firefly Optimization Algorithm: Convergence Analysis and Parameter Selection”, International Journal of Computer Applications, Vol. 69, No. 3, pp. 1-13, 2013.

  • V. Kumar and D. Kumar, “A Systematic Review on Firefly Algorithm: Past, Present, and Future”, Archives of Computational Methods in Engineering, Vol. 28, No. 4, pp. 3269-3291, 2021.


Abstract Views: 282

PDF Views: 0




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

Abstract Views: 282  |  PDF Views: 0

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