Optimal Power Control for Underlay Cognitive Radio Systems With Arbitrary Input Distributions

This paper studies optimal power control policies that maximize the achievable rates of underlay cognitive radio systems with arbitrary input distributions under both peak/average transmit power and peak/average interference power constraints for general fading distributions. In particular, optimal power adaptation schemes are formulated and low-complexity optimal power control algorithms are proposed. Additionally, simpler approximations of optimal power control policies in the low-power regime are determined. By considering gamma distributed channel power gains of the interference link between the secondary transmitter and the primary receiver and of the transmission link between the secondary transmitter and the secondary receiver, closed-form expressions for the maximum achievable rate attained with optimal power control in the low-power regime are provided. Through numerical results, the impact of the fading severity of both interference and transmission links and transmit power and interference power constraints on the maximum achievable rate of the cognitive user for different practical constellations and Gaussian signals are investigated.

[1]  Ying-Chang Liang,et al.  Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity , 2008, IEEE Transactions on Wireless Communications.

[2]  Shlomo Shamai,et al.  Mutual information and minimum mean-square error in Gaussian channels , 2004, IEEE Transactions on Information Theory.

[3]  Ying-Chang Liang,et al.  Optimal power allocation for OFDM-based cognitive radio with new primary transmission protection criteria , 2010, IEEE Transactions on Wireless Communications.

[4]  Leila Musavian,et al.  Outage-constrained capacity of spectrum-sharing channels in fading environments , 2008, IET Commun..

[5]  Lars K. Rasmussen,et al.  Power allocation for block-fading channels with arbitrary input constellations , 2009, IEEE Transactions on Wireless Communications.

[6]  Mohamed-Slim Alouini,et al.  On the Capacity of Nakagami-m Fading Channels with Full Channel State Information at Low SNR , 2012, IEEE Wireless Communications Letters.

[7]  Brian M. Sadler,et al.  Opportunistic Spectrum Access via Periodic Channel Sensing , 2008, IEEE Transactions on Signal Processing.

[8]  Hiroyuki Ishii,et al.  Distributions of Transmit Power and SINR in Device-to-Device Networks , 2013, IEEE Communications Letters.

[9]  Jon M. Peha,et al.  Approaches to spectrum sharing , 2005, IEEE Communications Magazine.

[10]  Mohamed-Slim Alouini,et al.  Capacity of spectrum sharing Cognitive Radio systems over Nakagami fading channels at low SNR , 2013, 2013 IEEE International Conference on Communications (ICC).

[11]  Daniel Pérez Palomar,et al.  A tutorial on decomposition methods for network utility maximization , 2006, IEEE Journal on Selected Areas in Communications.

[12]  Gaston H. Gonnet,et al.  On the LambertW function , 1996, Adv. Comput. Math..

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

[14]  Antonia Maria Tulino,et al.  Optimum power allocation for parallel Gaussian channels with arbitrary input distributions , 2006, IEEE Transactions on Information Theory.

[15]  Ying-Chang Liang,et al.  Optimal Power Allocation Strategies for Fading Cognitive Radio Channels with Primary User Outage Constraint , 2011, IEEE Journal on Selected Areas in Communications.

[16]  Santosh Pandey,et al.  IEEE 802.11af: a standard for TV white space spectrum sharing , 2013, IEEE Communications Magazine.

[17]  Ekram Hossain,et al.  Dynamic Spectrum Access and Management in Cognitive Radio Networks: Introduction , 2009 .

[18]  Sooyong Choi,et al.  Joint Mode Selection and Power Allocation Scheme for Power-Efficient Device-to-Device (D2D) Communication , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[19]  Sonia Aïssa,et al.  Capacity and power allocation for spectrum-sharing communications in fading channels , 2009, IEEE Transactions on Wireless Communications.

[20]  Ekram Hossain,et al.  Dynamic Spectrum Access and Management in Cognitive Radio Networks , 2009 .

[21]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[22]  Andrea Goldsmith,et al.  Principles of Cognitive Radio , 2012 .

[23]  Andrea Goldsmith,et al.  Wireless Communications , 2005, 2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS).

[24]  Zhongding Lei,et al.  IEEE 802.22: The first cognitive radio wireless regional area network standard , 2009, IEEE Communications Magazine.

[25]  A. Goldsmith,et al.  Capacity of Rayleigh fading channels under different adaptive transmission and diversity-combining techniques , 1999, IEEE Transactions on Vehicular Technology.

[26]  Kazuhiko Aomoto,et al.  Theory of Hypergeometric Functions , 2011 .

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

[28]  Leila Musavian,et al.  Effective capacity of delay-constrained cognitive radio in Nakagami fading channels , 2010, IEEE Transactions on Wireless Communications.

[29]  Joseph Lipka,et al.  A Table of Integrals , 2010 .

[30]  Youyun Xu,et al.  Resource allocation for cognitive networks with D2D communication: An evolutionary approach , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[31]  Fabrizio Granelli,et al.  Standardization and research in cognitive and dynamic spectrum access networks: IEEE SCC41 efforts and other activities , 2010, IEEE Communications Magazine.

[32]  Andrea Abrardo,et al.  Performance analysis of a distributed resource allocation scheme for D2D communications , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[33]  D. Owen Handbook of Mathematical Functions with Formulas , 1965 .