Energy-Efficient Cooperative Spectrum Sensing in Cognitive Satellite Terrestrial Networks

Having the ability to provide seamless coverage and alleviate the frequency scarcity, the cognitive satellite terrestrial network becomes a promising candidate for future communication networks. In the cognitive network, spectrum sensing plays an important role in detecting the channel state for opportunistic utilization, where cooperative spectrum sensing is employed to improve the sensing performance. Additionally, it is critical for battery-powered satellite mobile terminals to diminish energy consumption costs. In this regard, this paper proposes a novel sensing-based cognitive satellite terrestrial network (SCSTN), which integrates the cognitive satellite terrestrial network with the distributed cooperative spectrum sensing network. Specifically, we focus on energy-efficient cooperative sensing in the SCSTN, which maximizes the energy efficiency (EE) of the cognitive satellite network by a tradeoff between the average throughput and the average energy consumption. In the SCSTN, the energy detection threshold of the sensing node and the rule threshold of fusion affect the average throughput and the average energy consumption. Hence, the objective of this paper is to identify the energy detection threshold of the sensing node and the rule threshold of fusion to achieve the maximum EE. We first study the EE formulation of the rule threshold of fusion when the energy detection threshold of the sensing node is given, and transform the ratio-type objective function of EE into a parametric formulation. Subsequently, by exploring the relationship between the two formulations and making use of the monotonicity of the parametric formulation, an algorithm to obtain the optimal rule threshold of fusion for the original problem is developed. Furthermore, we study the optimal formulation of the energy sensing threshold of the sensing node and discuss the effect of the sensing duration and the number of distributed cooperative terminals on the EE. Lastly, the performance of the proposed method is evaluated through numerical simulation results.

[1]  Vibhum Singh,et al.  Cognitive Hybrid Satellite-Terrestrial Relay Networks with Simultaneous Energy and Information Transmission , 2018, 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS).

[2]  A. Beylot,et al.  Analysis of interference issues in Integrated Satellite and Terrestrial Mobile Systems , 2010, 2010 5th Advanced Satellite Multimedia Systems Conference and the 11th Signal Processing for Space Communications Workshop.

[3]  Andrea J. Goldsmith,et al.  Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective , 2009, Proceedings of the IEEE.

[4]  Steven Grippando,et al.  Comparison of Centralized and Distributed Spectrum Monitoring Methods for Enabling Spectrum Sharing Between Weather Satellites and Terrestrial Networks , 2019, 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[5]  Geoffrey Ye Li,et al.  A survey of energy-efficient wireless communications , 2013, IEEE Communications Surveys & Tutorials.

[6]  Symeon Chatzinotas,et al.  Performance Limits of Cognitive-Uplink FSS and Terrestrial FS for Ka-Band , 2019, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Mingfeng Li,et al.  A high reliable satellite communication for emergency communications , 2011 .

[8]  Symeon Chatzinotas,et al.  Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges , 2018, IEEE Communications Surveys & Tutorials.

[9]  Symeon Chatzinotas,et al.  Resource Allocation for Cognitive Satellite Communications With Incumbent Terrestrial Networks , 2015, IEEE Transactions on Cognitive Communications and Networking.

[10]  Jaewoo So.  Cooperative spectrum sensing with two-stage reporting for cognitive radio networks , 2016 .

[11]  Xiaoning Zhang,et al.  Analysis of mutual interferences between two components in satellite-terrestrial integrated system , 2013, 2013 3rd International Conference on Consumer Electronics, Communications and Networks.

[12]  Hang Zhang,et al.  SE and EE Optimization for Cognitive UAV Network Based on Location Information , 2019, IEEE Access.

[13]  Rui Zhang,et al.  Cooperative Resource Management for Cognitive Satellite-Aerial-Terrestrial Integrated Networks Towards IoT , 2020, IEEE Access.

[14]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[15]  Min Jia,et al.  Joint cooperative spectrum sensing and channel selection optimization for satellite communication systems based on cognitive radio , 2017, Int. J. Satell. Commun. Netw..

[16]  Naitong Zhang,et al.  Broadband Hybrid Satellite-Terrestrial Communication Systems Based on Cognitive Radio toward 5G , 2016, IEEE Wireless Communications.

[17]  T Sudha,et al.  Energy harvesting aided localization for green cognitive radio network , 2017, 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS).

[18]  Ying Chen,et al.  Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things , 2019, IEEE Transactions on Cloud Computing.

[19]  Gan Zheng,et al.  Optimal Power Control for Real-Time Applications in Cognitive Satellite Terrestrial Networks , 2017, IEEE Communications Letters.

[20]  Yiyang Pei,et al.  Energy-Efficient Design of Sequential Channel Sensing in Cognitive Radio Networks: Optimal Sensing Strategy, Power Allocation, and Sensing Order , 2011, IEEE Journal on Selected Areas in Communications.

[21]  Yongqiang Hei,et al.  Energy efficient techniques with sensing time optimization in cognitive radio networks , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[22]  K. B. Letaief,et al.  Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks , 2009, IEEE Transactions on Wireless Communications.

[23]  Mario Marchese,et al.  The impact of delay in software-defined integrated terrestrial-satellite networks , 2018, China Communications.

[24]  H. Vincent Poor,et al.  Cooperative Sensing With Imperfect Reporting Channels: Hard Decisions or Soft Decisions? , 2012, IEEE Transactions on Signal Processing.

[25]  Fatih Alagöz,et al.  Energy Efficiency and Satellite Networking: A Holistic Overview , 2011, Proc. IEEE.

[26]  Symeon Chatzinotas,et al.  Satellite cognitive communications: Interference modeling and techniques selection , 2012, 2012 6th Advanced Satellite Multimedia Systems Conference (ASMS) and 12th Signal Processing for Space Communications Workshop (SPSC).

[27]  Youzheng Wang,et al.  Energy-efficient power allocation over multibeam satellite downlinks with imperfect CSI , 2015, 2015 International Conference on Wireless Communications & Signal Processing (WCSP).

[28]  Ying-Chang Liang,et al.  Sensing-Based Spectrum Sharing in Cognitive Radio Networks , 2008, IEEE Transactions on Vehicular Technology.

[29]  Arumugam Nallanathan,et al.  On the Throughput and Spectrum Sensing Enhancement of Opportunistic Spectrum Access Cognitive Radio Networks , 2012, IEEE Transactions on Wireless Communications.

[30]  Ming Chen,et al.  Optimal Energy-Efficient Sensing and Power Allocation in Cognitive Radio Networks , 2014 .

[31]  Ying-Chang Liang,et al.  State of the Art, Taxonomy, and Open Issues on Cognitive Radio Networks with NOMA , 2018, IEEE Wireless Communications.

[32]  Hang Hu,et al.  Delay QoS guaranteed cooperative spectrum sensing in cognitive radio networks , 2013 .

[33]  Yongming Huang,et al.  Outage Performance of Cognitive Hybrid Satellite–Terrestrial Networks With Interference Constraint , 2016, IEEE Transactions on Vehicular Technology.

[34]  Wanrong Yu,et al.  Networking in Space Terrestrial Integrated Networks , 2019, 2019 18th International Conference on Optical Communications and Networks (ICOCN).

[35]  Dae-Sub Oh,et al.  Performance Analysis of Dynamic Resource Allocation for Interference Mitigation in Integrated Satellite and Terrestrial Systems , 2015, 2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies.

[36]  Philip Constantinou,et al.  Power Allocation in Cognitive Satellite Terrestrial Networks with QoS Constraints , 2013, IEEE Communications Letters.

[37]  Hsiao-Hwa Chen,et al.  Energy-efficient non-cooperative cognitive radio networks: micro, meso, and macro views , 2014, IEEE Communications Magazine.

[38]  Symeon Chatzinotas,et al.  Cognitive approaches to enhance spectrum availability for satellite systems , 2017, Int. J. Satell. Commun. Netw..

[39]  Liang Gou,et al.  Optimal Power Control for Cognitive LEO Constellation With Terrestrial Networks , 2020, IEEE Communications Letters.

[40]  Luc Vandendorpe,et al.  Sensing throughput tradeoff for cognitive radio networks with noise variance uncertainty , 2014, 2014 9th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM).

[41]  Eytan Modiano,et al.  Optimal energy allocation and admission control for communications satellites , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[42]  Hang Zhang,et al.  Optimization of Energy Management for UAV-Enabled Cognitive Radio , 2020, IEEE Wireless Communications Letters.

[43]  Xing Zhang,et al.  Green Hybrid Satellite Terrestrial Networks: Fundamental Trade-Off Analysis , 2016, 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring).