CHAPTER 23 EMERGING WIRELESS COMMUNICATION TECHNOLOGIES1
GHAïS EL ZEIN AND ALI KHALEGHI Member, IEEE Abstract:
1.
This paper describes some latest development in the area of wireless communication technologies. At first, we give an introduction on the current wireless communication systems. Then, we discuss the characterization and the modeling of the propagation channel which are particularly critical in the design of the recent technologies as MIMO, UWB and time reversal. Thereafter, these techniques are presented and some results concerning the possible integration in future wireless systems are discussed
INTRODUCTION
Today, third generation (3G) mobile communications systems allow the integration of new services such as multimedia, packet switching and wideband radio access. In the same time, Wireless Local Area Networks (WLAN) equipments are penetrating the market. These new systems make possible the connectivity with IP networks. In this context, emerging technologies such as Multiple-Input Multiple-Output (MIMO) and Ultra-WideBand (UWB) are recognized as good solutions in the development of the forthcoming generation of broadband wireless networks. These techniques are very attractive for digital communications to increase data rates and/or to improve system performance. The purpose of this invited paper is to highlight different aspects concerning these new technologies. In section 2 a brief summary of current wireless technologies is
1
This work was supported in part by the CPER PALMYRE project with the financial support of Région Bretagne and the MIRTEC project with the financial support of ANR. The authors are with the National Institute of Electronics and Telecommunications, IETR – UMR CNRS 6164 – INSA, 35043 Rennes Cedex, France (phone: (+33) (0)2 23 23 86 04; fax: (+33) (0)2 23 23 84 39; e-mail:
[email protected],
[email protected])
271 H. Labiod and M. Badra (eds.), New Technologies, Mobility and Security, 271–279. © 2007 Springer.
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given. Section 3 deals with the characterization and the modeling of the propagation channel, which are particularly important in the design of these new systems. Sections 4, 5 and 6 introduce respectively the principles and the features of MIMO, UWB and time reversal (TR) techniques. Some research results concerning the possible integration of these new technologies in the future wireless systems are also presented.
2.
CURRENT WIRELESS TECHNOLOGIES
After the great success of 2G cellular service and the tremendous growth of the Internet, multimedia is now penetrating the mass market. Since, wireless access to the worldwide wired-line infrastructure is becoming an essential feature of modern communication networks. The first realizations of these capabilities are the 3G mobile systems. In the same way, WLAN equipments are supported by the two most prominent standards IEEE 802.11/WiFi and HIPERLAN, and allow connectivity in buildings for portable computers. However, current wireless access networks show limits in terms of data rate and quality of service (QoS). For several years, efforts have been made to improve the design of the existing systems. Fig. 1 shows the limits in the data rate of the actual communication systems that depends on the system mobility. The trend of the recent communication is to increase the data rate and to deliver better services for mobile systems (4G and 5G). In fact, the trend to increase data rates will most probably continue to reach 100 Mbps considering a moderate mobility, and up to 1 Gbps for a reduced mobility. In this context, MIMO and UWB technologies appear as new concepts to fulfill those specifications. In addition, these systems can be combined with TR technique to improve their performance.
Figure 1. Current wireless technologies and trends
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WIRELESS CHANNEL CHARACTERIZATION AND MODELING
The radio propagation of electromagnetic waves from a transmitter to a receiver is characterized by the presence of multipath due to various phenomena such as reflection, refraction, scattering and diffraction. The study of these propagation phenomena appears as an important task when developing a wireless system [1], [2]. For broadband systems, the analysis of both path loss and impulse response is required. The analysis is usually made in the time domain, which allows to measure the coherence bandwidth, the coherence time, the respective delay spread, and Doppler spread values. Also, coherence distance, correlation distance, and wave direction spread are used to highlight the link between propagation and system in the space domain. Therefore, an accurate description of the spatial and temporal properties of the channel is required for the design of broadband/multiantenna systems, and also for the choice of the network topology. In this context, the characterization and the space-time modeling of the channel appear essential. Several methods of classification of the models are proposed in the literature [3]–[9]. Deterministic models are based on a fine description of a specific environment, when the stochastic models aim to describe the channel parameters by random laws. In practice, different sounding techniques can be used in order to characterize the propagation channel. 4.
MIMO WIRELESS COMMUNICATION SYSTEMS
The MIMO (Multiple-Input Multiple-Output) principle can be defined simply. Since time and frequency domains processing are pushed to their limits, the space domain can be exploited. The main idea is to transmit multiple streams of data on multiple antennas at the same frequency. Usually, multiple receiving antennas are considered to improve the system performance (Fig. 2). In an ideal case, it can be shown that the channel capacity grows linearly with the number of transmitting (Tx) and receiving (Rx) antennas [10]. This technique can be viewed as a generalization of space diversity and smart antennas [11]. It supposes a channel rich in multiple paths in order to exploit independent transmission channels between the Tx and Rx antennas. This transmitting and receiving structure can be modeled using a matrix representation of the channel.
TX
Figure 2. MIMO wireless transmission system
RX
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Information theoretic considerations allow to highlight that two fundamental mechanisms are at stake in the process of transferring information: diversity (reception of multiple de-correlated copies of the same transmitted information, by combining, allow to combat channel fading), and multiplexing (reception of multiple independent symbols of information to increase the channel capacity) [12]. The structure of the propagation channel places a tradeoff between the two types of gain. The really large improvement in link reliability and/or data rates, and predicted by information theory relies on a fine knowledge of the propagation phenomena. Such knowledge makes it possible to choose the most appropriate coding/modulation scheme for a given environment. Transmitting and receiving antenna arrays have to be carefully designed to maximize the channel rank, ie the number of eigenmodes available for communication. In this case, correlation and dispersion measurements of channel parameters play a central role. For our measurements, a wideband channel sounder developed in our laboratory has been used to characterize the double directional channel [13]. It operates at 2.2 GHz. It uses a periodic transmit signal based on the direct sequence spread spectrum technique. This sounder offers a temporal resolution of about 11.9 ns. In order to estimate the propagation parameters of channel multipath components, we used the Unitary ESPRIT algorithm [14]. A dense urban environment was chosen for the first campaigns of the sounder test. The statistical analysis permits us to extract the second order statistical properties of the propagation channel and to provide important parameters for system design such as the coherence bandwidth, the coherence distances at the transmission and reception sites. Table 1 presents the average (m) and dispersion () of the coherence bandwidth (in MHz) for two correlation levels (50% and 90%) in different measurement sites. Table 2 presents the coherence distances (in ) obtained at the transmission and the reception sides. These various coherence parameters have a profound implication in the design of MIMO communication systems. In fact, the two main performance indicators relevant to power-limited and band-limited applications, ie probability of error and data rates, both depend on the second-order behavior of the communication channel. As an example, we can observe that antenna spacing, within the transmitting and
Table 1. Coherence bandwidth results Site
Coherence Bandwidth (MHz) 50%
Victoire Station JMF
90%
m
m
377 328 247
283 27 186
133 9 46
184 139 69
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EMERGING WIRELESS COMMUNICATION TECHNOLOGIES Table 2. Coherence distance results Site
Coh. Dist. Rx ( 50%
Victoire Station JMF
Coh. Dist. Tx ( 90%
50%
90%
m
m
m
m
973 57 634
603 43 521
18 07 1
18 09 16
023 022 029
010 012 012
006 005 007
003 003 003
receiving arrays, must be larger than the local correlation distance to get sufficient transmit and receive decorrelation. Likewise, the transmit bandwidth must be larger than the channel coherence bandwidth to gain frequency diversity. The first campaigns show that for a UMTS system with a 5 MHz bandwidth (Table 1), the channel offers little frequency paths diversity (in less than 10% of the cases) [15]. Moreover, space diversity has been obtained at the Rx base station by separating the antennas at distances close to 11 (1.5 m at 2.2 GHz) (Table 2). Also, the diversity at the Tx mobile position has been evaluated by spacing out the antennas at the distance 036 (4.9 cm at 2.2 GHz).
5.
UWB WIRELESS COMMUNICATION SYSTEMS
Another solution under consideration is the UWB technology, which relies on transmission of series of very short pulses (< 1 ns). This technology appears as an alternative air interface for the deployment of WLAN and WPAN (Wireless Personal Area Networks) that link portable and fixed equipments [16]. It promises benefits such as high location accuracy, robustness to multipath propagation, high-data rate and low-power wireless communications. The major principle of UWB communication systems consists in the transmission of impulse radio signals, as defined by the Federal Communications Commission (FCC), whose fractional bandwidth (3 dB bandwidth divided by the center frequency) is greater than 25%, or has a bandwidth larger than 500 MHz. Thus, the FCC has permitted UWB devices to operate using a spectrum ([3.1; 10.6] GHz) occupied by existing radio services, as long as emission levels meet the proposed spectral mask. The applications of UWB systems aim indoor home and professional environments, providing short range high-speed communications, precision location and tracking, wall and ground penetrating radar and medical imaging. In this context, the understanding of physical phenomena involved in the propagation of an impulse signal appears necessary for the modeling of the propagation channel associated with a specific environment [17]. Due to the frequency selectivity and the different time delays of multipath arrivals, the wide-band nature of the radio signal further complicates the channel modeling [18].
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x 10
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P D P
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90
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Figure 3. Instantaneous PDP of indoor UWB channel
Fig. 3 shows a sample of the power delay profile (PDP) of a NLOS (non-lineof-sight) UWB channel that is measured in an indoor environment. Due to the large number of taps at the receiver, the detection of the UWB signal is the most complicated part of the system. Traditional rake receivers can be used for signal detection. However, by considering IEEE 802.15 UWB channel models, the number of rake fingers that should be considered is in the order of 22-123 for LOS and NLOS channels. Therefore, the complexity of the receiver system is increased. To resolve this problem, the time reversal technique is proposed that moves the system complexity to the transmitter part of the communication link, which is ideal for some applications. Extremely simple non-coherent receivers can be used for low-cost and low-power sensors
6.
TIME–REVERSAL WIRELESS COMMUNICATIONS
Time reversal is already known in acoustical imaging, electromagnetic imaging, underwater acoustic communication and radar domain [19–21]. Recently, some industries invested for the development of time-reversal for wireless communication. In this technique, the channel response between transmitter and receiver is measured then the time reversed version of the channel impulse response is used as a pre-filter for data communication. Mathematically, it can be shown that the convolution product of the time reversed waveform of the channel and the channel response gives the signal auto-correlation i.e. (1)
sr = hr0 − ∗ hr
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-4
1.2
x 10
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x 10 1
1 0.8
PDP of TR-channel
0.6
0.8 0.4
0.2
0.6 0 164
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0 80
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Figure 4. PDP of equivalent TR channel response
where * denotes the convolution operation. If the environment is rich in multipath, the TR will focalize spatially the received signal energy and compressed it temporally. Fig. 4 shows the measured PDP of the equivalent TR channel given in Fig. 3. The channel response is compressed in time. Thus, the complex task of estimating a large number of taps at the receiver is greatly reduced. This implies low cost receivers with a much less need for equalization. Furthermore, due to the considerable focusing gain, better signal to noise ratio or equally higher data rate can be achieved. Spatial focusing reduces the co-channel interference in multi-cell systems. Furthermore, the TR technique can be combined with UWB and MIMO systems to increase their performance. In UWB, it would be possible to increase the communication range by respecting FCC transmitter spectral mask limit. In MIMO, the communication can be conducted between transmitter and the intended receiver without disturbing the other receivers or users. This means that the channel rank will be maximized. 7.
CONCLUSION
This paper focuses on the novel technologies in wireless communication MIMO, UWB and time-reversal. Aspects of wave propagation have been addressed. A fine knowledge of the propagation phenomena makes it possible to choose the most appropriate coding/modulation scheme for a given environment. In fact, considering different practical situations, the extracted spatio-temporal channel parameters can be used to highlight the connection between propagation and communication system. Thus, a great measurement number will be necessary to obtain significant statistical results that can give realistic MIMO and/or UWB channel models. Furthermore,
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the time reversal (TR) technique appears as an attractive solution that moves the system complexity to the transmitter part of the communication link, which is ideal for some applications. The TR technique can be combined with UWB and MIMO systems to increase their performance. Moreover, simple non-coherent receivers can be used for low-cost and low-power wireless communication systems.
8.
ACKNOWLEDGMENT
The authors would like to thank Hanna Farhat, Ronan Cosquer, Julien Guillet, Florence Sagnard, Ijaz Haider Naqvi and Guy Grunfelder for their technical contributions.
REFERENCES 1. F. Molisch, Wireless Communications, John Wiley & Sons Ltd, England, 2005. 2. P. A. Bello, “Characterization of randomly time-variant linear channels,” IEEE Transaction on Communications, pp. 360–393, December 1963. 3. K. Yu, and B. Ottersten, “Models for MIMO propagation channels, a review,” Special Issue on "Adaptive Antennas and MIMO Systems", Wiley Journal on Wireless Communications and Mobile Computing, vol. 2, no. 7, pp. 653–666, November 2002. 4. M. A. Jensen, and J. W. Wallace, “A review of antennas and propagation for MIMO wireless communications,” IEEE Transactions on Antennas and Propagation, vol. AP-52, n 11, pp. 2810–2824, November 2004. 5. L. Schumacher, L. T. Berger, and J. Ramiro-Moreno, “Recent advances in propagation characterisation and multiple antenna processing in the 3GPP framework,” in Proceedings of XXVIth URSI General Assembly, Maastricht, The Netherlands, August 02. 6. S. Salous, “Multiple input multiple output systems: capacity and channel measurements,” in Proceedings of the Seventh World Multiconference on Systemics, Cybernetics and Informatics (SCI2003), Florida, USA, pp. 1–5, 27–30 July 2003. 7. M. Steinbauer, A. F. Molisch, and E. Bonek, “The Double-Directional Radio Channel,” IEEE Antennas & Propagation Magazine, vol. 43, no. 4, pp. 51–63, 2001. 8. J. Guillet, “Caractérisation et modélisation spatio-temporelles du canal de propagation radioélectrique dans le contexte MIMO,” Ph.D. Thesis, INSA Rennes, July 2004. 9. G. El Zein, R. Cosquer, J. Guillet, H. Farhat, and F. Sagnard, “Characterization and Modeling of the MIMO Propagation Channel: An Overview,” in Proc. of European Microwave Week EUMW 2005, Paris, October 3–7, 2005. 10. G. J. Foschini, and M. J. Gans, “On limits of wireless communications in a fading environment when using multiple antennas,” IEEE Wireless Personal Communications, vol. 6, no. 3, pp. 311–335, March 1998. 11. D. Gesbert, M. Shafi, D. Shiu, P. J. Smith, and A. Naguib, “From theory to practice: An overview of MIMO space-time coded wireless systems,” IEEE Journal on Selected Areas in Communications, vol. 21, no. 3, pp. 281–301, April 2003. 12. P. Guguen and G. El Zein, Les Techniques Multi-Antennes pour les Réseaux sans Fil, Edition Hermes, Paris, 2004. 13. R. Cosquer, “Conception d’un sondeur de canal MIMO. Caractérisation du canal de propagation d’un point de vue directionnel et doublement directionnel,” Ph.D. Thesis, INSA Rennes, October 2004. 14. M. Haardt, “Efficient one-, two and multidimensional high resolution array signal processing,” Ph.D. Thesis, TU-Munchen, 1996.
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15. H. Farhat, R. Cosquer, J. Guillet, and G. El Zein, “Mobile radio channels characterization based on wideband SIMO 1 × 8 measurements at 2.2 GHz,” in Proceedings of the 5th International Conference on ITS Telecommunications (ITST ’05), Brest, June 27–29, 2005. 16. D. Porcino, and W. Hirt, “Ultra-Wideband Radio Technology: Potential and Challenges Ahead,” IEEE Commun. Mag., pp. 66–74, Jul. 2003. 17. F. Tchoffo-Talom, B. Uguen, and E. Plouhinec, “A site-specific tool for UWB channel modeling,” in Proc. IEEE IUWBST Conf., Kyoto, Japan, May 18–21, 2004. 18. F. Sagnard, and G. El Zein, “In-Situ Characterization of Building Materials for Propagation Modelling: Frequency and Time Responses,” IEEE Trans. on Antennas and Propagation, vol. 53, No. 10, Oct. 2005, pp. 3166–3173. 19. A. Derode, P. Roux, and M. Fink, “Robost acoustic time reversal with high order multiple scattering,” Phys. Rev. Letters, vol. 75, pp. 4206–4209, 1995. 20. P. Kyritsi, G. Papanicolau, P. Eggers, and A. Oprea, “MISO Time Reversal and Delay-Spread Compression for Fwa Channels at 5 GHz,” IEEE Antennas and Wireless Propagation Letters, vol. 3, pp. 96–99, 2004. 21. P. Kyritsi, G. Papanicolau, P. Eggers, and A. Oprea, “Time Reversal Techniques for Wireless Communications,” Proceedings of the 60th Vehicular Technology Conference, 2004.
