Multi-Input Multi-Output (MIMO) techniques can be used to increase the data rate for a given bit error rate (BER) and transmission power. Due to the small form factor, energy and processing constraints of wireless sensor nodes, a cooperative Virtual MIMO as opposed to True MIMO system architecture is considered more feasible for wireless sensor network (WSN) applications. Virtual MIMO with Vertical-Bell Labs Layered Space-Time (V-BLAST) multiplexing architecture has been recently established to enhance WSN performance. In this paper, we further investigate the impact of different modulation techniques, and analyze for the first time, the performance of a cooperative Virtual MIMO system based on V-BLAST architecture with multi-carrier modulation techniques. Through analytical models and simulations using real hardware and environment settings, both communication and processing energy consumptions, BER, spectral efficiency, and total time delay of multiple cooperative nodes each with single antenna are evaluated. The results show that cooperative Virtual-MIMO with Binary Phase Shift Keying-Wavelet based Orthogonal Frequency Division Multiplexing (BPSK-WOFDM) modulation is a promising solution for future high data-rate and energy-efficient WSNs.
References
[1]
Akyildiz, I.F.; Su, W.; Sankarasubramaniam, Y. Wireless sensor networks: A survey. Comp. Networks 2002, 38, 393–422.
Guo, H.; Low, K.-S.; Nguyen, H.-A. Optimizing the localization of a wireless sensor network in real time based on a low-cost microcontroller. IEEE Trans. Ind. Electron. 2011, 58, 741–749.
[4]
Gezer, C.; Niccoloni, M.; Buratti, C. An IEEE 802.15.4/ZigBee Based Wireless Sensor Network for Energy Efficient Buildings. Proceedings of the IEEE Conference on Wireless and Mobile Computing, Niagara Falls, ON, Canada, 11–13 October 2010; pp. 486–491.
[5]
Misra, S.; Reisslein, M.; Xue, G. A survey of multimedia streaming in wireless sensor networks. IEEE Commun. Surv. Tutor. 2008, 10, 18–39.
[6]
Akyildiz, I.F.; Melodia, T.; Chowdhury, K.R. Wireless multimedia sensor networks: Applications and testbeds. Proc. IEEE 2008, 96, 1588–1605.
[7]
Fa, R.; de Lamare, R.C. Multiple branch successive interference cancellation for MIMO spatial multiplexing system: Design, analysis and adaptive implementations. IET Commun. 2011, 5, 484–494.
[8]
Cui, S.; Goldsmith, A.J.; Bahai, A. Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks. IEEE J. Sel. Areas Commun. 2004, 22, 1089–1098.
[9]
Wang, H.; Xia, X.G. Asynchronous cooperative communication systems: A survey on signal design. Sci. China Inf. Sci. 2011, 54, 1547–1561.
[10]
Alamouti, S.M. A simple transmit diversity technique for wireless communications. IEEE J. Sel. Areas Commun. 1998, 16, 1451–1458.
[11]
Jiang, J.; Thompson, J.S.; Sun, H. A singular-value-based adaptive modulation and cooperation scheme for virtual-MIMO systems. IEEE Trans. Veh. Technol. 2011, 60, 2495–2504.
[12]
Jiang, J.; Thompson, J.S.; Sun, H.; Grant, P.M. Performance assessment of virtual multiple-input multiple-output systems with compress-and-forward cooperation. IET Commun. 2012, 6, 1456–1465.
[13]
Wolniansky, P.W.; Foschini, G.J.; Golden, G.D.; Valenzuela, R.A. V-BLAST: An Architecture for Realizing Very High Data Rates Over the Rich-Scattering Wireless Channel. Proceedings of the URSI International Symposium on Signals, Systems, and Electronics (ISSSE), Pisa, Italy, 2 October 1998.
[14]
Jayaweera, S.K. V-BLAST-based virtual MIMO for distributed wireless sensor networks. IEEE Trans. Commun. 2007, 55, 1867–1871.
[15]
Rafique, Z.; Seet, B.-C. Energy Efficient Wavelet Based OFDM for V-BLAST MIMO Wireless Sensor Networks. Proceedings of the IEEE Online Conference on Green Communication (GREENCOM), New York, NY, USA, 26–29 September 2011.
[16]
Yasir, M.; Mughal, M.J.; Gohar, N.D.; Moiz, S.A. Performance Comparison of Wavelet based OFDM (WOFDM) V-BLAST MIMO Systems with Different Detection Algorithms. Proceedings of the 4th IEEE International Conference on Emerging Technologies, Ralwapindi, Pakistan, 18–19 October 2008.
[17]
Leang, D.; Kalis, A. Smart Sensor DVB: Sensor Network Development Boards with Smart Antennas. Proceedings of the International Conference of Communications, Circuits and Systems, Chengdu, China, 27–29 June 2004; pp. 1476–1480.
[18]
Kounoudes, A.; Kalis, A.; Onoufriouand, T.; Constantinides, G. Wireless Sensor Technology for Continuous Health Monitoring of Structures. Proceedings of the Fifth International IABMAS Conference, Philadelphia, PA, USA, 11–15 July 2010.
[19]
Tsakalaki, E.P.; Alrabadi, O.N.; Kalis, A.; Papadias, C.B.; Prasad, R. Non cooperative space-time communication for energy efficiency in sensor networks. IEEE Trans. Wirel. Commun. 2012, 6, 48–54.
[20]
Psaltopulos, G.K.; Wittneben, A. Nonlinear MIMO: Affordable MIMO technology for wireless sensor networks. IEEE Trans. Wirel. Commun. 2010, 9, 824–832.
[21]
Andra, K.; Chakrabarti, C.; Acharya, T. A VLSI architecture for lifting-based forward and inverse wavelet transform. IEEE Trans. Signal Process. 2002, 4, 966–977.
[22]
Gupta, M.K.; Shrivastava, S.; Raghuvanshi, A.S.; Tiwari, S. Channel Estimation for Wavelet Based OFDM System. Proceedings of the International Conference on Devices and Communications, Mesra, India, 24–25 February 2011; pp. 1–4.
[23]
Lakshmanan, M.K.; Nikookar, H. A review of wavelets for digital wireless communication. Wirel. Pers. Commun. 2006, 37, 387–420.
[24]
Lindsey, R. Wavelet packet modulation for orthogonally multiplexed communication. IEEE Trans. Signal Process. 1997, 45, 1336–1339.
[25]
Gracias, S.; Reddy, V.U. An equalization algorithm for wavelet packet modulation. IEEE Trans. Signal Process. 1998, 46, 3082–3087.
[26]
Lee, H.; Jeon, H.; Jung, H.; Lee, H. A Novel Detection Algorithm Using the Sorted QR Decomposition Based on Log-Likelihood Ration inV-BLAST Systems. Proceedings of the International Conference on Wireless Communications, Networking, and Mobile Computing, Wuhan, China, 22–24 September 2006.
Chen, J.-C. Partial transmit sequences for peak-to-average power ratio reduction of OFDM signals with the cross-entropy method. IEEE Signal Process. Lett. 2009, 6, 545–548.
[29]
Rappaport, T.S. Wireless Communications, Principle and Practice, 2nd ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2001.
[30]
Gurun, S.; Krintz, C.; Wolski, R. NWSLite: A general-purpose nonparametric prediction utility for embedded systems. ACM Trans. Embed. Comput. Syst. 2008, 7, 1–36.
[31]
TelosB Datasheet. Available online: http://www.memsic.com (accessed on 23 December 2012).
[32]
Ahmad, J.J.; Khan, H.A.; Khayam, S.A. Energy Efficient Video Compression for Wireless Sensor Networks. Proceedings of the Annual Conference on Information Sciences & Systems, Baltimore, MD, USA, 18–20 March 2009.
[33]
Gupta, J.D.; Suzuki, H.; Ziri-Castro, K. Effect of pedestrian movement on MIMO-OFDM channel capacity in an indoor environment. IEEE Anten. Wirel. Propag. Lett. 2009, 8, 682–685.
[34]
Simon, S.; Aragon-Zavala, A. Antennas and Propagation for Wireless Communication Systems, 2nd ed.; John Wiley & Sons: Chichester, UK, 2007.
[35]
Zha, W.; Blostein, S. Modified Decorrelating Decision-Feedback Detection of BLAST Space-Time System. Proceedings of the IEEE International Conference on Communication, Helsinki, Finland, 11–14 June 2002.
[36]
Foschini, G.J.; Gans, M.J. On limits of wireless communication in a fading environment when using multiple antennas. Wirel. Pers. Commun. 1998, 6, 311–335.