A SIMULATION STUDY ON SPACE-TIME EQUALIZATION FOR MOBILE BROADBAND COMMUNICATION IN AN INDUSTRIAL INDOOR ENVIRONMENT

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1 A SIMULATION STUDY ON SPACE-TIME EQUALIZATION FOR MOBILE BROADBAND COMMUNICATION IN AN INDUSTRIAL INDOOR ENVIRONMENT U. Trautwein, G. Sommerkorn, R. S. Thomä FG EMT, Ilmenau University of Technology P.O.B. 565, D Ilmenau, Germany Phone: Fax: WWW: Abstract - Linear space-time equalization is shown to be effective in coping with the complicated propagation conditions for wireless broadband communication in an industrial indoor environment. This is demonstrated by realistic simulations that use real channel measurement data obtained from a vector channel sounder for modeling the influence of the radio channel. The system parameters are similar to the HIPERLAN/ standard of ETSI with a data rate of about 24 Msym/s and a carrier frequency of 5.2 GHz. I. INTRODUCTION The design of new broadband wireless communication systems requires special attention to the propagation conditions of the application scenario. Industrial indoor environments like large factory halls show typically a complicated radio channel because of the presence of many reflecting objects. This results in wide delay spreads and a considerably changing channel for a moving mobile unit. There exists a number of options to overcome the difficulties of heavy multipath propagation. Besides the choice of a robust modulation scheme the use of adaptive antenna arrays is frequently proposed because of their ability for improving the radio channel by exploiting its directional dimension. This paper investigates the potential of a particular implementation of an adaptive antenna, an MMSE space-time equalizer [3], for a wireless LAN system in an industrial environment. The system parameters have been chosen similar to the HIPERLAN/ standard of ETSI [2] for HIgh PERformance Radio LAN s that employs a single-carrier TDMA transmission scheme with a symbol rate of about 24 Msym/s. The performance of receivers with different numbers of antennas and equalizer memory lengths has been determined by link-level simulations. A crucial point for reasonable simulation results is the application of a realistic radio channel model. For adaptive antenna array simulations this model has to be directional. When the mobiles are moving through complex scenarios the model should be able to represent the time-varying as well as the non-stationary characteristics of the channel. Although a number of channel models have been proposed in the literature [6] there exists no appropriate directional channel model for industrial scenarios. As an alternative it is possible to insert measured impulse responses in the simulation. Of course, this requires special measurement equipment. This is available with the RUSK ATM vector channel sounder [4]. A measurement campaign has been carried out in a car factory hall of DaimlerChrysler (Germany). The measured and preprocessed vector impulse responses have been applied for this study. Section II describes the measurement device for vector channel sounding, section III gives an overview of the characteristics of the measured impulse responses, section IV describes the simulation setup, and section V shows results of the simulations. II. VECTOR CHANNEL SOUNDING The RUSK ATM channel sounder is able to measure the time-variant vector impulse response between one omnidirectional transmit antenna (located at the mobile) and an 8 element uniform linear patch antenna array with a bandwidth of 2 MHz at carrier frequencies of 5 6 GHz. The radio channel is excited by a periodic multifrequency sounding signal which is generated at baseband by an arbitrary waveform generator and afterwards up-converted to RF and radiated with a power of 27 dbm. The period has to be chosen according to the expected maximum delay of the channel. Values of s are possible. Transmitter and receiver are synchronized either by a cable connection or by two synchronous rubidium frequency references, thus allowing the measurement of the complex baseband impulse responses. Fast antenna multiplexing is employed at the receiver to limit the hardware costs to one RF receive channel for all 8 receive antennas. After the down-conversion to an intermediate frequency of 8 MHz the signal is digitized and digital signal processing is used for IQ demodulation and correlation processing to calcu-

2 late the impulse responses. The delay resolution of the individual multipath components is in the range of 8 5 ns which corresponds to m path length and depends on the type of a frequency window which is applied for enhanced sidelobe suppression in the impulse responses. The dynamic range of the impulse responses is approximately 35 db. The measurement repetition rate (the sampling rate of the impulse responses in time) can be set in a wide range up to 78 khz. This rate has to be chosen according to the expected maximum Doppler shift of the channel. Then, the measurement data completely represent the time-variant channel statistics. Due to hardware limitations there exists a mutual dependency of this sampling rate and the maximum number of consecutive impulse responses that can be recorded, e.g., at khz sampling rate a record length of 7 s can be achieved. [db] Azimuth [ ] III. CHANNEL CHARACTERIZATION The measurements within the car factory hall of DaimlerChrysler comprise impulse responses for a number of fixed s of the transmitter (Tx) as well as for a drive through the hall. The distances to the receiver (Rx) were in the range of 5 to 2 m. Line-of-sight (LOS) as well as non line-of-sight (NLOS) situations have been considered. The path loss ranges from 5 8 db. The rms delay spread is in the range from 3 ns for LOS situations up to 6 ns for NLOS situations, which corresponds to 4 symbol periods for the simulated 24 MSym/s system. Knowledge about the angular characteristics of the channel is especially important for adaptive antenna array systems. The delay-azimuth spectrum has been determined from the measurement data by high resolution direction of arrival estimation [5]. For distant LOS locations the multipath components arrive from a significantly smaller angular segment than for close LOS locations or NLOS locations (Fig. ). The values for the path loss, rms delay spread, and rms angular spread for 5 different s in the hall, approx. separated by m each, are given in Fig. 7. The change from the LOS to NLOS situations (around 68) are clearly visible by a strong increase in path loss and delay spread values. The measurement repetition interval was set to 5.2 ms which allows a measurable Doppler bandwidth of up to 95 Hz. This means at the utilized carrier frequency of 5.2 GHz that the total path length may change with a speed of maximally 5.6 m/s. Fig. 2 shows a typical average Doppler spectrum and an average delay spectrum calculated from the impulse responses during a drive of a length of 7 cm (2 wavelengths at 5.2 GHz) in a NLOS situation. This part has also been used for the dynamic transmission simulations in the next section. Clearly visible is the main peak at a Doppler frequency of 32 Hz which corresponds to the speed of the mobile of.8 m/s. For the fixed measurement locations no significant Doppler spread has been observed, indicating that multipath components from the moving objects present in the scenario are negli- [db] Azimuth [ ] Fig. : Delay-azimuth spectrum for a distant LOS (top) and a NLOS (bottom) gible. This has the consequence that it is possible to simulate a faster speed of motion than the real speed during the measurement simply by assuming a higher time sampling rate for the impulse responses. IV. Approach SIMULATION SETUP The influence of the channel on the transmit signal can be modeled by FIR filtering. For an array receiver a separate set of filter coefficients for each antenna element is required. For this paper two kinds of link-level simulations have been performed. For fixed Tx locations static simulations assume the impulse response to be time-invariant. Magnitude [db] Doppler frequency [Hz] Magnitude [db] Fig. 2: Average Doppler spectrum (left) and average delay spectrum (right) for a measurement drive

3 For a moving Tx a sequence of measured vector impulse responses is used for dynamic simulations. This is possible due to the real-time capabilities of the RUSK ATM vector channel sounder. Thereby the preprocessed impulse responses act as coefficients of time-varying FIR filters. This allows to investigate the behavior and performance of different receiver structures as if they are facing the particular conditions of the radio channel during the measurement. This is advantageous for the optimization of the receiver s signal processing when an appropriate vector channel model is not available for complex scenarios. gain relative timing [samples] Real Imag For both kinds of simulation a preprocessing of the measurement data is necessary to suit the sampling rates of the measurement to the sampling rates of the simulated system. The measurement data files of the DaimlerChrysler scenario consist of 92 complex frequency samples within the 2 MHz measurement bandwidth at an impulse response length of.6 s. The simulations have been performed at waveform level with 4 times oversampling w.r.t. the symbol rate of Msym/s (4.83 ns symbol period). The selection of 53 consecutive frequency samples at the desired carrier frequency realizes the accommodation to the sampling rate of the simulation. Then the impulse responses are calculated by means of an inverse FFT. For reducing the computational burden of the simulation it is advantageous to reduce the number of taps of the FIR filters to the effective delay window of the impulse responses, which is the part of the impulse response containing the most energy. Here, due to relatively large delay spreads, the required number of taps is 7 9 ( ns). This introduces intersymbol interference (ISI) up to 7 23 symbols. The measurement rate is set according to the maximum expected Doppler shift, thus fully representing the timevariance of the impulse responses. For the dynamic simulations it has to be increased according to the simulation sampling rate by some interpolation procedures. Here, the required interpolation factor is very high (4896 for original speed). To reduce the computations a two stage interpolation has been implemented for the system. The st stage consists of an FIR lowpass interpolator with a stopband attenuation of 7 db. It increases the sampling rate by a factor of 5. The further increase in sampling rate is achieved by linear interpolation, which has shown to yield sufficient accuracy. It has been verified by experiment that for the given data rates and burst durations even a th order interpolation (hold) does not yield significant differences in the simulation results. The Implemented System The system parameters for the simulations have been chosen similar to the HIPERLAN/ standard of ETSI. The frame structure consists of a training sequence (45 bits) and a variable number (...47) of data packets (496 bits) Fig. 3: Synchronization values from the correlation unit For this paper a continuous transmission of frames, separated by a guard interval of 2 bits, has been simulated. Contrary to the standard, BPSK modulation with an excess bandwidth of 5 % has been used instead of GMSK. The transmit signal is generated and processed with 4 times oversampling w.r.t. the symbol rate. The receiver filters are FIR lowpass filters. The optimum symbol timing for sampling at the receiver is obtained by a correlation unit that finds the maximum correlation between the known training sequence (spectrally white) and the receive signal (Fig. 3 top). The value of the correlation function at this instant is used to determine a complex gain factor (Fig. 3 bottom) to adjust the amplitude and phase of the incoming signal once per burst, thus enabling the use of coherent detection. Clearly, the results of the correlation unit will be imperfect due to noise and ISI (like it is in reality), but the influence on the results is found to be weak. Different receiver configurations have been investigated, consisting of, 2, 4, and 8 array channels in combination with linear T/2-spaced space-time equalizers of different memory lengths (Fig. 4). This means, each receiver channel possesses a number of equalizer taps that are jointly adapted during the training phase, and the output signals of all equalizers are summed to form the signal for the detection of the data symbols. The recursive least squares (RLS) algorithm has been used for the adaptation, which minimizes the mean square error (MMSE criterion) between the output signal of the equalizer and the training sequence [3]. It is especially suitable for mobile communication applications because of its fast convergence []. After the training phase the equalizer taps are held constant for the duration of the burst. The fractionally-spaced structure was chosen because it does not need matched filters at the input and is not sensitive to sampling phase errors [7]. This eases synchronization because the timing phase could be different for all array outputs.

4 w, w,2 w,3 w,m output close LOS distant LOS distant NLOS w 2, w 2,2 w 2,3 w 2,M t=nt mean bit error rate 2 3 w N, w N,2 w N,3 w N,M weight adaptation 4 t=nt/2 Fig. 4: Structure of the space-time equalizer in the complex baseband..8 Prob{BER > } Prob{BER > 2 } Prob{BER > 3 } 5 Fig. 6: Mean BERs for different Tx/Rx constellations (SNR= db) probability * 45 4 * 5 8 * 3.2 mean bit error rate 3 4 Fig. 5: Outage probabilities (SNR= db). V. RESULTS With the system described above the raw bit error rate (BER) performance has been determined by Monte Carlo simulations for static as well as dynamic channel conditions. With time-invariant impulse responses it is possible to see whether a particular receiver can cope with the propagation conditions at many locations within the hall. The dynamic simulation can be used to verify the behavior of the receiver during the motion of the transmitter. This shows whether tracking is necessary over the burst duration, what is an appropriate maximum burst length, how is the influence of the fast fading, or whether a particular adaptation algorithm is suitable. Static Transmissions Temporal equalization is a must for the given scenario and system parameters because of strong intersymbol interference (ISI). Beamforming only improves the reception but is not sufficient. This can be concluded from Figs. 5, 6, and 7. An important factor for the effectiveness of linear equalization is the signal to noise ratio (SNR). For the presented simulations a constant SNR at all locations has been adjusted which is equivalent to an ideal power control. For low SNR values a significant performance gain can be path loss [db] delay spread [ns] angular spread [ ] Fig. 7: Characterization of different transmitter locations in terms of (i) bit error rates for 3 receiver configurations (missing bars for BER ) at SNR db, (ii) path loss, (iii) delay spread, and (iv) angular spread (from top).

5 * 2 * 5 4 * 5 8 * 3 bit error rate 2 4 bit error rate Fig. 8: Mean bit error rates during the drive (NLOS) Fig. 9: Instantaneous bit error rates during the drive achieved by the use of multiple antennas. This is expected because the antenna gain effectively increases the SNR. On the other hand, this performance gain could be replaced to some extent by a longer equalizer plus a higher transmit power. Even an equalizer with many taps leaves some critical locations which leads to a coverage problem (Fig. 7). It is also visible that performance gains are possible that are beyond the pure SNR enhancement capability of an array receiver, meaning that the space-time equalizer achieves ISI reduction by suppressing multipath components with delays exceeding the equalizer memory length. Fig. 5 shows the probabilities that the BER exceeds a certain threshold at the different locations. This is useful for determining the coverage with a given quality of the radio link. Dynamic Transmissions The simulations with a moving transmitter (.8 m/s) in NLOS situations use burst lengths with or 4 data packets, resp., which yields burst durations of.23 ms or.85 ms. For the assumed burst durations there is no significant BER degradation towards the end of the burst without any tracking over the burst. This is remarkable because on the other hand the optimum sampling time as calculated by the correlation unit makes jumps of more than 5 symbol periods from burst to burst (Fig. 3). This means that the length of the main propagation path changes considerably over very small distances. Fig. 8 shows the mean BERs for different receiver complexities for the drive in a NLOS scenario. Again it indicates that without at least short temporal equalization component no sufficient performance can be achieved. Fig. 9 gives examples of instantaneous BER curves. These results can be used to derive the temporal BER statistics of a radio link which in turn can be used, e.g., to optimize coding, interleaving, and also network protocols. VI. CONCLUSIONS AND OUTLOOK It has been shown that linear space-time equalization is an effective mean for combating the strong ISI present at broadband communications in an industrial scenario. A further aspect of space-time equalizers has been omitted in this study so far, their capability for interference reduction of co-channel and adjacent channel interferences. It is possible to use the same approach also for the investigation of this problem. Thereby the signals of co- and adjacent channel users are processed by FIR filters measured at different locations of the environment. Furthermore it is of interest to test more advanced algorithms, such as nonlinear equalizers. The derivation of relations between channel parameters and the necessary receiver complexity requires more investigations and the evaluation of more measurement data. ACKNOWLEDGMENTS This work is partially supported by the German Federal Ministry of Education, Science, Research, and Technology under the project line ATMmobil. The authors are grateful to MEDAV GmbH for cooperation in designing the channel sounder and to Dr. Aldinger (DaimlerChrysler) for cooperation in the field measurement. REFERENCES () J. Fuhl, A. F. Molisch, Space Domain Equalisation for Second and Third Generation Mobile Radio Systems, 2. ITG-Fachtagung Mobile Kommunikation, Neu-Ulm, Germany, pp , Sep (2) ETSI (ETS 3 652), Radio Equipment and Systems, HIPERLAN Type, Functional specification, Oct (3) A. J. Paulraj, B. C. Ng, Space-Time Modems for Wireless Personal Communications, IEEE Pers. Comm., pp , Feb (4) U. Trautwein, K. Blau, D. Brückner, F. Herrmann, A. Richter, G. Sommerkorn, R. Thomä, Radio Channel Measurement for Realistic Simulation of Adaptive Antenna Arrays, Proc. 2nd European Personal Mobile Communications Conference, pp , Sep (5) R. S. Thomä, D. Hampicke, A. Richter, G. Sommerkorn, A. Schneider, U. Trautwein, Identification of Time-Variant Directional Mobile Radio Channels, Proc. IEEE Instrumentation and Measurement Conference, May 999. (6) G.Raleigh, S. N. Diggavi, A. F. Naguib, A. Paulraj, Characterization of Fast Fading Vector Channels for Multi- Antenna Communication Systems, IEEE Asilomar Conf. on Signals, Computers and Systems, pp , 994. (7) J. G. Proakis, Digital Communications, McGraw-Hill, 989.

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