Multicast in multi-channel cognitive radio ad hoc networks: Challenges and research aspects

Abstract Cognitive radio (CR) is an emerging technology that has been around for more than fifteen years to relieve spectrum shortages. Based on the concept of CR, multiple nodes opportunistically share the licensed spectrum over multiple licensed channels and form a multi-channel cognitive radio ad hoc networks (CRAHNs). While multicast in multi-channel CRAHNs is urgently needed, it is challenging since there are some intrinsic differences between multi-channel CRAHNs and conventional multi-channel wireless ad hoc networks (WAHNs). This article summarizes the main unique characteristics of multi-channel CRAHNs in time-, frequency- and space-domain. The key research aspects of multicast in multi-channel CRAHNs include joint multicast routing and spectrum allocation, cross-layer multicast scheduling, and multicast with QoS guarantee. The article also pays a special attention on discussing how to employ network coding techniques to improve the performance of multicast in multi-channel CRAHNs.

[1]  Yan Chen,et al.  On cognitive radio networks with opportunistic power control strategies in fading channels , 2008, IEEE Transactions on Wireless Communications.

[2]  Miao Pan,et al.  Session-Based Cooperation in Cognitive Radio Networks: A Network-Level Approach , 2017, IEEE/ACM Transactions on Networking.

[3]  Hanif D. Sherali,et al.  Multicast Communications in Multi-Hop Cognitive Radio Networks , 2011, IEEE Journal on Selected Areas in Communications.

[4]  Qihui Wu,et al.  Spatial-Temporal Opportunity Detection for Spectrum-Heterogeneous Cognitive Radio Networks: Two-Dimensional Sensing , 2013, IEEE Transactions on Wireless Communications.

[5]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[6]  Uma Bhattacharya,et al.  On Green Multicasting Over Cognitive Radio Fading Channels , 2017, IEEE Transactions on Vehicular Technology.

[7]  Xiaohua Jia,et al.  QoS multicast routing in cognitive radio ad hoc networks , 2012, Int. J. Commun. Syst..

[8]  Wei Liang,et al.  End-to-End Throughput Maximization for Underlay Multi-Hop Cognitive Radio Networks With RF Energy Harvesting , 2017, IEEE Transactions on Wireless Communications.

[9]  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.

[10]  Nirwan Ansari,et al.  On Green-Energy-Powered Cognitive Radio Networks , 2014, IEEE Communications Surveys & Tutorials.

[11]  Xinming Huang,et al.  Multicast communications in cognitive radio networks using directional antennas , 2015, Wirel. Commun. Mob. Comput..

[12]  Sanjay Dhar Roy,et al.  Throughput of an Energy Harvesting Cognitive Radio Network Based on Prediction of Primary User , 2017, IEEE Transactions on Vehicular Technology.

[13]  Tracey Ho,et al.  A Random Linear Network Coding Approach to Multicast , 2006, IEEE Transactions on Information Theory.

[14]  Alagan Anpalagan,et al.  Opportunistic Spectrum Access in Unknown Dynamic Environment: A Game-Theoretic Stochastic Learning Solution , 2012, IEEE Transactions on Wireless Communications.

[15]  Ahmed E. Kamal,et al.  Exploiting Multichannel Diversity for Cooperative Multicast in Cognitive Radio Mesh Networks , 2014, IEEE/ACM Transactions on Networking.

[16]  Fang Zhao,et al.  Minimum-cost multicast over coded packet networks , 2005, IEEE Transactions on Information Theory.

[17]  Rudolf Ahlswede,et al.  Network information flow , 2000, IEEE Trans. Inf. Theory.

[18]  Shaojie Tang,et al.  Spectrum-Aware Network Coded Multicast in Mobile Cognitive Radio Ad Hoc Networks , 2017, IEEE Transactions on Vehicular Technology.

[19]  Xiaohui Zhao,et al.  Throughput maximization-based optimal power allocation for energy-harvesting cognitive radio networks with multiusers , 2018, EURASIP J. Wirel. Commun. Netw..

[20]  Ramón Agustí,et al.  Multiuser Resource Allocation Optimization Using Bandwidth-Power Product in Cognitive Radio Networks , 2013, IEEE Journal on Selected Areas in Communications.

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

[22]  Minyi Guo,et al.  Delay-Minimized Routing in Mobile Cognitive Networks for Time-Critical Applications , 2017, IEEE Transactions on Industrial Informatics.

[23]  Miao Pan,et al.  Spectrum Harvesting and Sharing in Multi-Hop CRNs Under Uncertain Spectrum Supply , 2012, IEEE Journal on Selected Areas in Communications.

[24]  Qinghua Guo,et al.  Multichannel Selection for Cognitive Radio Networks With RF Energy Harvesting , 2018, IEEE Wireless Communications Letters.