Power Consumption Minimization for MIMO Systems — A Cognitive Radio Approach

This paper shows how cognitive radio (CR) can help to optimize system power consumption of multiple input multiple output (MIMO) communication systems. Leveraging results from information theory and capabilities of a CR (e.g., the awareness of the component capabilities and characteristics), a theoretical framework is developed to minimize the system power consumption of MIMO systems while still considering radiated power. This paper mathematically formulates the system power consumption minimization problem under a sum rate constraint for MIMO systems. The impact of channel correlation and partial channel state information at the transmitter is considered. Numerical algorithms are developed to solve the constrained optimization problem. The simulation results show that significant power savings (e.g., up to 75% for a 4 × 4 MIMO system with Class A power amplifiers) can be achieved compared to conventional power allocation schemes. The results also show that the more computationally efficient suboptimal heuristic algorithms can achieve power savings comparable to the exhaustive search algorithm.

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

[2]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[3]  Preben E. Mogensen,et al.  A stochastic MIMO radio channel model with experimental validation , 2002, IEEE J. Sel. Areas Commun..

[4]  Zhu Han,et al.  Power minimization under throughput management over wireless networks with antenna diversity , 2004, IEEE Transactions on Wireless Communications.

[5]  Friedrich Jondral,et al.  Average efficiency of power amplifiers in power-controlled systems with multi-antenna diversity , 2011, 2011 Wireless Telecommunications Symposium (WTS).

[6]  Andrea J. Goldsmith,et al.  Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[7]  An He,et al.  Development of a case-based reasoning cognitive engine for IEEE 802.22 WRAN applications , 2009, MOCO.

[8]  William H. Tranter,et al.  Minimizing Energy Consumption Using Cognitive Radio , 2008, 2008 IEEE International Performance, Computing and Communications Conference.

[9]  Andrea J. Goldsmith,et al.  Capacity limits of MIMO channels , 2003, IEEE J. Sel. Areas Commun..

[10]  R. Min,et al.  A framework for energy-scalable communication in high-density wireless networks , 2002, Proceedings of the International Symposium on Low Power Electronics and Design.

[11]  Björn E. Ottersten,et al.  A wideband statistical model for NLOS indoor MIMO channels , 2002, Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367).

[12]  B. Daneshrad,et al.  Energy-aware link adaptation for MIMO-OFDM based wireless communication , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[13]  Valentine A. Aalo,et al.  Performance of maximal-ratio diversity systems in a correlated Nakagami-fading environment , 1995, IEEE Trans. Commun..

[14]  D.R. Farris.  Electronic circuit analysis and design , 1978, Proceedings of the IEEE.

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

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

[17]  Marian Verhelst,et al.  System power consumption minimization for multichannel communications using cognitive radio , 2009, 2009 IEEE International Conference on Microwaves, Communications, Antennas and Electronics Systems.

[18]  Donald A. Neamen.  Electronic circuit analysis and design , 2001 .

[19]  Zhiying Wang,et al.  Robust AM-MIMO based on minimized transmission power , 2006, IEEE Communications Letters.

[20]  Joseph M. Kahn,et al.  Fading correlation and its effect on the capacity of multielement antenna systems , 2000, IEEE Trans. Commun..

[21]  Andrea J. Goldsmith,et al.  Energy-constrained modulation optimization for coded systems , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[22]  Lijun Qian,et al.  Energy Efficient Adaptive Modulation in Wireless Cognitive Radio Sensor Networks , 2007, 2007 IEEE International Conference on Communications.