Discrete Breathing: An Energy Efficient Resource Scheduling for Future Wireless Networks

With ever growing smart applications and data demand, the energy consumption of a cellular network is increasing. Also, with increase in the number of base stations deployed, the cell edge users are greatly affected. Increase in energy consumption causes increase in operational expenditure of the system while inefficient handling of cell edge users results in decreased quality of experience (QoE) of users. Therefore, in this paper, we propose an energy efficient scheduling algorithm called discrete breathing which aims at minimizing the energy consumption of the system, such that the interference of cell edge users is mitigated. Furthermore, we analyze the proposed system and derive some important theorems that make the system energy efficient. We then propose a modified algorithm to consider QoE of users to schedule the resources (resource blocks). We also show that the proposed algorithm is generic and can be applied to various advanced networks, such as heterogeneous networks and dense femtocell networks. By extensive simulations we show the effectiveness of our proposed algorithm in terms of energy consumption, throughput, and energy consumption rating.

[1]  Abdallah Shami,et al.  QoS-Aware Energy-Efficient Downlink Predictive Scheduler for OFDMA-Based Cellular Devices , 2017, IEEE Transactions on Vehicular Technology.

[2]  Xiaoli Chu,et al.  Coverage Analysis of Reduced Power Subframes Applied in Heterogeneous Networks With Subframe Misalignment Interference , 2018, IEEE Wireless Communications Letters.

[3]  Cong Xiong,et al.  Energy-efficient wireless communications: tutorial, survey, and open issues , 2011, IEEE Wireless Communications.

[4]  Jianchao Zheng,et al.  QoE Driven Decentralized Spectrum Sharing in 5G Networks: Potential Game Approach , 2017, IEEE Transactions on Vehicular Technology.

[5]  Gerhard Fettweis,et al.  Power consumption modeling of different base station types in heterogeneous cellular networks , 2010, 2010 Future Network & Mobile Summit.

[6]  Jianhua Lu,et al.  An energy-efficient hybrid structure with resource allocation in OFDMA networks , 2011, 2011 IEEE Wireless Communications and Networking Conference.

[7]  Abraham O. Fapojuwo,et al.  A Survey of Energy Efficient Resource Management Techniques for Multicell Cellular Networks , 2014, IEEE Communications Surveys & Tutorials.

[8]  Chandra S. Bontu,et al.  DRX mechanism for power saving in LTE , 2009, IEEE Communications Magazine.

[9]  Mohsen Guizani,et al.  Efficient Usage of Renewable Energy in Communication Systems Using Dynamic Spectrum Allocation and Collaborative Hybrid Powering , 2016, IEEE Transactions on Wireless Communications.

[10]  Abdallah Shami,et al.  QoS-Aware Energy and Jitter-Efficient Downlink Predictive Scheduler for Heterogeneous Traffic LTE Networks , 2018, IEEE Transactions on Mobile Computing.

[11]  Satish Kumar,et al.  Co-operative downlink scheduling for cell edge and handoff users , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  Maria Rita Palattella,et al.  Internet of Things in the 5G Era: Enablers, Architecture, and Business Models , 2016, IEEE Journal on Selected Areas in Communications.

[13]  Dong-Ho Cho,et al.  Mobile Data Offloading With Almost Blank Subframe in LTE-LAA and Wi-Fi Coexisting Networks Based on Coalition Game , 2017, IEEE Communications Letters.

[14]  Gerhard Fettweis,et al.  Green Resource Allocation to Minimize Receiving Energy in OFDMA Cellular Systems , 2012, IEEE Communications Letters.

[15]  Derrick Wing Kwan Ng,et al.  Energy-Efficient Resource Allocation in Multi-Cell OFDMA Systems with Limited Backhaul Capacity , 2012, IEEE Trans. Wirel. Commun..

[16]  Muhammad Ali Imran,et al.  On the Energy Efficiency-Spectral Efficiency Trade-Off in the Uplink of CoMP System , 2012, IEEE Transactions on Wireless Communications.

[17]  Xiaohu You,et al.  Energy-Efficient Resource Allocation in Coordinated Downlink Multicell OFDMA Systems , 2016, IEEE Transactions on Vehicular Technology.

[18]  C. Siva Ram Murthy,et al.  A Q-Learning Framework for User QoE Enhanced Self-Organizing Spectrally Efficient Network Using a Novel Inter-Operator Proximal Spectrum Sharing , 2016, IEEE Journal on Selected Areas in Communications.

[19]  C. Siva Ram Murthy,et al.  Breathe to Save Energy: Assigning Downlink Transmit Power and Resource Blocks to LTE Enabled IoT Networks , 2016, IEEE Communications Letters.

[20]  Mohsen Guizani,et al.  Distributed Learning-Based Cross-Layer Technique for Energy-Efficient Multicarrier Dynamic Spectrum Access With Adaptive Power Allocation , 2016, IEEE Transactions on Wireless Communications.

[21]  F. Richard Yu,et al.  Enhancing cell edge users performance in open access small cells networks: A Cross layer approach , 2014, 2014 IEEE Global Communications Conference.

[22]  L. Wosinska,et al.  Mobile backhaul in heterogeneous network deployments: Technology options and power consumption , 2012, 2012 14th International Conference on Transparent Optical Networks (ICTON).

[23]  Youngnam Han,et al.  Cell selection for range expansion with almost blank subframe in heterogeneous networks , 2012, 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC).

[24]  C. Siva Ram Murthy,et al.  An Energy and Cost Aware Framework for Cell Selection and Energy Cooperation in Rural and Remote Femtocell Networks , 2017, IEEE Transactions on Green Communications and Networking.