Interference-based optimal power-efficient access scheme for cognitive radio networks

In this paper, we propose a new optimization-based access strategy of multi-packet reception (MPR) channel for multiple secondary users (SUs) accessing the primary user (PU) spectrum. We devise an analytical model that realizes the multi-packet access strategy of the SUs. All the network receiving nodes have MPR capability. We aim at maximizing the throughput of the individual SUs subject to the PU's queue stability. Moreover, we are interested in providing an energy-efficient cognitive scheme. Therefore, we include energy constraints on the PU and SU average transmitted energy to the optimization problem. Each SU accesses the medium with certain probability that depends on the PU's activity, i.e., active or inactive. The numerical results show the advantage in terms of SU throughput of the proposed scheme over the conventional access scheme, where the SUs access the channel randomly with fixed power when the PU is sensed to be idle.

[1]  Lang Tong,et al.  Stability and delay of finite-user slotted ALOHA with multipacket reception , 2005, IEEE Transactions on Information Theory.

[2]  Stuart C. Schwartz,et al.  Stability properties of slotted Aloha with multipacket reception capability , 1988 .

[3]  Ahmed El Shafie,et al.  Stability Analysis of an Ordered Cognitive Multiple-Access Protocol , 2013, IEEE Transactions on Vehicular Technology.

[4]  K. J. Ray Liu,et al.  Cognitive multiple access via cooperation: Protocol design and performance analysis , 2007, IEEE Transactions on Information Theory.

[5]  Anthony Ephremides,et al.  On the stability of interacting queues in a multiple-access system , 1988, IEEE Trans. Inf. Theory.

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

[7]  David Grace,et al.  Using cognitive radio to deliver ‘Green’ communications , 2009, 2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[8]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[9]  Ahmed El Shafie,et al.  Optimal Random Access for a Cognitive Radio Terminal with Energy Harvesting Capability , 2013, IEEE Communications Letters.

[10]  Anthony Ephremides,et al.  Stable throughput tradeoffs in cognitive shared channels with cooperative relaying , 2011, 2011 Proceedings IEEE INFOCOM.

[11]  K. J. Ray Liu,et al.  Opportunistic Multiple Access for Cognitive Radio Networks , 2011, IEEE Journal on Selected Areas in Communications.

[12]  Leonard J. Cimini,et al.  Energy-Efficient Cooperative Relaying in Heterogeneous Radio Access Networks , 2012, IEEE Wireless Communications Letters.

[13]  Mohammad Reza Nakhai,et al.  Green Radio Communication Networks , 2012 .

[14]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[15]  Wei Luo,et al.  Stability of N interacting queues in random-access systems , 1999, IEEE Trans. Inf. Theory.

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

[17]  Anthony Ephremides,et al.  On the throughput, capacity, and stability regions of random multiple access , 2005, IEEE Transactions on Information Theory.

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