Throughput maximisation in cognitive radio networks with residual bandwidth

Recent progress in cognitive radio networks (CRNs) promises to meet device-to-device communication requirements for spectrum utilisation and power control to support billions of machines/devices to be connected worldwide. The architecture of the CRN must maintain a high data rate (throughput) at low power consumption, which requires both spectrum efficient and energy efficient system design. To this aim, the proposed work adopts a CRN model, which operates in an interweave mode that allows spectrum sensing followed by opportunistic secondary user (SU) data transmission over the unused bandwidth of the primary user (PU) in a non-overlapping frame structure. Closed form expressions of the optimal spectrum sensing duration, bandwidth, and power allocation of each secondary node are obtained to maximise the sum throughput of the overall CRN while maintaining the constraints of sensing reliability of PU, individual SU transmission outage probability, permissible interference, and residual bandwidth. Numerical results highlight that the proposed scheme maximises the sum network throughput by $\sim 50.54$~50.54 and $\sim 63.76\%$~63.76% over the other existing techniques.

[1]  Jian Tang,et al.  Spectral and Energy Efficiency in Cognitive Radio Systems With Unslotted Primary Users and Sensing Uncertainty , 2017, IEEE Transactions on Communications.

[2]  Anal Paul,et al.  Joint Power Allocation and Route Selection for Outage Minimization in Multihop Cognitive Radio Networks with Energy Harvesting , 2018, IEEE Transactions on Cognitive Communications and Networking.

[3]  Santi P. Maity,et al.  On Throughput Maximization in Cooperative Cognitive Radio Networks With Eavesdropping , 2019, IEEE Communications Letters.

[4]  Santi P. Maity,et al.  On Residual Energy Maximization in Cognitive Relay Networks With Eavesdropping , 2019, IEEE Systems Journal.

[5]  Fabrizio Granelli,et al.  On Results’ Reporting of Cooperative Spectrum Sensing in Cognitive Radio Networks , 2016, Telecommun. Syst..

[6]  Symeon Chatzinotas,et al.  Sensing-Throughput Tradeoff for Interweave Cognitive Radio System: A Deployment-Centric Viewpoint , 2015, IEEE Transactions on Wireless Communications.

[7]  Athanasios D. Panagopoulos,et al.  QoS-driven power and time allocation scheme for spectrum leasing in overlay cognitive radio networks , 2018, IET Commun..

[8]  Mérouane Debbah,et al.  Joint Stochastic Geometry and Mean Field Game Optimization for Energy-Efficient Proactive Scheduling in Ultra Dense Networks , 2017, IEEE Transactions on Cognitive Communications and Networking.

[9]  Fabrizio Granelli,et al.  Cooperative spectrum sensing for cognitive radio networks under limited time constraints , 2014, Comput. Commun..

[10]  Zhigang Chen,et al.  Resource allocation optimisation for delay-sensitive traffic in energy harvesting cloud radio access network , 2018, IET Commun..

[11]  Nellore Kapileswar,et al.  Maximizing Cognitive Radio Networks Throughput Using Limited Historical Behavior of Primary Users , 2018, IEEE Access.

[12]  Priyadip Ray,et al.  Simultaneous Detection and Channel Estimation for Censoring-Based Spectrum Sensing in Cognitive Radio Networks , 2018, IEEE Wireless Communications Letters.

[13]  Hong Jiang,et al.  Energy-efficient sensing and transmission for multi-hop relay cognitive radio sensor networks , 2018, China Communications.

[14]  Zhetao Li,et al.  Dynamic Compressive Wide-Band Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

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

[16]  Nirwan Ansari,et al.  Joint spectrum allocation and energy harvesting optimization in green powered heterogeneous cognitive radio networks , 2018, Comput. Commun..

[17]  Santi P. Maity,et al.  Energy Efficiency in Cooperative Cognitive Radio Network in the Presence of Malicious Users , 2018, IEEE Systems Journal.

[18]  Sonia Aïssa,et al.  Full-Duplex Cognitive Radio With Asynchronous Energy-Efficient Sensing , 2018, IEEE Transactions on Wireless Communications.