Fundamentals of Space–Time Processing
In previous sections, we discussed paradigms of using several antennas to transmit or receive signals through wireless links. This is the principle underlying space–time processing where diversity is the fundamental idea. In Vucetic and Yuan , Paulraj and Papadias , and Paulraj et al. , the fundamentals of space–time processing are introduced with channel characteristics such as fading, path loss, delay spread, Doppler spread, and angle spread.
Discussion of multiple transmitters and receiverswas introduced in previous sections, where channel impulse responses were also presented along with the advantages of MIMO technologies.
The received signal in the set of m elements indicated in the MIMO case was explained, and itwas found that adding noise is the basis of the signal considered for space–time processing. The main problem is to determine the transmitted signal from observations of the received signal and the limited knowledge of the channel. Limitations in the knowledge of the channel are what the authors call structure. The objective is to characterize the spatial and temporal structure of the channel. The spatial structure contains information on array geometry, scattering, receiver gains, and so on, and the temporal structure consists of modulation format, pulse shaping, signal constellation, symbol rate, signal alphabet, and so forth.
In Paulraj and Papadias , an interference suppression approach is considered to apply space–time processing in order to receive the signal from a transmitter. Multiuser interference is treated as unknown additive noise. Two algorithms are introduced, basically the maximum likelihood sequence estimation (MLSE) and the minimum mean square error (MMSE). Performance of both schemes depends on co-channel interference and intersymbol interference together with channel characteristics.
The authors also argue that MLSE outperforms MMSE when perfect channel estimates are available, especially for the case when intersymbol interference (ISI) is significant. In contrast, when the channel behavior has significant co-channel interference, MMSE outperforms MLSE.
Application of this technique can also be seen in switched-beam systems, and providing diversity helps to improve cellular coverage and QoS, which in turn translates into better interference suppression algorithms that increase network capacity. In principle, due to the need for accurate channel parameter estimation, networks with infrastructure will be clearly deployed in the uplink. Also, space–time techniques are signal dependent;that is, the air interface of the technology used will determine performance of the algorithm applied, and such performance will not be the same if the technology is changed.
Printed with permission from Academic Press, a division of Elsevier. Copyright 2009. "Position Location Techniques and Applications" by David Munoz, Frantz Bouchereau Lara, Cesar Vargas & Rogerio Enriquez-Caldera. For more information about this title and other similar books, please visit www.elsevierdirect.com.
For more articles like this and others related to designing for the embedded Internet, visit Embedded Internet Designline and/or subscribe to the biweekly Embedded Internet newsletter (free registration).
 R. A. Berry, E. M. Yeh, Cross-layer wireless resource allocation, IEEE Signal Processing Magazine, September (2004) 59–68.
 D. Chizhik, G. J. Foschini, M. J. Gans, R. A. Valenzuela, Keyholes, correlations, and capacities of multielement transmit and receive antennas, IEEE Transactions on Wireless Communications, 1 (2) (2002) 361–368.
 S. De, C. Qiao, P. Pados, M. Chatterjee, S. Philip, An integrated cross-layer study of wireless CDMA sensor networks, IEEE Journal on Selected Areas in Communications, 22 (7) (2004) 1271–1285.
 A. del Coso, U. Spagnolini, C. Ibars, Cooperative distributed MIMO channels in wireless sensor networks, IEEE Journal on Selected Areas in Communications, 25 (2) (2007) 402–414.
 G. Dimic, N. D. Sidiropoulos, R. Zhang, Medium access control–Physical cross–layer Design, IEEE Signal Processing Magazine (2004) 40–50.
 Y-W. Hong, W-J. Huang, F-H. Chiu, C-C. J. Kuo, Cooperative communications in resource-constrained wireless networks, IEEE Signal Processing Magazine, 24 (3) (2007) 47–57.
 A. J. Paulraj, C. B. Papadias, Space-time processing for wireless communications, IEEE Signal Processing Magazine, 14 (6) (1997) 49–83.
 A. J. Paulraj, R. Nabar, D. Gore, Introduction to Space-TimeWireless Communications, Cambridge University Press, 2003.
 A. Scaglione, D. Goeckel, N. Laneman, Cooperative communications in mobile ad hoc networks, IEEE Signal Processing Magazine, 23 (6) (2006) 18–29.
 V. Srinivasan, P. Nuggehalli, C. F. Chiaserini, R. R. Rao, An analytical approach to the study of cooperation in wireless ad hoc networks, IEEE Transactions onWireless Communications, 4 (2) (2005) 722–733.
 M. Van Der Schaar, S. Shankar, Cross-layer wireless multimedia transmission: challenges, principles, and new paradigms, IEEE Wireless Communications, 12 (4) (2005) 50–58.
 B. Vucetic, J. Yuan, Space-Time Coding, JohnWiley & Sons, 2003.