PERSONAL communication systems (PCS s) make communication

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 46, NO. 3, AUGUST 1997 625 A Novel FSK Demodulation Method Using Short-Time DFT Analysis for LEO Satellite Communication Systems Shinsuke Hara, Member, IEEE, Attapol Wannasarnmaytha, Student Member, IEEE, Yuuji Tsuchida, and Norihiko Morinaga, Senior Member, IEEE Abstract This paper proposes a novel frequency-shift keying (FSK) demodulation method using the short-time discrete Fourier transform (ST-DFT) analysis for low-earth-orbit (LEO) satellite communication systems. The ST-DFT-based FSK demodulation method is simple and robust to a large and time-variant frequency offset because it expands the received signal in a time-frequency plane and demodulates it only by searching the instantaneous spectral peaks with no complicated carrier-recovery circuit. Two kinds of demodulation strategies are proposed: a bit-by-bit demodulation algorithm and an efficient demodulation-algorithm frequency-sequence estimation (FSE) based on the Viterbi algorithm. In addition, in order to carry out an accurate ST-DFT window synchronization, a simple DFT-based ST-DFT windowsynchronization method is proposed. Index Terms Discrete Fourier transforms, Doppler effect, frequency-shift keying, satellite communication. I. INTRODUCTION PERSONAL communication systems (PCS s) make communication from person-to-person, with a wide range of services such as voice and data transmission with different service qualities, whenever they are required, regardless of where we locate [1]. Low-earth-orbit (LEO) satellite systems have the advantages of the interoperability of terrestrial cellular and mobile systems as well as shorter transmission delay and lower propagation path loss as compared with geostationary-earth-orbit (GEO) satellite systems. The LEO satellite network is a candidate to provide such truly seamless global personal communications services because it has all the coverage, capacity, and features required for the PCS realization. However, the system suffers from the Doppler frequency offset. In the LEO satellite system, there exists a large and timevariant frequency shift due to the Doppler effect, depending on the carrier frequency, satellite altitude, orbit, and coverage assigned to each LEO satellite. Fig. 1 shows the Doppler shift and rate versus the time, where the earth station is located Manuscript received December 15, 1995; revised August 1, 1996. S. Hara is with the Department of Electronic, Information, and Energy Engineering, Graduate School of Engineering, Osaka University, Osaka, Japan (e-mail: hara@comm.eng.osaka-u.ac.jp). A. Wannasarnmaytha and N. Morinaga are with the Department of Communication Engineering, Graduate School of Engineering, Osaka University, Osaka, Japan. Y. Tsuchida is with the Audio Laboratory, Sony Corporation, Tokyo, Japan. Publisher Item Identifier S 0018-9545(97)04630-6. Fig. 1. Doppler shift and its rate. The earth station is located on the crosspoint of the equator and footprint of the satellite coverage of a polar circular orbit with a satellite altitude =788 km and a carrier frequency =2GHz. on the crosspoint of the equator and footprint of the satellite coverage in a polar circular orbit with a satellite altitude of 788 km and a carrier frequency of 2 GHz [2]. In this case, the Doppler shift ranges from 40 to 40 khz and the Doppler rate from 0 to 5.5 khz/s. For a symbol transmission rate of 8.0 kb/s as a low-rate service, for instance, the required bandwidth of the receiver front-end bandpass filter becomes approximately five times as large as the symbol rate. Therefore, it is essential to develop modulation or demodulation schemes to cope with such a large and time-variant frequency offset. Also, in the PCS, associated with the miniaturization of personal terminals, the problem of frequency offset is caused by the frequency instability of the terminal local oscillator. An efficient automatic frequency control (AFC) loop might be one of the solutions [3], [4]. However, there exists a fundamental time-frequency tradeoff: improving the frequency resolution results in a loss of time resolution and vice versa [5]. In other words, an accurate frequency estimation requires a long preamble and inevitably introduces a loss of transmitted power efficiency. Much effort has been devoted to the analysis of the AFC tracking performance in the presence of frequency offset and to the proposal of modulation/demodulation schemes robust to the large and fast frequency offset. For instance, the tracking performance of the crossproduct AFC in the Costas loop is discussed in [6]. A double-pilot-assisted QPSK coherent demodulation method and a Doppler-corrected differential detection method of MPSK are proposed in [7] and [8], 0018 9545/97$10.00 1997 IEEE

626 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 46, NO. 3, AUGUST 1997 where is the time and is the signal-burst length in time. We call and the (initial) fixed frequency offset and frequency-offset rate, respectively. Taking this first-order approximation, we can evaluate the robustness of the proposed demodulation method against the frequency offset only for and. Fig. 2. Fig. 3. LEO satellite channel model. Transmitter model. respectively, both of which can cope with time-variant frequency offset. A dual-channel PSK demodulator for LEO satellite DS/CDMA communications is proposed in [9], which is absolutely insensitive to time-variant Doppler frequency offset. Also, a simple coarse frequency acquisition method through fast Fourier transform (FFT) is proposed in [10]. This paper proposes a novel frequency-shift keying (FSK) demodulation method using the short-time discrete Fourier transform (ST-DFT) analysis for an LEO satellite communication channel with a large and time-variant frequency offset [11]. The ST-DFT-based FSK demodulation method expands the received signal in a time-frequency plane based on the ST-DFT analysis and demodulates it by searching the instantaneous spectral peaks with no complicated carrierrecovery circuit. Two kinds of demodulation strategies are proposed: a bit-by-bit demodulation algorithm and a novel efficient demodulation-algorithm frequency-sequence estimation (FSE) based on the Viterbi algorithm. In addition, in order to carry out an accurate ST-DFT window synchronization, a simple DFT-based ST-DFT window-synchronization method is proposed. Sections II and III deal with the channel model and transmitter/receiver model, respectively. Sections IV and V explain the ST-DFT-based demodulation principle and algorithms, respectively. Section VI explains the DFT-based ST-DFT window-synchronization method. Section VII shows the computer simulation results on the bit error probability (BEP). Finally, Section VIII draws the conclusions. II. CHANNEL MODEL We model an LEO satellite communication channel as an additive white Gaussian Noise (AWGN) channel with frequency offset (see Fig. 2). In a burst mode transmission, where the signal-burst length is small, considering the frequency variation up to the first-time derivative, we can approximate the frequency offset introduced in a signal burst as (1) III. TRANSMITTER AND RECEIVER MODELS A. Transmitter Model Fig. 3 shows the block diagram of a binary differentially encoded FSK (BDEFSK) transmitter. The information data stream ( or ) is differentially encoded, passed through the Nyquist filter with rolloff factor, and then modulated by the FM modulator with modulation index. The transmitted signal with unit amplitude is written by where and represent the real part of and the center frequency, respectively. is the modulated phase given by where is the symbol duration and ( or )is the differentially encoded th symbol The impulse response of the Nyquist filter is given by In the BDEFSK scheme, the information 1 is transmitted by shifting the carrier frequency relative to the previous carrier frequency and information 1 by keeping the same carrier frequency. We define and as the higher and lower transmitted frequencies at sampling instant, respectively, and as the frequency separation (2) (3) (4) (5) (6) (7) (8) B. Receiver Model Figs. 4 and 5 show the block diagram of the ST-DFT-based differential frequency receiver and the instantaneous energy distribution of the received signal, respectively. The received signal through the LEO satellite channel mentioned in Section II is written as where is the amplitude of the received signal and assumed to be constant and is the complex AWGN. is passed through the receiver front-end bandpass filter (BPF) with bandwidth Hz centered at the nominal center frequency (9)

HARA et al.: NOVEL FSK DEMODULATION METHOD FOR SATELLITE COMMUNICATION SYSTEMS 627 Fig. 4. Receiver model. Hz and then downconverted by. After analog-to-digital (A/D) conversion with sampling rate Hz, the output signal is expanded into a time-frequency plane by the ST-DFT in order to analyze the instantaneous energy distribution. Finally, the demodulation is made based on the spectral analysis result. Defining and as the maximum frequency offset introduced in the channel and bandwidth of the transmitted signal (see Figs. 1 and 5) in order to introduce no distortion in the received signal, the bandwidth of BPF must satisfy the following condition: (10) Also, in order to introduce no aliasing distortion in the A/D conversion, the sampling rate must satisfy the following condition: IV. ST-DFT-BASED DEMODULATION PRINCIPLE (11) A. ST-DFT The time-frequency representation of a signal based on the ST-DFT, which is often called Spectrogram, is given by [12] (12) (13) where is the sampling interval and and represent a finite-time and even-symmetrical window function and a number of samples in one window, respectively. Equation (13) represents the spectral component of at the th time index and th frequency index. We define as a point (node) at and on the time-frequency plane. Furthermore, we define as the instantaneous energy spectrum of at (14) B. Basic Demodulation Principle Fig. 5 shows the basic principle of the ST-DFT-based demodulation method. After the ST-DFT window synchronization is established, the differential frequency demodulation is made by searching the instantaneous spectral peak of at. Analysis of the Fig. 5. Instantaneous energy distribution of received FSK signal and basic demodulation principle. received signal with the ST-DFT is all the same as observation through a filter bank with a number of narrowband filters. Therefore, the demodulation performance depends not on the front-end BPF output signal-to-noise power ratio (SNR), but on the narrowband BPF output SNR. Consequently, in principle, however wide the front-end BPF may be made, it introduces no difference in the demodulation performance. In other words, the ST-DFT-based demodulation method is insensitive to the SNR degradation caused by the excessively wide bandwidth of front-end BPF. Also, when there are frequency-division multiplexed channels in the received frequency band because of the wide front-end BPF, the receiver could find distinct peaks in the instantaneous energy spectrum at every demodulation instance. When a signal burst is transmitted with a specific preamble (unique word) in each channel, the receiver can easily identify the desired channel and carry out demodulation, focusing attention only on the desired part of the received frequency band. Therefore, the ST-DFT-based demodulation method can mask the false spectral peaks in adjacent channels. C. Maximum-Likelihood Estimation (MLE) Characteristic The transmitted signal (when an unknown frequency offset is introduced in the channel) can be considered to be a monotone with an unknown (discrete) frequency. Assuming that the frequency of the received signal does not change in one DFT window, the monotone composed of - time samples in one window is written in a vector form as (15) (16) where is an unknown phase. Defining as the received signal vector composed of -time samples in one window at, is written as (17) where is a noise vector and each component is Gaussian distributed. Therefore, the joint

628 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 46, NO. 3, AUGUST 1997 probability density function (pdf) for conditioned on the monotone signal can be written as [13] (18) where is the power of. Averaging (18) by, the joint pdf for conditioned on the frequency is written as Fig. 6. Bit-by-bit demodulation algorithm. where and are the th peak frequency for and the decided frequency for, respectively. 5) Make a decision according to (19) where is the zeroth-order modified Bessel function of the first kind. Equation (19) shows that although we assume a rectangular window, the frequency, which maximizes, is the MLE of the frequency of the transmitted signal. Therefore, the ST-DFT-based demodulation method is optimum and can minimize the BEP when the frequency-offset rate is negligibly small. However, the following bit-bybit demodulation algorithm and frequency-sequence estimation (FSE) algorithm are suboptimal because the demodulation principle is modified in order to track the frequency drift due to the larger frequency-offset rate. V. ST-DFT-BASED DEMODULATION ALGORITHMS In order to analyze the instantaneous energy distribution of the received signal accurately in the ST-DFT-based demodulation method, the interpolation technique with points is employed. The frequency resolution is given by (20) A. Bit-by-Bit Demodulation Algorithm The bit-by-bit demodulation is made according to the following algorithm (see Fig. 6). 1) Let. 2) Calculate for the th symbol. 3) Let. 4) Calculate given by (21) (22) 6) If does not satisfy the condition in Step 5), then let and go to Step 4). 7) Set to be, then let and go to Step 2). The decision criterion in Step 5) ensures the prevention of the misdetection of the false spectral peak due to background noise and the tracking of the frequency drift due to the frequency-offset rate. B. FSE Algorithm We propose a novel demodulation algorithm FSE to improve the demodulation performance for a large and time-variant frequency offset. The FSE is a kind of Viterbi algorithm [14], where state and metric in the Viterbi algorithm correspond to the node on the time-frequency plane and the amplitude of ST-DFT output, respectively (see Fig. 7). The FSE algorithm examines all the frequency paths leading to a given node and chooses the most likely path according to the accumulated metric. After the procedure is repeated for all the frequency indexes in a given time period (the data-field length in a signal burst), a frequency-index sequence with the largest accumulated metric is finally chosen. The FSE is made according to the following algorithm (see Fig. 7), where is the accumulated metric at, is a set of transition frequency indexes, which allows the previous nodes to transit to, and is the number of data symbols in a signal burst. 1) Let and set to be for. 2) Let, examine all the frequency paths leading to, and choose the most likely path according

HARA et al.: NOVEL FSK DEMODULATION METHOD FOR SATELLITE COMMUNICATION SYSTEMS 629 (a) Fig. 8. (a) (b) Allowable frequency transition. (a) MLFSE and (b) FSE. Fig. 7. (b) FSE. (a) Metric and node. (b) Frequency-index-sequence estimation. to for (23) 3) If, then go to Step 2). 4) Find the frequency-index sequence according to the largest. We propose the following two sets of the allowable transition frequency indexes. 1) Maximum Likelihood FSE (MLFSE):, where the frequency offset is assumed to be time-invariant and the frequency transitions associated only with the modulation process are allowed [see Fig. 8(a)]. 2) FSE:, where the frequency offset is assumed to be time-variant and frequency transitions associated with both the modulation process and the frequency drift due to frequency-offset rate are allowed [see Fig. 8(b)]. Note that the MLE characteristic can hold only for the MLFSE, where the frequency variation in one signal burst is negligibly small. C. Required Memory, Demodulation Delay, and Complexity Comparisons The bit-by-bit algorithm needs to memorize only a previously decided frequency at every demodulation instant and requires no demodulation delay. This is the simplest among the three algorithms. On the other hand, the MLFSE and FSE algorithms need to store all the frequency paths with a huge memory and require -symbol demodulation delay similar to conventional Viterbi algorithms for convolutional codes. Furthermore, the number of comparisons to choose a most-likely frequency path leading to each node is two and eight for the MLFSE and FSE, respectively. In this sense, the FSE is more complicated than the MLFSE. In order to shorten the demodulation delay, we have discussed the effect of the frequency-path history length. A truncated algorithm only with an eight-symbol path-history length can achieve almost the same BEP performance as the (nontruncated) FSE algorithm [the associated demodulation delay is eight (symbols)] [15]. VI. DFT-BASED ST-DFT WINDOW SYNCHRONIZATION The ST-DFT-based demodulation method requires no carrier frequency/phase recovery, but an accurate ST-DFT window synchronization. Therefore, we propose a DFT-based ST-DFT window-synchronization method. Fig. 9 shows a signal burst used in the ST-DFT-based demodulation method. The preamble is composed of symbols, where and alternately appear and the tail symbol is used to identify the end of the preamble. Defining as the number of samples per window, we can calculate kinds of for the th symbol with different sets of window timing. The ST-DFT window-synchronization method finds the best window timing that can minimize the intersymbol interference due to the window-timing offset (see Fig. 9). The ST-DFT window synchronization is made according to the following algorithm. 1) Let. 2) Calculate for the symbols in the preamble.

630 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 46, NO. 3, AUGUST 1997 TABLE I TRANSMISSION PARAMETERS Fig. 9. DFT-based ST-DFT window-synchronization method. 3) Calculate the cumulative for odd symbols and for even symbols as 4) Search, which maximizes, and, which maximizes. 5) Calculate given by (24) 6) If, then let and go to Step 2). 7) Find the optimum window timing to maximize. Step 3) ensures the reduction of the effect of background noise by adding up (averaging) the instantaneous energy spectra for odd and even symbols, respectively. VII. NUMERICAL RESULTS Table I shows the transmission parameters to demonstrate the BEP performance of the ST-DFT-based demodulation method. In Figs. 10 15, we assume a perfect ST-DFT window synchronization, and finally, in Figs. 16 and 17, we show the performance of the DFT-based ST-DFT windowsynchronization method. The theoretical BEP lower bound, which corresponds to the BEP of the differentially encoded binary FSK/noncoherent detection scheme in the AWGN channel with no frequency offset, is given by (see Appendix) BEP (25) (26) where represents the signal-to-noise energy ratio per bit. Fig. 10 shows the BEP versus the ST-DFT window width. It could be impossible to evaluate the performance of all the window functions because a number of window functions have been proposed so far. Here, we choose typical three window functions: the Hamming, Hanning, and rectangular Fig. 10. BEP versus ST-DFT window width. window functions [16] (also, see [17] for the performance of the Blackman and Kaiser window functions) and try to find the best window function and width suited to the transmitted Nyquist pulse. In general, a shorter window width results in a worse BEP because of a lack of signal energy, while a longer window also results in a worse BEP because of being rich in intersymbol interference. Therefore, there is an optimum value in the window width to minimize the BEP. It can be seen from the figure that the Hanning window with the width of two-symbol duration is the best choice among three window functions. The rolloff factor and modulation index are important parameters for determining the required bandwidth of the transmitted signal. Figs. 11 and 12 show the BEP versus and, respectively. As increases, the BEP improves because of less intersymbol interference. On the other hand, a smaller results in a worse BEP because of narrower frequency separation, while a larger also results in a worse BEP because of frequent misdetection of the false spectral peak caused by the Nyquist filter. Therefore, there is an optimum value in for minimizing the BEP. It can be seen from the figure that, leaving the required bandwidth out of consideration, and are the best choices. Fig. 13 shows the BEP versus for different values of interpolation index. As increases, the BEP performance improves because the instantaneous energy spectrum can be analyzed in more detail. However, it requires more time for the calculation. It can be seen from the figure that is a reasonable choice from the viewpoint of calculation time and BEP improvement. Fig. 14 shows the BEP versus the fixed frequency offset, where the frequency-offset rate is set to be zero. The ST-DFT-based demodulation method is insensitive to the

HARA et al.: NOVEL FSK DEMODULATION METHOD FOR SATELLITE COMMUNICATION SYSTEMS 631 Fig. 11. BEP versus rolloff factor. Fig. 14. BEP versus fixed frequency offset. Fig. 12. BEP versus modulation index. Fig. 15. BEP versus frequency-offset rate. Fig. 13. BEP versus E b =N 0 for different values of interpolation index. fixed frequency offset, as long as the received signal can be passed through the receiver front-end bandpass filter with no distortion. Fig. 15 shows the BEP versus the frequency-offset rate. The bit-by-bit algorithm, which is the simplest among the three proposed algorithms, can keep a good BEP performance for [Hz/s]. The MLFSE algorithm can achieve the best performance for small values of ( [Hz/s]), which is almost the same as the lower bound. However, the performance suddenly degrades as the frequency-offset rate becomes large because the frequency variation in one signal burst becomes significantly large. The FSE algorithm, which is the most complicated one, is more robust to the frequency-offset rate than the bit-by-bit algorithm, and it can keep a better performance for [Hz/s]. Note that the bit-by-bit and FSE algorithms can work well for the maximum Doppler rate shown in Fig. 1. Fig. 16 shows the average window offset versus the length of preamble. is a reasonable choice from the viewpoint of power efficiency and achievable window-offset error. Fig. 17 shows the BEP versus for. The performance of the proposed DFT-based ST-DFT windowsynchronization method is almost the same as that of the perfect window synchronization.

632 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 46, NO. 3, AUGUST 1997 Fig. 16. Average window error versus preamble length. The receiver configuration has shown the simple structure of the ST-DFT-based FSK demodulation method, and the numerical results show that it is robust to the time-variant frequency offset. Also, the ST-DFT principle has revealed that the performance is insensitive to the front-end signal-to-noise power ratio. The ST-DFT-based FSK demodulation method is insensitive to the fixed frequency offset. The bit-by-bit demodulation algorithm is robust to the frequency-offset rate and can keep good BEP s for various values of. The maximum likelihood FSE (MLFSE) can achieve the best BEP performance among three demodulation methods for small values of the frequencyoffset rate, which is almost the same as the BEP lower bound. However, when the frequency-offset rate becomes large, the performance suddenly degrades. The FSE is more robust to the frequency-offset rate and can keep better BEP s than the bit-by-bit algorithm in the wider range of the frequency-offset rate. Also, the DFT-based ST-DFT window-synchronization method, when an adequate preamble length is chosen, can achieve almost the same performance as the perfect window synchronization. APPENDIX The BEP lower bound is given by BEP (27) Fig. 17. BEP versus E b =N 0 with ST-DFT-based DFT window-synchronization method. TABLE II MODULATION/DEMODULATION PARAMETERS Table II summarizes the best combination of modulation and demodulation parameters obtained in this paper. VIII. CONCLUSION This paper has proposed a novel FSK demodulation method using the ST-DFT analysis for an LEO satellite communication channel with a large and time-variant frequency offset. A bit-by-bit demodulation algorithm and a novel efficient demodulation algorithm FSE have been introduced. In addition, this paper has proposed a simple DFT-based ST-DFT windowsynchronization method. where and are the probability of and the probability of given, respectively, and is the BEP of binary FSK/noncoherent detection scheme in the AWGN channel with no frequency offset given by [18] REFERENCES (28) [1] S. Ginn, Personal communication services: Expanding the freedom to communicate, IEEE Commun. Mag., vol. 29, no. 2, pp. 30 39, 1991. [2] T. Toyonaga, Research on coherent demodulation scheme suited for mobile satellite communication systems (in Japanese), Master s thesis, Osaka University, Osaka, Japan, 1993. [3] H. Meyr and G. Ascheid, Synchronization in Digital Communications, Vol. I. New York: Wiley, pp. 305 329, 1990. [4] F. D. Natali, AFC tracking algorithm, IEEE Trans. Commun., vol. COM-32, no. 8, pp. 935 947, 1984. [5] Wavelets and signal processing, IEEE Signal Processing Mag., vol. 8, no. 4, pp. 14 38, 1991. [6] C. R. Cahn, Improving frequency acquisition of Costas loop, IEEE Trans. Commun., vol. COM-25, no. 12, pp. 1453 1459, 1977. [7] M. K. Simon, Dual-pilot tone calibration technique, IEEE Trans. Veh. Technol., vol. VT-35, no. 2, pp. 63 70, 1986. [8] M. K. Simon and D. Divsalar, Doppler-corrected differential detection of MPSK, IEEE Trans. Commun., vol. 37, no. 2, pp. 99 100, 1989. [9] A. Kajiwara, Mobile satellite CDMA system robust to Doppler offset, IEEE Trans. Veh. Technol., vol. 44, no. 3, pp. 480 486, 1995. [10] W. K. M. Ahmed and P. J. Mclane, A simple method for coarse frequency acquisition through FFT, in Proc. IEEE VTC 94, June 1994, pp. 297 301. [11] A. Wannasarnmaytha, S. Hara, and N. Morinaga, A novel FSK demodulation method using short-time DFT analysis for LEO satellite communication systems, in Proc. IEEE GLOBECOM 95, Nov. 1995, pp. 549 553.

HARA et al.: NOVEL FSK DEMODULATION METHOD FOR SATELLITE COMMUNICATION SYSTEMS 633 [12] F. Hlawatsch and G. H. Boudreaux-Bartels, Linear and quadratic timefrequency signal representations, IEEE Signal Processing Mag., vol. 9, no. 2, pp. 21 67, 1992. [13] J. G. Proakis, Digital Communications. New York: McGraw-Hill, pp. 340 344, 1989. [14] V. K. Bhargava, D. Haccoun, R. Matyas, and P. P. Nuspl, Digital Communications by Satellite. New York: Wiley, pp. 375 382, 1981. [15] A. Wannasarnmaytha, S. Hara, and N. Morinaga, A novel ST-DFT based M-ary FSK demodulation method, to be published. [16] L. R. Rabiner and B. Gold, Theory and Application of Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, pp. 88 93, 1975. [17] A. Wannasarnmaytha, S. Hara, and N. Morinaga, A new short-time DFT FSK demodulation method for LEO satellite communications systems, to be published. [18] M. Schwartz, W. R. Bennett, and S. Stein, Communication Systems and Techniques. New York: McGraw-Hill, pp. 295 298, 1966. Shinsuke Hara (S 87 M 90) received the B.Eng., M.Eng., and Ph.D. degrees in communication engineering from Osaka University, Osaka, Japan, in 1985, 1987, and 1990, respectively. From 1990 to 1996, he was an Assistant Professor in the Department of Communication Engineering, Osaka University. Since April 1996, he has been a Lecturer in the Department of Electronic, Information, and Energy Engineering, Graduate School of Engineering, Osaka University. From April 1996 to March 1997, he was a Visiting Scientist in the Telecommunications and Traffic Control Systems Group, Delft University of Technology, Delft, The Netherlands. His research interests include satellite, mobile and indoor wireless communications systems, and digital signal processing. Dr. Hara is a Member of the IEICE of Japan. Yuuji Tsuchida received the B.Eng. and M.Eng. degrees in communication engineering from Osaka University, Osaka, Japan, in 1992 and 1994, respectively. Since 1994, he has been with the Audio Laboratory, Sony Corporation, Tokyo, Japan, working on the research and development of an advanced digital audio system. Mr. Tsuchida is a Member of the IEICE of Japan. Norihiko Morinaga (S 64 M 68 SM 92) received the B.Eng. degree in electrical engineering from Shizuoka University, Shizuoka, Japan, in 1963 and the M.Eng. and Ph.D. degrees in communication engineering from Osaka University, Osaka, Japan, in 1965 and 1968, respectively. He is currently a Professor in the Department of Communication Engineering, Graduate School of Engineering, Osaka University, working in the areas of radio, mobile, satellite and optical communication systems and EMC. Dr. Morinaga is a Member of the IEICE and ITE of Japan. Attapol Wannasarnmaytha (S 94) received the B.Eng. degree in electrical engineering from Chulalongkorn University, Bangkok, Thailand, in 1992 and the M.Eng. degree in communication engineering from Osaka University, Osaka, Japan, in 1995. He is currently working toward the Ph.D. degree at Osaka University. His research interests are digital signal processing and mobile satellite communications. Mr. Wannasarnmaytha is a Student Member of the IEICE of Japan.