Joint Power, Original Bandwidth, and Detected Hole Bandwidth Allocation for Multi-Homing Heterogeneous Networks Based on Cognitive Radio

In this paper, we investigate a joint resource allocation problem based on cognitive radio (CR) techniques for user equipment with multi-homing capabilities. We consider a heterogeneous wireless medium where users in overlapping coverage areas simultaneously communicate with different base stations and access points. Currently, existing works assume that the working frequency bands of different networks are separated. Unlike these works, this paper focuses on the multi-homing networks, which can share spectrum resources of each other to enhance the resource utilization efficiency. Based on spectrum sensing and spectrum sharing techniques in CR, we propose and then formulate an uplink joint original bandwidth, detected hole bandwidth and power allocation method. Specifically, the formulated optimization problem is a mixed integer nonlinear optimization problem. We adopt the continuity relaxation method to further transform it into a convex optimization problem and then solve it by Lagrange dual solution. A suboptimal method is further proposed with a reduced system overhead. Simulation results demonstrate the significantly improved performance of our proposed methods (both optimal and suboptimal) in terms of system throughput and energy efficiency over a joint resource allocation benchmark. Our results also indicate that the suboptimal strategy can indeed reduce the system overhead remarkably.

[1]  Markku J. Juntti,et al.  Energy-Efficient Bandwidth and Power Allocation for Multi-Homing Networks , 2015, IEEE Transactions on Signal Processing.

[2]  Martin Reisslein,et al.  Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols , 2016, IEEE Communications Surveys & Tutorials.

[3]  Weihua Zhuang,et al.  Decentralized Radio Resource Allocation for Single-Network and Multi-Homing Services in Cooperative Heterogeneous Wireless Access Medium , 2012, IEEE Transactions on Wireless Communications.

[4]  Lei Xu,et al.  Joint Spectrum Allocation and Pricing for Cognitive Multi-Homing Networks , 2018, IEEE Transactions on Cognitive Communications and Networking.

[5]  Johann M. Marquez-Barja,et al.  5G: Adaptable Networks Enabled by Versatile Radio Access Technologies , 2017, IEEE Communications Surveys & Tutorials.

[6]  Zhijin Qin,et al.  Malicious User Detection Based on Low-Rank Matrix Completion in Wideband Spectrum Sensing , 2018, IEEE Transactions on Signal Processing.

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

[8]  Liu,et al.  Enhancing the Physical Layer Security of Non-Orthogonal Multiple Access in Large-Scale Networks , 2016, IEEE Transactions on Wireless Communications.

[9]  Weihua Zhuang,et al.  Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey , 2016, IEEE Transactions on Vehicular Technology.

[10]  Jian Yang,et al.  Power and Bandwidth Allocation for Cognitive Heterogeneous Multi-Homing Networks , 2018, IEEE Transactions on Communications.

[11]  Mohamed Saad Zaghloul,et al.  Comparative Study of Spectrum Sensing for Cognitive Radio System Using Energy Detection over Different Channels , 2016, 2016 World Symposium on Computer Applications & Research (WSCAR).

[12]  Zhu Han,et al.  QoE-Driven Channel Allocation and Handoff Management for Seamless Multimedia in Cognitive 5G Cellular Networks , 2017, IEEE Transactions on Vehicular Technology.

[13]  Yue Gao,et al.  Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals Under Sub-Nyquist Rate , 2016, IEEE Transactions on Wireless Communications.

[14]  Mohamed-Slim Alouini,et al.  Achievable Rate of Spectrum Sharing Cognitive Radio Multiple-Antenna Channels , 2015, IEEE Transactions on Wireless Communications.

[15]  Bala Srinivasan,et al.  Exclusive Use Spectrum Access Trading Models in Cognitive Radio Networks: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[16]  Zhijin Qin,et al.  Wideband Spectrum Sensing on Real-Time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes , 2016, IEEE Transactions on Signal Processing.

[17]  Byeong Gi Lee,et al.  Energy-Per-Bit Minimized Radio Resource Allocation in Heterogeneous Networks , 2014, IEEE Transactions on Wireless Communications.

[18]  Yi Sun,et al.  Interference Alignment Based on Antenna Selection With Imperfect Channel State Information in Cognitive Radio Networks , 2016, IEEE Transactions on Vehicular Technology.

[19]  Zhi-Hua Zhou,et al.  Resource allocation for heterogeneous multiuser OFDM-based cognitive radio networks with imperfect spectrum sensing , 2012, 2012 Proceedings IEEE INFOCOM.

[20]  Nada Chendeb Taher,et al.  Comparing Resource Allocation Schemes in Multi-Homed LTE/WiFi Access Networks , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[21]  Arumugam Nallanathan,et al.  Optimal Sensing Time and Power Allocation in Multiband Cognitive Radio Networks , 2010 .

[22]  Byeong Gi Lee,et al.  Radio resource allocation for energy consumption minimization in multi-homed wireless networks , 2013, 2013 IEEE International Conference on Communications (ICC).

[23]  Minglu Jin,et al.  Optimal Transceiver Design for Interference Alignment Based Cognitive Radio Networks , 2015, IEEE Communications Letters.

[24]  Lei Xu,et al.  Spectrum allocation for cognitive multi-homing networks , 2017, 2017 3rd IEEE International Conference on Computer and Communications (ICCC).

[25]  Tarik Taleb,et al.  Dynamic Clustering-Based Adaptive Mobile Gateway Management in Integrated VANET — 3G Heterogeneous Wireless Networks , 2011, IEEE Journal on Selected Areas in Communications.

[26]  Jun Li,et al.  Rayleigh flat fading channels' capacity , 2005, 3rd Annual Communication Networks and Services Research Conference (CNSR'05).

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

[28]  Markku J. Juntti,et al.  Optimal Energy Efficient Resource Allocation for Heterogeneous Multi-Homing Networks , 2014, 2014 IEEE 79th Vehicular Technology Conference (VTC Spring).

[29]  Octavia A. Dobre,et al.  A Multiobjective Optimization Approach for Optimal Link Adaptation of OFDM-Based Cognitive Radio Systems with Imperfect Spectrum Sensing , 2014, IEEE Transactions on Wireless Communications.

[30]  Weihua Zhuang,et al.  A Distributed Multi-Service Resource Allocation Algorithm in Heterogeneous Wireless Access Medium , 2012, IEEE Journal on Selected Areas in Communications.

[31]  Weihua Zhuang,et al.  Radio Resource Allocation for Single-Network and Multi-Homing Services in Heterogeneous Wireless Access Medium , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[32]  Tarik Taleb,et al.  Optimizing service replication for mobile delay-sensitive applications in 5G edge network , 2017, 2017 IEEE International Conference on Communications (ICC).

[33]  Julien Montavont,et al.  Multihoming in IPv6 mobile networks: progress, challenges, and solutions , 2013, IEEE Communications Magazine.

[34]  Ali Jamshidi,et al.  Capacity and power allocation for spectrum sharing in cognitive radio systems under unknown channel state information and imperfect spectrum sensing , 2012, IET Commun..

[35]  Weihua Zhuang,et al.  Cooperative Decentralized Resource Allocation in Heterogeneous Wireless Access Medium , 2013, IEEE Transactions on Wireless Communications.

[36]  Yue Gao,et al.  Scalable and Reliable IoT Enabled by Dynamic Spectrum Management for M2M in LTE-A , 2016, IEEE Internet of Things Journal.

[37]  Wen-Kang Jia,et al.  Joint resource allocation for multi-homing and single-network users in heterogeneous cognitive radio networks , 2017, 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP).

[38]  Peng Gong,et al.  Energy-Efficient Resource Optimization for OFDMA-Based Multi-Homing Heterogenous Wireless Networks , 2016, IEEE Transactions on Signal Processing.

[39]  Weihua Zhuang,et al.  Uplink Decentralized Joint Bandwidth and Power Allocation for Energy-Efficient Operation in a Heterogeneous Wireless Medium , 2015, IEEE Transactions on Communications.

[40]  Bongyong Song,et al.  A holistic view on hyper-dense heterogeneous and small cell networks , 2013, IEEE Communications Magazine.