Sensing Time Optimization and Power Control for Energy Efficient Cognitive Small Cell With Imperfect Hybrid Spectrum Sensing

Cognitive radio enabled small cell network is an emerging technology to address the exponential increase of mobile traffic demand in next generation mobile communications. Recently, many technological issues, such as resource allocation and interference mitigation pertaining to cognitive small cell network have been studied, but most studies focus on maximizing spectral efficiency. Different from the existing works, we investigate the power control and sensing time optimization problem in a cognitive small cell network, where the cross-tier interference mitigation, imperfect hybrid spectrum sensing, and energy efficiency are considered. The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem. We solve the proposed problem in an asymptotically optimal manner. An iterative power control algorithm and a near optimal sensing time scheme are developed by considering imperfect hybrid spectrum sensing, cross-tier interference mitigation, minimum data rate requirement, and energy efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithms for energy efficient resource allocation in the cognitive small cell network.

[1]  Kwang-Cheng Chen,et al.  Design and Analysis of Downlink Spectrum Sharing in Two-Tier Cognitive Femto Networks , 2012, IEEE Transactions on Vehicular Technology.

[2]  Jing Wang,et al.  Cognitive radio in 5G: a perspective on energy-spectral efficiency trade-off , 2014, IEEE Communications Magazine.

[3]  Victor C. M. Leung,et al.  Hybrid Spectrum Sensing Based Power Control for Energy Efficient Cognitive Small Cell Network , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[4]  Xiaoli Chu,et al.  Coexistence of Wi-Fi and heterogeneous small cell networks sharing unlicensed spectrum , 2015, IEEE Communications Magazine.

[5]  Dong In Kim,et al.  QoS-Aware and Energy-Efficient Resource Management in OFDMA Femtocells , 2013, IEEE Transactions on Wireless Communications.

[6]  I. Stancu-Minasian.  Nonlinear Fractional Programming , 1997 .

[7]  Energy-Efficient Design for Downlink OFDMA with Delay-Sensitive Traffic , 2013, IEEE Transactions on Wireless Communications.

[8]  Victor C. M. Leung,et al.  Fronthauling for 5G LTE-U Ultra Dense Cloud Small Cell Networks , 2016, IEEE Wireless Communications.

[9]  Derrick Wing Kwan Ng,et al.  Energy-efficient resource allocation in multi-cell OFDMA systems with limited backhaul capacity , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[10]  Guanding Yu,et al.  Cognitive radio enhanced interference coordination for femtocell networks , 2013, IEEE Communications Magazine.

[11]  Zhu Han,et al.  Self-Organization in Small Cell Networks: A Reinforcement Learning Approach , 2013, IEEE Transactions on Wireless Communications.

[12]  Pin-Han Ho,et al.  Interference Analysis and Mitigation for Cognitive-Empowered Femtocells Through Stochastic Dual Control , 2012, IEEE Transactions on Wireless Communications.

[13]  Matti Latva-aho,et al.  Ultra Dense Small Cell Networks: Turning Density Into Energy Efficiency , 2016, IEEE Journal on Selected Areas in Communications.

[14]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[15]  Chunxiao Jiang,et al.  Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach , 2015, IEEE Transactions on Wireless Communications.

[16]  Michael J. Neely,et al.  Opportunistic Cooperation in Cognitive Femtocell Networks , 2011, IEEE Journal on Selected Areas in Communications.

[17]  Donglin Hu,et al.  On Medium Grain Scalable Video Streaming over Femtocell Cognitive Radio Networks , 2012, IEEE Journal on Selected Areas in Communications.

[18]  Xiaojiang Du,et al.  Cognitive femtocell networks: an opportunistic spectrum access for future indoor wireless coverage , 2013, IEEE Wireless Communications.

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

[20]  F. Richard Yu,et al.  Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells , 2012, IEEE Transactions on Wireless Communications.

[21]  Arumugam Nallanathan,et al.  On the Outage Capacity of Sensing-Enhanced Spectrum Sharing Cognitive Radio Systems in Fading Channels , 2011, IEEE Transactions on Communications.

[22]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Trans. Wirel. Commun..

[23]  Hsiao-Hwa Chen,et al.  Interference-Limited Resource Optimization in Cognitive Femtocells With Fairness and Imperfect Spectrum Sensing , 2016, IEEE Transactions on Vehicular Technology.

[24]  Meixia Tao,et al.  Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services , 2014, IEEE Transactions on Communications.

[25]  Meryem Simsek,et al.  When cellular meets WiFi in wireless small cell networks , 2013, IEEE Communications Magazine.

[26]  Xianfu Chen,et al.  Energy-Efficiency Oriented Traffic Offloading in Wireless Networks: A Brief Survey and a Learning Approach for Heterogeneous Cellular Networks , 2015, IEEE Journal on Selected Areas in Communications.

[27]  Sergio Barbarossa,et al.  Joint Optimization of Collaborative Sensing and Radio Resource Allocation in Small-Cell Networks , 2013, IEEE Transactions on Signal Processing.

[28]  Xianfu Chen,et al.  Stochastic Power Adaptation with Multiagent Reinforcement Learning for Cognitive Wireless Mesh Networks , 2013, IEEE Transactions on Mobile Computing.