Detection of Physics-Based Ultra-Wideband Signals ... - CiteSeerX

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Detection of Physics-Based Ultra-Wideband Signals Using Generalized RAKE With Multiuser Detection (MUD) and Time-Reversal Mirror Robert Caiming Qiu, Senior Member, IEEE, John Qiang Zhang, Student Member, IEEE, and Nan Guo, Member, IEEE

Abstract—This paper first introduces per-path pulse distortion in multiuser detection for ultra-wideband communications. A new generalized RAKE structure that estimates and compensates for the pulse distortion is used. An finite-impulse response filter representation of the per-path impulse response is used and estimated. The new structure greatly improves the system performance. With four users considered in our simulations in a high-rise building environment, it is found that the average performance of the generalized RAKE using minimum mean-square error (MMSE) detection is improved over the conventional RAKE by 1.8 dB. Both synchronous and asynchronous transmission schemes for decorrelating detector and MMSE detector are considered. Index Terms—Generalized RAKE, multiuser detection (MUD), physics-based, pulse distortion, time reversal, ultra-wideband (UWB).

I. INTRODUCTION

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ARLY research on ultra-wideband (UWB) communications was based on impulse radio. The channel model for the UWB system is unique due to the frequency dependency of path attenuation in the multipath channel. UWB communications has received enormous attention since 2002 [1]. Pulse distortion is a challenging problem in UWB communications [2]–[6]. This problem has become practically significant after the concept of frequency dependency was adopted in the IEEE 802.15.4a [7], a standard for low-rate (up to 1 Mb/s), moderate range (100–300 m) UWB applications such as sensor networks and ranging. In [7], two papers of the first author of this paper are cited for the first introduction of frequency dependency in the UWB channel model. When a short pulse propagates through a channel, multiple pulses are received via multipath. This is true for both narrowband and UWB. However, unlike narrowband systems, these pulses in general have pulse shapes different from the incident UWB short pulse. This phenomenon is called pulse waveform distortion in contrast to the amplitude and delay distortion. Pulse distortion can be caused by frequency dependency of the propManuscript received March 5, 2005; revised October 15, 2005. This work was supported in part by the Army Research Laboratory and the Army Research Office through a Short Term Innovative Research (STIR) Grant (W911NF-05-10468) and in part by a Defense University Research Instrumentation Program (DURIP) under Grant W911NF-05-1-0111. The authors are with the Department of Electrical and Computer Engineering, Center for Manufacturing Research, Tennessee Technological University, Cookeville, TN 38505 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/JSAC.2005.863813

agation channel and antennas. The per-path impulse response is introduced to describe pulse distortion for each individual path. The impact of pulse distortion on the baseband transmission has been investigated in the past [2]–[6]. It is found that pulse distortion can greatly degrade the system performance if no compensation is carried out. However, these papers are restricted to the single user only. In this paper, our previous framework will be extended to the multiuser case and multiuser detection (MUD) will be used. In general, the narrowband results cannot be directly used in the UWB analysis without reexamination of their validity. In addition, MUD for UWB in absence of pulse distortion has been considered in [12]. The results for MUD in the narrowband and UWB systems in the past are under the assumption of using the matched filter in the receiver front-end [10], [11]. In the single UWB user scenario [4]–[6], the receiver front-end may or may not be matched to the received distorted pulses at the receiver. The mismatched receiver front-end will degrade the system performance. As a result, it is natural to compensate for the pulse distortion to obtain better results. When pulse distortion is present for each received pulse in a multipath channel, a generalized RAKE structure is proposed where pulse distortion is considered in the channel estimation, which is very different from that of a conventional RAKE. When multipath is present and no pulse distortion is compensated for, a conventional RAKE receiver structure is reached. II. PHYSICS-BASED CHANNEL MODEL One big challenge of UWB is the per-path pulse distortion caused by the channel and antennas. Mathematically, the generalized channel model is expressed by [1]–[6] (1) where generalized paths are associated with amplitude , delay , and per-path impulse response . The represents an arbitrary function that has finite energy. Symbol “ ” denotes convolution and is the Dirac Delta function. Turin’s model that is widely used for narrowband channels and some UWB channels is a special case of (1) if , . A large category of UWB signals can be expressed as

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QIU et al.: DETECTION OF PHYSICS-BASED ULTRA-WIDEBAND SIGNALS USING GENERALIZED RAKE WITH MUD AND TRM

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. It is convenient to consider the transmission of a block be of some arbitrary length, say . The data block from the th user is (4) where is the transmitted energy of the th user for each bit. The transmitted waveform is (5) The composite transmitted signal for the

users is

Fig. 1. Pulse distortion in a high-rise building environment.

where assumes an arbitrary real value, and and are the Gamma function and the unit function, respectively. For , , this model results in the frea special case for quency dependency model recently accepted in IEEE 802.15.4a [7]. (Here, is a random variable varying between 0.8 and 1.4.) In other words, it is assumed that pulse distortion for all the paths are identical in the IEEE 802.15.4a channel model. In this paper, we will investigate a general case of (1) where pulse distortion for two received paths is different. Since the statistical model of such a form is currently not available, a physics-based channel model is adopted. As an example, the high-rise building environment (Fig. 1) that is widely studied for a narrowband system [8], [9] is investigated for a UWB system where the incident pulse, typical of a modern UWB system, is used. The detailed formulation and simulation are reported in [2]. The propagation environment illustrated in Fig. 1 can be represented by a channel model in a general form of (1). The in (1) causes many challenges in the signal processing for MUD. All the existing formulation for MUD is only valid for the conventional Turin’s model. We need to extend the current framework to deal with the channel model in (1) where the received pulse shapes are different from the incident pulse shape. We are also interested in how pulse distortion will affect the performance of MUD if no proper compensation is performed. III. OPTIMUM DETECTION OF PHYSICS-BASED SIGNALS Let us consider a direct-sequence code-division multiple access (DS-CDMA) UWB channel that is shared by simultaneous users. Each user is assigned a signature waveform of duration , where is the symbol interval. A transmitted signature waveform for the th user may be expressed as (3) where is a pseudonoise (PN) code sequence consisting of chips that take values , is a pulse of duration , the chip interval. Without loss of generality, it is assumed that signature waveforms have unit energy. For simplicity, it is further assumed that binary antipodal signals are used to transmit the information from each user. Consider a block of consecutive bits for each user in an observation window. Let the information sequence of the th user be denoted by , where the value of each information bit may

(6) where are the transmission delays, which satisfy the condifor . Without loss of generality, we tion . This is the model assume that for in an asynchronous mode. For synchronous mode, . We assume that the receiver knows . At the receiver end, the corresponding equivalent low-pass, received waveform may be expressed as (7) where is additive white Gaussian noise (AWGN), with . The received signal is power spectral density of (8) where

is the received signature waveform given by

(9) is the impulse response of the th user given in where (1). Denoted by , the pulse response of the front-end filter is used in forming . When , , (9) reduces to the conventional case [11] and the conventional RAKE is thus reached. In simulations, the estimated channel impulse response will be used to replace the . The channel estimation will be addressed in Section IV. With pulse distortion included in , we call our new receiver structure (in Fig. 2) the generalized RAKE structure. The optimum receiver is defined as the receiver that selects the most probable sequence of bits given the received signal observed over the time interval . The cross correlation between pairs of signature waveforms play an important role in the metrics for the signal detector and on the performance. The pulse distortion affects the system through the cross correlation (see also Section V). We define, where and

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Fig. 2.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 24, NO. 4, APRIL 2006

Generalized RAKE for multiuser detection. Estimated channel impulse response is used in forming the signature waveform g (t) for the k th user.

Similarly, we may define

result many (say ) discrete taps for each generalized path are obtained. Equation (1) is rewritten as (14) (11)

It is important to connect these cross-correlation functions through the channel impulse response via (9). As a result, it follows that:

where is the real amplitude of each tap corresponding to . With a mapping, the 2-D model is reduced to a one-dimensional (1-D) discrete model

(12) , (12) reduces to the familiar auto-corWhen relation form. Further, when and can be modeled by the Turin’s model, (7) reduces to , which is the conventional form in [11]. Optimum detection and suboptimum detection using decorrelator and minimum mean-square error (MMSE) detector have been considered in this paper for synchronous and asynchronous transmission. The detailed formulation can follow standard procedures, see, e.g., Proakis [11]. IV. CHANNEL ESTIMATION All the above algorithms require the knowledge of the channel parameters in order to detect the signal. The channel must be first estimated prior to the actual detection. One uses a data-aided (DA) approach [13], [14] where the data frame begins with a sequence of known data, so called pilot signal.

(15) where

The 1-D discrete model can be handled using conventional channel estimation algorithm that is used for a narrowband system. Thus, the FIR representation reduces the channel estimation for the generalized RAKE to that of the conventional RAKE. B. Optimal Maximum-Likelihood (ML) Channel Estimation Following steps of [14], the received signal can be expressed as

A. Two-Dimensional (2-D) Tap-Delayed Line Model The key of the generalized RAKE is to use an finite-impulse response (FIR) filter to represent the per-path impulse response in (1)

(16) where

(13) taps with tap spacing . The received This FIR filter has signal is sampled every seconds. Note a FIR representation of pulse distortion was used in channel modeling [15]. As a

is AWGN with two-sided spectral density and is the vector of the channel amplitude defined in (15). We assume that a frame consists of known pilot symbols .

QIU et al.: DETECTION OF PHYSICS-BASED ULTRA-WIDEBAND SIGNALS USING GENERALIZED RAKE WITH MUD AND TRM

The received channel signal is Gaussian with mean and covariance matrix that has terms of noise variance on its diagonal and zeros elsewhere. The optimal channel estimation is , where to maximize a function

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ceived pulse waveform for the th path is estimated as (20)

(17) where is a vector of channel path delays corresponding to amplitudes . The search for the optimum is complex and we will use a suboptimum algorithm in the following.

The matched filter for each user should be designed to match , instead of . In other words, the front-end filter impulse response is equal to , not . The generalized RAKE structure is obtained here.

C. Successive Channel (SC) Estimation The above optimal channel estimation can be used for a one-tap channel. The estimated delay and amplitude are (18) (19) where

V. COMPENSATION FOR PULSE SHAPE DISTORTION THROUGH TIME REVERSAL COMMUNICATIONS , In (7), the received signal at a receiver is where and is an AWGN noise. It is well known that the optimum receiver is the matched filter that [1]–[5]. The output of the optimum receiver is matched to , where is a new Gaussian is random variable. The correlations are expressed as (21)

Here, with duration is defined in (3). The above scheme can be performed iteratively for the multipath channel defined in (15). The algorithm is summarized by the following four steps in [14], originally in [13]: 1. Initialization: set and for ; 2. Perform the search for the strongest tap and calculate by using the above equations

3. If , go to step 2; otherwise set and stop. Using the above successive channel estimation algorithm, the channel impulse response is obtained. In the following, is replaced by the estimate for the th user, and the superscript can be dropped for convenience. With (13) and (14), the FIR representation of the per-path impulse response is estimated as . When the pulse waveform is transmitted, the estimated received signal is used in (9). For the th path, the pulse waveform is . Let us consider two cases. , and Case 1) If one tap is used in (13), thus . So the matched filter can be implemented with an impulse response of , the transmitted pulse waveform. This special case is just the conventional RAKE receiver used in narrowband and UWB scenario [10]–[14]. Case 2) If several taps (say three) are used in (13), then , and thus the re-

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(23) where , , and are symmetric with maximum at , which is the sampling point for the optimum receiver. For the commonly used second derivative Gaussian pulse , we have . For plots of for IEEE channel models, the reader is referred to [1]. The per-path impulse response in (1) affects the correlations through (21)–(23). Equations (22) and (23) also give the mathematical justification for time reversal communications [16]–[18]. Without loss of generality, we assure that has unit energy , which is the total energy in (1). At of the channel impulse response. At this point, all the energies of paths add coherently (focus at . However, this is not true for , as the terms in the second part of (23) would add destructively and form noise-like spikes. Through the use of , the optimum receiver compensates for per-path pulse waveform distortion, , while a conventional RAKE does not. For UWB communications, the realization of is difficult since the estimate of is difficult at the receiver. This has been illustrated in Section IV. One alternative approach is to realize using time-reversal mirror (TRM) [16], [17]. First, the receiver sends a short pulse to the transmitter. Second, the transmitter records and stores the received signal . Third, the transmitter realizes , time revered version

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of , and precodes the information bits with . Finally, at the receiver the recreated signal is . Time reversal is based on the spatially theorems in acoustics and electromagnetics. UWB radio experiments conducted using a time-domain approach verified that the reciprocal theorem is indeed valid for the indoor rich multipath environment [18]. Detailed results will be reported elsewhere. This measurement approach is reported in [17] and [18]. To achieve optimum performance using time reversal, an matched filter that is matched to should be used at the receiver. If multiple antennas are used at the transmitter and one antenna is used at the receiver, using the time reversal scheme the received signal is expressed , where is the channel as impulse response defined in (1) between the th antenna at the transmitter and the receiver antenna. The energies of all antennas can be focused the signals transmitted from the and the function of is made very sharp. This at temporal focusing greatly reduces the intersymbol interference (ISI) for high data rates. Moreover, the focused peak achieved grows linearly with [18]. This property will be at able to increase the antijamming capability of the system. If TRM is used in the transmitter, the new equivalent signal is with AWGN noise. The new channel is much easier to handle. It typically has a strong peak with weak sidelobes. The ISI will be greatly reduced through time reversal. The interuser interference will be reduced accordingly. The time reversal process can be viewed as spatial time precoding at the transmitter such that the signals will be temporally and spatially focused.

Fig. 3. The number of taps affects the accuracy of the FIR representation of the distorted pulse waveform.

VI. NUMERICAL RESULTS We take the high-rise building environment (Fig. 1) as an example to examine the channel model expressed in (1). The pulse width of the transmitted pulse is about 0.4 ns. To represent the pulse in high fidelity, in our simulation, we choose a sampling corresponding to a sampling frequency interval 80 GHz. A spreading code of length 8 is used in a DS/SS-based four-user system. The code is made from a Gold code of length 7 by attaching one chip to each code sequence. This code may not be the best but selection of spreading code is beyond the scope of this paper. For the selected propagation channel with sparse impulse response, to isolate pulse distortion impact, we use a special signaling format to avoid ISI: spreading code length in time is equal to 5 ns, one pulse per chip, and symbol duration 28.5 ns (i.e., data rate 35.1 Mb/s). Another key parameter for the SC algorithm is the threshold that affects estimation accuracy. In our simulation, we set the threshold to 30 dB down from the maximum amplitude in the algorithm. We use 512 pilot symbols in channel estimation, which guarantees an energy capturing loss less than 4% for pulse fitting with all terms (80 taps) at around 5 dB, and less than 1% in absence of noise. Illustrated in Fig. 3 is the impact of the number of terms , on the FIR representation of the first received pulses where is plotted in absence of noise. It is observed that the number of taps has visible impact on pulse representation. The convolution of the template with the FIR filter impulse response ((13)) yields the received signal .

Fig. 4. The impact of the number of taps in the FIR representation on the BER performance for a single user.

Shown in Fig. 4 is performance comparison for a single user case. No ISI occurs at a data rate of 35.1 Mb/s. The theoretical bounds in a closed-form [2] are plotted in the figure. The receiver is modeled as the receiver filter followed by a threshold detector. The matched-filter bound (lower bound) is obtained when the receiver filter matches the distorted received signals ( and ). The curve labeled “theoretical bit-error rate (BER) for conventional RAKE” is obtained in closed-form formula in [2], where the received filter matches the input pulse waveform . The curve of conventional RAKE (one tap representation) agrees very well with its theoretical curve and is 1.3 dB away from that of the matched-filter bound at 10 . The generalized RAKE with three taps representation is 1.1 dB better than the conventional RAKE. For MUD, four users are assigned the spreading ; code as following: user 1: user 2: ; user 3: ; and user 4: . For asynchronous transmission, the performance is averaged over

QIU et al.: DETECTION OF PHYSICS-BASED ULTRA-WIDEBAND SIGNALS USING GENERALIZED RAKE WITH MUD AND TRM

Fig. 5.

Performance comparison based on decorrelating detector for MUD.

ten random delays. The performance shown in Figs. 5 and 6 is for user one. Here, only one term (labeled “conventional decorrelator”) and three terms (labeled “G-decorrelator” meaning generalized RAKE) are considered to represent a single distorted waveform. The performance of decorrelating receiver is given in Fig. 5. The upper three curves are for synchronous transmission for user one. The circle dotted curve is obtained from the closed form and can serve as the lower bound for the performance of synchronous transmission with different number of taps. Here, only one tap (labeled “conventional decorrelator”) and three taps (labeled “G-decorrelator” meaning generalized RAKE) are considered to represent a single distorted pulse in simulations. The two curves immediately above the matched-filter bound are for asynchronous transmission. For synchronous transmission the generalized decorrelator (three taps) is 0.45 dB away from the theoretical lower bound and 1.3 dB better than the conventional decorrelator (one tap). For asynchronous transmission, the generalized decorrelator (three taps) achieves 1.7 dB gain over the conventional decorrelator (one tap). The conventional decorrelator detector (one tap) for synchronous transmission is about 4 dB in at from the decorrelator detector with the generalized RAKE (three taps) for asynchronous transmission. In Fig. 6, we compare the performance of MMSE detector for user one. The solid line is the matched-filter bound. The upper two curves are for synchronous transmission, while the two curves immediately above the matched-filter bound are for asynchronous transmission. For synchronous transmission, the generalized MMSE detector (three taps) performs 1.2 dB better than the conventional MMSE detector (one tap). For asynchronous transmission, the generalized MMSE detector gains 1.1 dB compared with the conventional MMSE detector. For MUD, the system performance also depends on the codes assigned to users. The performance improvement averaged over four users by using the generalized MMSE detector (three taps) is about 1.8 dB over the conventional MMSE detector (one tap). Based on our study, pulse distortion has larger impact on the performance of MUD (Figs. 5 and 6) than that of the single-user

Fig. 6.

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Performance comparison based on MMSE detector for MUD.

detection (Fig. 4). The conventional MMSE detector (one tap representation) for synchronous transmission is more than 3 dB at 10 from the MMSE detector with the in generalized RAKE (three taps representation) for asynchronous transmission. Based on Figs. 5 and 6, the MMSE detector with the generalized RAKE (three taps) is the best among all those schemes considered, and only 1 dB from the matched-filter bound at 10 . By using this scheme, about 0.5 dB can be further improved by using more taps (say 80 taps) in the FIR filter representation the pulse distortion. VII. CONCLUSION This paper for the first time introduces per-path pulse distortion in MUD for UWB communications. A new generalized RAKE structure that estimates and compensates for the pulse distortion is used. This structure is motivated to approach the optimum receiver that is matched to the composite channel impulse response. The optimum receiver can be also realized through time reversal communications to simplify the complex task of channel estimation at the receiver. When an FIR filter representation of the per-path impulse response is used for generalized RAKE, the new channel estimation problem has been reduced to a problem that can be handled by an existing signal processing algorithm such as successive channel estimation. The new generalized RAKE structure greatly improves the system performance. With simulations for a high-rising building environment, we show that the average performance of the MMSE receiver is improved over the conventional one by 1.8 dB in bit energy to noise ratio at 10 . Pulse distortion results in waveform mismatching, which is a common issue for various MUD schemes. Nonlinear MUD schemes usually outperform their linear counterparts and can be also used together with the generalized RAKE structure to strike tradeoff between performance and complexity. TRM can be used to compensate for pulse distortion and reduce ISI and interuser interference. The scheme of using a TRM combined with multiple-input–multiple-output (MIMO) antenna systems

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based on the impulse nature unique to UWB communications will prove to be useful to receivers of low complexity.

ACKNOWLEDGMENT The authors would like to thank C. (Jim) Zhou for his help. They also want to thank B. M. Sadler, A. Swami, R. Ulman, S. K. Das, and T. C. Yang for useful discussions.

REFERENCES [1] R. C. Qiu, H. P. Liu, and X. Shen, “Ultra-wideband for multiple access,” IEEE Commun. Mag., vol. 43, no. 2, pp. 80–87, Feb. 2005. [2] R. C. Qiu, C. M. Zhou, and Q. Liu, “Physics-based pulse distortion for ultra-wideband signals,” IEEE Trans. Veh. Technol., vol. 54, no. 5, pp. 1–10, Sep. 2005. [3] R. C. Qiu, “Pulse Propagation and Detection,” in UWB Wireless Communications, S. Shen, M. Guizani, R. Qiu, and T. Le-Ngoc, Eds: John Wiley, 2006. , “A generalized time domain multipath channel and its applica[4] tion in ultra-wideband (UWB) wireless optimal receiver design: Part III system performance analysis,” IEEE Trans. Wireless Commun., to be published. , “A generalized time domain multipath channel and its application [5] in ultra-wideband (UWB) wireless optimal receiver design: Part II wavebased system analysis,” IEEE Trans. Wireless Commun., vol. 3, no. 11, pp. 2312–2324, Nov. 2004. , “A study of the ultra-wideband wireless propagation channel and [6] optimum UWB receiver design, Part I,” IEEE J. Sel. Areas Commun., vol. 20, no. 9, pp. 1628–1637, Dec. 2002. [7] (2004) Status of models for UWB propagation channel. Channel Model Subcommittee. [Online]. Available: http://www.ieee802.org/ 15/pub/TG4a.html [8] W. Zhang, “Wideband propagation model based on UTD for cellular mobile radio communications,” IEEE Trans. Ant. Prop., vol. 45, no. 11, pp. 1669–1678, Nov. 1997. [9] H. Bertoni, Radio Propagation for Modern Wireless Systems, ser. NJ. Engelwood Cliffs: Prentice-Hall, 2000. [10] S. Verdu, Multiuser Detection. Cambridge, U.K.: Cambridge Univ. Press, 1998. [11] J. Proakis, Digital Communications, 4th ed. New York: McGraw-Hill, 2000. [12] Q. Li and L. A. Rusch, “Multiuser detection for DS-CDMA UWB in the home environment,” IEEE J. Sel. Areas Commun., vol. 20, no. 9, pp. 1701–1711, Dec. 2002. [13] A. A. D’Amico, U. Mengali, and M. Morelli, “Multipath channel estimation for the uplink of a DS-CDMA system,” in Proc. IEEE ICC, 2002, pp. 16–20. [14] M. Wessman, Ed., “Delivery D4.2 transceiver design and link level simulation results,” IST Ultrawaves Project, Rep. W-04-03-0025-R07, 2003. [15] R. M. Buehrer, A. Safaai-Jazi, W. Davis, and D. Sweeney, “Ultra-Wideband Propagation Measurements and Modeling,” Virginia Tech., Blacksburg, VA, DAPRA NETEX program, 2004. [16] N. Guo, R. C. Qiu, and B. M. Sadler, “An ultra-wideband autocorrelation demodulation scheme with low-complexity time reversal enhancement,” in Proc. IEEE MILCOM, Atlanta City, NJ, Oct. 17–20, 2005. [17] A. E. Akogun, R. C. Qiu, and N. Guo, “Demonstrating time reversal in ultra-wideband communications using time domain measurements,” in Proc. 51st Int. Instrumentation Symp., Knoxville, TN, May 8–12, 2005. [18] R. C. Qiu, C. Zhou, N. Guo, and J. Q. Zhang, “Time reversal with MISO for ultra-wideband communications: Experimental results,” IEEE Antennas and Wireless Propagat. Lett., 2006, to be published.

Robert Caiming Qiu (S’93–M’96–SM’01) received the Ph.D. degree in electrical engineering from Polytechnic University, Brooklyn, NY. He is currently an Associate Professor in the Department of Electrical and Computer Engineering, Center for Manufacturing Research, Tennessee Technological University, Cookeville. He was Founder-CEO and President of Wiscom Technologies, Inc., manufacturing and marketing WCDMA chipsets. All the assets of Wiscom were sold to Intel in 2003. Prior to Wiscom, he worked for GTE Labs, Inc. (now Verizon), Waltham, MA, and Bell Laboratories, Lucent Technologies, Whippany, NJ. He has worked in wireless communications, radio propagation, digital signal processing, EM scattering, composite absorbing materials, RF microelectronics, UWB, underwater acoustics, and fiber optics. He holds over 10 U.S. patents pending in WCDMA and authored over 40 technical papers and 4 book chapters. He contributed to 3GPP and IEEE standards bodies, and delivered invited seminars to institutions including Princeton University and the U.S. Army Research Lab. His current interest is in wireless communication and networking systems, in particular, ultra-wideband (UWB). Dr. Qiu serves as Associate Editor for the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, the International Journal of Sensor Networks (Inderscience), and Wireless Communication and Mobile Computing (NewYork: Wiley). He is a Guest Book Editor for Ultra-Wideband (UWB) Wireless Communications (NewYork: Wiley, 2006), and three Special Issues on UWB including the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY. He serves as a Member of TPC for GLOBE-COM, WCNC, and MILCOM. In addition, he served on the Advisory Board of the New Jersey Center for Wireless Telecommunications (NJCWT).

John Qiang Zhang (S’04) received the B.S. degree in electrical engineering from Beijing Jiaotong University, Beijing, China, in 1994, and the M.S. degree in mathematics from Tennessee Technological University, Cookeville, in 2004. He is working towards the Ph.D. degree in the Department of Electrical and Computer Engineering, Tennessee Technological University, where he works as a Research Assistant in the Wireless Networking Systems Laboratory. From August 1994 to March 2000, he was with Beijing Jiaotong University, as a R&D Engineer working on design and development of the embedded systems. His research interests include ultra-wideband transceiver design, multiuser detection, wireless sensor networks, embedded system and system integration.

Nan Guo (M’06) received the M.S. degree in telecommunication engineering from Beijing University of Posts and Telecommunications, Beijing, China, in 1990, and the Ph.D. degree in communications and electronic systems from the University of Electronic Science and Technology of China, Chengdu, China, in 1997. In January 1997, he joined Dr. L. B. Milstein’s research group at the Center for Wireless Communications, University of California, San Diego, participating in research projects funded by NSF, and the industry. From December 1999 to January 2002, he was with Golden Bridge Technology, Inc., West Long Branch, NJ, as a Research/System Engineer, where he was deeply involved in 3G CDMA system design, intellectual property development, and standardization activities. From June 2002 to February 2003, he was a Research Engineer at the system group, Ansoft Corporation, Elmwood Park, NJ, where his major responsibility was software development with emphasis on functionality modeling of emerging technologies. Since 2004, he has been with the Center for Manufacturing Research, Tennessee Technological University, Cookeville, doing R&D work in wireless communications. He has 15 years of industrial and academic experience in R&D, teaching, and lab work. His main research interests are in wireless communications physical layer and MAC layer. Currently, he focuses on ultra-wideband radio technology.