- Research Article
- Open Access
Channel Characteristics and Performance of MIMO E-SDM Systems in an Indoor Time-Varying Fading Environment
© Huu Phu Bui et al. 2010
- Received: 13 October 2009
- Accepted: 13 March 2010
- Published: 20 April 2010
Multiple-input multiple-output (MIMO) systems employ advanced signal processing techniques. However, the performance is affected by propagation environments and antenna characteristics. The main contributions of the paper are to investigate Doppler spectrum based on measured data in a typical meeting room and to evaluate the performance of MIMO systems based on an eigenbeam-space division multiplexing (E-SDM) technique in an indoor time-varying fading environment, which has various distributions of scatterers, line-of-sight wave existence, and mutual coupling effect among antennas. We confirm that due to the mutual coupling among antennas, patterns of antenna elements are changed and different from an omnidirectional one of a single antenna. Results based on the measured channel data in our measurement campaigns show that received power, channel autocorrelation, and Doppler spectrum are dependent not only on the direction of terminal motion but also on the antenna configuration. Even in the obstructed-line-of-sight environment, observed Doppler spectrum is quite different from the theoretical U-shaped Jakes one. In addition, it has been also shown that a channel change during the time interval between the transmit weight matrix determination and the actual data transmission can degrade the performance of MIMO E-SDM systems.
- MIMO System
- Antenna Element
- Maximal Ratio Combine
- MIMO Channel
- Mutual Coupling
The use of multiple antennas at both ends of a communication link, commonly referred to as a multiple-input multiple-output (MIMO) system, has been widely studied and is considered as one of the prospective technologies to provide high data rate transmission and good performance for the dramatically growing wireless communications demands nowadays. Many studies have confirmed that, without additional power and spectrum compared with conventional single-input single-output (SISO) systems, channel capacity of MIMO systems can increase in proportion to the number of antennas in Rayleigh fading environments [1–3]. Moreover, when channel state information (CSI) is available at a transmitter (TX), the performance of the MIMO system can be improved further by applying an eigenbeam-space division multiplexing (E-SDM) technique, which is also called eigenmode transmission or singular value decomposition- (SVD-) based technique [1–6]. In the E-SDM technique, orthogonal transmit beams are formed based on the eigenvectors obtained from singular value decomposition of a MIMO channel matrix, and transmit data resources can be allocated adaptively. In the ideal case, in which the transmit weight matrix completely matches an instantaneous MIMO channel response, spatially orthogonal substreams with the optimal resource allocation can be achieved. As a result, a simple maximum ratio combining (MRC) detector or a spatial filter such as a minimum mean square error (MMSE) filter or zero-forcing (ZF) filter can detect the substreams without inter-substream interference, and the maximum channel capacity is obtained.
In realistic environments, however, due to dynamic nature of the channel and processing delay at both the TX and the receiver (RX), a channel transition may cause a severe loss of subchannel orthogonality, which results in large inter-substream interference. In addition, the channel change prevents optimal resource allocation from being achieved. Consequently, based on computer-generated channels assuming the Jakes model , we have confirmed that the performance of MIMO E-SDM systems is degraded in time-varying fading environments with rich scatterers [8, 9]. The Jakes model is very simple because required parameters are very few, and it is easy as regards simulations. However, actual MIMO systems may be used in line-of-sight (LOS) environments, and even in a non-LOS (NLOS) case, scatterers may not be uniformly distributed around an RX and/or a TX. The geometry-based stochastic channel model (GSCM) has been proposed for multiple antenna systems [10–13]. The model includes also the LOS component and is more comprehensive than the Jakes model. It is expected that GSCM can explain phenomena in real-life fading environments. In order to apply GSCM, however, we need to determine several parameters, and we need three-dimensional ray tracing or extensive measurement campaigns [12, 13]. This is much more difficult to apply than the Jakes model. On the other hand, when using multiple antennas at both the TX and the RX, mutual coupling among antenna elements cannot be ignored because it affects the system performance in practical implementation [14–16]. Therefore, investigations into the systems in actual communications are necessary.
MIMO measurement campaigns have already been extensively conducted as reported in papers such as [6, 15–18]. However, most of MIMO measurement campaigns have not explicitly considered the effect of time-varying fading on the performance of MIMO systems. In , measurements were carried out in a case where a mobile station was moving. The objective of the study was not to examine the effect of time-varying channels but to introduce a stochastic MIMO radio channel model. In , the performance of closed-loop MIMO (i.e., MIMO E-SDM) systems was investigated in the fading environment where both TX and RX were fixed, and scatterers were moving during the experiment. It is said that the effects of moving scatterers in the environment were relatively unimportant.
In time-varying wireless communications, Doppler spectrum is a useful measure to evaluate the mobility of terminals . Then, the Doppler spectrum may affect the performance of MIMO E-SDM systems in dynamic channels. Due to various distributions of scatterers, LOS wave existence, and mutual coupling effect among antennas, the Doppler spectrum of SISO and MIMO channels in actual environments are, in general, different from the theoretical analyses. To the best of our knowledge, such work has rarely been considered [22, 23]. In , Doppler spectrum of a SISO channel was investigated where the base and user were both stationary, but scatterers in the environment were moving, causing time variations in the channel response. In , Doppler spectrum of a MIMO channel was examined in both indoor and outdoor environments. The results in [22, 23] revealed that the effects of moving scatterers in the environment were relatively unimportant. Both of [22, 23] did not consider the Doppler spectrum in the case of the LOS condition and the effect of the spectrum on the performance of MIMO systems. Also, array configurations have been considered based on measurement campaigns to clarify the channel capacity [24, 25]. The studies did not consider the effect of the array configuration to the MIMO E-SDM performance in time-varying environments.
We conducted SISO and MIMO measurement campaigns at a 5.2 GHz frequency band in an indoor time-varying fading environment. In our measurement campaigns, the RX was moved while the TX and scatterers were fixed. We evaluated the MIMO system performance partially using the HIPERLAN/2 standard . Based on the measured channel data, in this paper, we examined some channel properties such as antenna pattern, received power, channel autocorrelation, and Doppler spectrum of both SISO and MIMO cases. Then, we evaluated the bit-error rate (BER) performance of MIMO E-SDM systems in the environment.
The radiation patterns of the antenna elements in MIMO case are examined. It can be seen that the patterns change from the SISO case due to mutual coupling. This has an effect on the received power.
The received power, channel autocorrelation, and Doppler spectrum in actual fading LOS and obstructed LOS (OLOS) environments are considered. The results show that they are dependent on the direction of the RX motion, the antenna array configuration, and the propagation environments.
The performance of the E-SDM system is investigated in actual time-varying fading environments. It is shown that the performance can be degraded by the channel change during the time interval between the transmit weight matrix determination and the actual data transmission.
The paper is organized as follows. In the next section, a detailed measurement setup for our experiment is presented. In Section 3, the antenna pattern of a two-element array is considered. Based on the measured channel data, we examine received power in Section 4 and channel autocorrelation and Doppler spectrum in Section 5 for both SISO and MIMO cases. To investigate the performance of MIMO E-SDM systems in actual environments, we first describe the systems in Section 6. Then, a procedure of applying measured data for evaluation of the system performance in an indoor time-varying fading environment is given in Section 7. Based on the measured data, the performance of MIMO E-SDM systems in the environment is evaluated in Section 8. The conclusions are provided in Section 9.
On the RX side, a stepping motor was used to move the RX array along the - or -axis during the experiments. Each step of the motor was 0.0088 cm. This motor was exactly controlled by a personal computer. The RX array was stopped at every 10 steps (equal to 0.088 cm) of the motor. Channels were measured at intervals of 0.088 cm, and we had a total of 500 spatial measurement points. Therefore, the length of the measurement route was cm = 44 cm. Here, we chose the length of 44 cm because it covered several wavelengths of signal and the difference of pathloss measured at the first point and the last point was less than 1 dB.
The total of channel response matrix data was obtained for each case of the direction of the RX antenna motion, the array orientation, the antenna spacing, and the LOS/OLOS condition. It should be noted that the measurement campaigns were conducted while no one was in the room, to ensure statistical stationarity of propagation.
It is well known that when antenna spacing (AS) among elements is not large enough, there exists mutual coupling among the elements and their patterns are changed. In MIMO systems, due to the limitation of space, especially at mobile stations, the antenna spacing may be small. As a result, mutual coupling among antennas may be large, and this would affect the system performance. Thus, in this section, we consider the antenna pattern for a two-element linear array.
In this section, based on the measured channel data, we examine received power of both SISO and MIMO channels.
In this section, based on our measured channel data, we examine channel autocorrelation and Doppler spectrum of both SISO and MIMO cases.
where is the speed of light ( m/s) and is the carrier frequency of the mobile terminal.
where is the wavelength of the carrier frequency.
where the carrier frequency was assumed to be the center of the measurement band .
Before investigating the performance of MIMO E-SDM systems in actual time-varying fading environments, the concept of a MIMO E-SDM system is briefly described in the section. For more details on the system, refer to .
At the TX side, an input stream is divided into substreams . Then, signals before transmission are driven by a TX weight matrix to form orthogonal eigenbeams and control power allocation. At the RX side, received signals are detected by an RX weight matrix.
Here, are positive eigenvalues of H H H The columns of are the eigenvectors corresponding to those positive eigenvalues, and is the diagonal transmit power matrix. It should be noted that holds.
In an ideal MIMO E-SDM system, in which the TX weight matrix completely matches an instantaneous MIMO channel response, spatially orthogonal substreams with optimal resource allocation can be achieved. Under the circumstance, received signals can easily be demultiplexed by using a maximal ratio combining (MRC) or spatial filtering weight. However, in time-varying fading environments spatial filtering weight is a better choice to mitigate the degradation of system performance .
where , and is noise power. This indicates that the quality of each detected substream is different. Therefore, the channel capacity and performance of MIMO E-SDM systems can be improved by adapting the TX data resource and power allocation .
If the terminal's velocity increased up to , then also rose to Hz. In this case, the MIMO channel responses for the uplink ACK and DL packets were given by the measurement points and , respectively, as shown in Figure 16(b).
8.1. Simulation Parameters
Simulation Parameters of MIMO E-SDM System.
No. of TX & RX antennas
Minimum BER criterion
based on Chernoff upper-bound 
Data burst length
48 symbols (no coding)
15 PN symbols (BPSK)
Frame duration ( )
Delay from ACK ( )
Max Doppler frequencies ( )
31 & 93 Hz
Additive white Gaussian noise
RX signal processing
We assumed the DL packet duration of 0.12 milliseconds. This value is not shown in Table 1 because it does not explicitly affect the results. Because we have 48 symbols in the DL packet, the symbol duration is 0.0025 milliseconds. Then, the bandwidth is 400 kHz when the roll-off parameter is 0. On the other hand, as examined in , the time delay spread in the measurement site was less than 40 ns; thus the channel coherence bandwidth was considered to be wider than 2.5 MHz. The transmission bandwidth is much narrower than the coherence bandwidth, and we can assume the frequency flat fading.
The data rate was set to 2 bps/Hz (2 bits per symbol duration) per TX antenna; therefore, the total data rate was fixed constantly at 4 bps/Hz (4 bits per symbol duration) for the 2 2 MIMO system. The number of substreams was dependent on the resource adaptation, specifically the modulation scheme and the transmit power. We had two cases of the resource selection, namely, 16QAM 1 (1 stream) and QPSK 2 (2 streams). The reason why we need resource selection is because we should send more bits over a substream with higher SNR and fewer bits over a substream with lower SNR to obtain better BER under the fixed data rate requirement. Thus, we need to determine the modulation schemes for each substream considering the SNR that was stated in Section 6. Also, we need to allocate transmit power to each substream properly. The modulation and power allocation are determined in such a way that the upper bound of BER has the lowest value .
The HIPERLAN/2 system may be used in some different scenarios as described in , and depending on the scenarios, the mobility of mobile terminals may be fixed, walking speed, or slow vehicles limited within 10 m/s. In this paper, two values of of 31 and 93 Hz, which correspond to two terminal's velocities of 1.8 and 5.4 m/s for the carrier frequency of 5.2 GHz, were considered. The mobility can be considered as walking speed or slow vehicles. For those terminal velocities, we can assume that both of the uplink and the downlink packet duration were so short that the channel change during the duration was negligible.
8.2. Simulation Results
BER performance in the LOS environment is better than that in the OLOS one due to the higher received power, as shown in Figure 11. The BER performance is related to the direction of the RX motion. Better performance can be obtained in the LOS environment when the motion is along the -axis than when it is along the -axis. This is due to the effect of Dopper spectrum. As seen from Figure 13, the Doppler spectrum is distributed around 0 Hz in the LOS case for the RX motion along the -axis, whereas it is concentrated around for the RX motion along the -axis. It can be easily seen that the more distributed around 0 Hz the Doppler spectrum is, the better BER performance is obtained because of the less channel transition. In addition, the BER performance is also related to the antenna orientation. Better BER performance is obtained for the TX- /RX- orientation than for the TX- /RX- orientation in the case of the LOS environment and AS = 0.5 . This is because the antenna gain for the opposite end in the MIMO system was higher for the TX- /RX- orientation than for the TX- /RX- orientation due to the effect of mutual coupling among antenna elements, as shown in Figure 6. As a result, higher received power was obtained for the TX- /RX- orientation than for the TX- /RX- orientation in both cases of the RX array motion along the - and the -axes in the LOS environment and AS = 0.5 , as shown in Figure 11.
Furthermore, as in simulation results based on computer generated channels assuming the Jakes model [8, 9], the higher was, the more the BER performance was degraded in the indoor fading environment. This is because greater channel change during the time interval caused larger inter-substream interference and prevented optimal resource allocation from being achieved. Therefore, a countermeasure such as a channel prediction scheme [8, 9] may be necessary for MIMO E-SDM transmission in fast time-varying fading environments.
In this paper, we have presented an experiment for measuring SISO and MIMO channel responses at the 5.2 GHz frequency band in an indoor time-varying fading environment. In the environment, not only OLOS condition but also LOS condition was considered; scatterers were located at both the TX and the RX, and were not necessarily distributed uniformly; the effect of mutual coupling among antennas was also taken into account.
We first considered the antenna patterns of SISO and MIMO systems. Different from the SISO case where the antenna has an omnidirectional pattern, in the MIMO case, the patterns of antenna elements are changed due to the mutual coupling among antennas, and the antenna gain seems to decrease as the AS becomes smaller.
Based on the measured data, we second examined received power, channel autocorrelation, and Doppler spectrum. The results showed that these fading properties are dependent not only on the direction of the RX motion but also on the array configuration and propagation environments. These are due to the effects of various distributions of scatterers, multipath signals, LOS wave existence, and mutual coupling among antenna elements. Unlike theoretical analysis, Doppler spectrum in the indoor fading environment is different from the U-shaped Jakes one.
Finally, based on the measured data, the performance of MIMO E-SDM systems was evaluated. Simulation results showed that a channel change during the time interval between the transmit weight matrix determination and the actual data transmission could degrade the system performance in indoor communications. It was shown that the performance relates to the Doppler spectrum. Therefore, a channel prediction scheme may be necessary for the systems in indoor fast time-varying fading environments.
- Telatar E: Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications 1999, 10(6):585-595. 10.1002/ett.4460100604View ArticleGoogle Scholar
- Gesbert D, Shafi M, Shiu D-S, Smith PJ, Naguib A: From theory to practice: an overview of MIMO space-time coded wireless systems. IEEE Journal on Selected Areas in Communications 2003, 21(3):281-302. 10.1109/JSAC.2003.809458View ArticleGoogle Scholar
- Paulraj AJ, Gore DA, Nabar RU, Bölcskei H: An overview of MIMO communications—a key to gigabit wireless. Proceedings of the IEEE 2004, 92(2):198-217. 10.1109/JPROC.2003.821915View ArticleGoogle Scholar
- Miyashita K, Nishimura T, Ohgane T, Ogawa Y, Takatori Y, Cho K: High data-rate transmission with eigenbeam-space division multiplexing (E-SDM) in a MIMO channel. Proceedings of IEEE Vehicular Technology Conference (VTC '02-Fall), September 2002, Vancouver, Canada 3: 1302-1306.View ArticleGoogle Scholar
- Lebrun G, Gao J, Faulkner M: MIMO transmission over a time-varying channel using SVD. IEEE Transactions on Wireless Communications 2005, 4(2):757-764.View ArticleGoogle Scholar
- Ting SH, Sakaguchi K, Araki K: A robust and low complexity adaptive algorithm for MIMO eigenmode transmission system with experimental validation. IEEE Transactions on Wireless Communications 2006, 5(7):1775-1784.View ArticleGoogle Scholar
- Jakes WC: Microwave Mobile Communications. John Wiley & Sons, New York, NY, USA; 1974.Google Scholar
- Nishimura T, Tsutsumi T, Ohgane T, Ogawa Y: Compensation of channel information error using first order extrapolation in eigenbeam space division multiplexing (E-SDM). Proceedings of International Conference on Wireless Communications and Applied Computational Electromagnetics (ACES '05), April 2005, Honolulu, Hawaii, USA 44-47.Google Scholar
- Phu BH, Ogawa Y, Ohgane T, Nishimura T: Extrapolation of time-varying MIMO channels for an E-SDM system. Proceedings of IEEE Vehicular Technology Conference (VTC '06-Spring), May 2006, Melbourne, Australia 4: 1748-1752.Google Scholar
- Fuhl J, Molisch AF, Bonek E: Unified channel model for mobile radio systems with smart antennas. IEE Proceedings Radar, Sonar and Navigation 1998, 145(1):32-41. 10.1049/ip-rsn:19981750View ArticleGoogle Scholar
- Hofstetter H, Steinböck G: A geometry based stochastic channel model for MIMO systems. Proceedings of Internationa ITG Workshop on Smart Antennas (WSA '04), March 2004, Munich, Germany 194-199.View ArticleGoogle Scholar
- Bonek E, Weichselberger W, Herdin M, Özcelik H: A geometry-based stochastic MIMO channel model for 4G indoor broadband packet access. Proceedings of 18th General Assembly of the International Union of Radio Science (URSI '05), October 2005, New Delhi, India C03.1Google Scholar
- Karedal J, Tufvesson F, Czink N, et al.: A geometry-based stochastic MIMO model for vehicle-to-vehicle communications. IEEE Transactions on Wireless Communications 2009, 8(7):3646-3657.View ArticleGoogle Scholar
- Wallace JW, Jensen MA: Mutual coupling in MIMO wireless systems: a rigorous network theory analysis. IEEE Transactions on Wireless Communications 2004, 3(4):1317-1325. 10.1109/TWC.2004.830854View ArticleGoogle Scholar
- Nishimoto H, Ogawa Y, Nishimura T, Ohgane T: Measurement-based performance evaluation of MIMO spatial multiplexing in a multipath-rich indoor environment. IEEE Transactions on Antennas and Propagation 2007, 55(12):3677-3689.View ArticleGoogle Scholar
- Ogawa Y, Nishimoto H, Nishimura T, Ohgane T: Performance of MIMO spatial multiplexing in indoor line-of-sight environments. Proceedings of IEEE Vehicular Technology Conference (VTC '05-Fall), September 2005, Dallas, Tex, USA 4: 2398-2402.Google Scholar
- Czink N, Yin X, Özcelik H, Herdin M, Bonek E, Fleury BH: Cluster characteristics in a MIMO indoor propagation environment. IEEE Transactions on Wireless Communications 2007, 6(4):1465-1474.View ArticleGoogle Scholar
- Kolmonen V-M, Kivinen J, Vuokko L, Vainikainen P: 5.3-GHz MIMO radio channel sounder. IEEE Transactions on Instrumentation and Measurement 2006, 55(4):1263-1269. 10.1109/TIM.2006.877724View ArticleGoogle Scholar
- Kermoal JP, Schumacher L, Pedersen KI, Mogensen PE, Frederiksen F: A stochastic MIMO radio channel model with experimental validation. IEEE Journal on Selected Areas in Communications 2002, 20(6):1211-1226. 10.1109/JSAC.2002.801223View ArticleGoogle Scholar
- Mizutani K, Sakaguchi K, Takada J, Araki K: Measurement of time-varying MIMO channel for performance analysis of closed-loop transmission. IEEE Vehicular Technology Conference (VTC '06-Spring), May 2006, Melbourne, Australia 6: 2854-2858.Google Scholar
- Stoica P, Moses R: Introduction to Spectral Analysis. Prentice Hall, New York, NY, USA; 1997.MATHGoogle Scholar
- Domazetovic A, Greenstein LJ, Mandayam NB, Seskar I: Estimating the Doppler spectrum of a short-range fixed wireless channel. IEEE Communications Letters 2003, 7(5):227-229. 10.1109/LCOMM.2003.812172View ArticleGoogle Scholar
- Wallace JW, Jensen MA: Time-varying MIMO channels: measurement, analysis, and modeling. IEEE Tranactions on Antennas and Propagation 2006, 54(11):3265-3273.View ArticleGoogle Scholar
- Sulonen K, Suvikunnas P, Vuokko L, Kivinen J, Vainikainen P: Comparison of MIMO antenna configurations in picocell and microcell environments. IEEE Journal on Selected Areas in Communications 2003, 21(5):703-712. 10.1109/JSAC.2003.810297View ArticleGoogle Scholar
- Nishimori K, Makise Y, Ida M, Kudo R, Tsunekawa K: Channel capacity measurement of 8 x 2 MIMO transmission by antenna configurations in an actual cellular environment. IEEE Transactions on Antennas and Propagation 2006, 54(11):3285-3291.View ArticleGoogle Scholar
- Broadband Radio Access Networks (BRAN); High Performance Radio Local Area Network (HIPERLAN) Type 2; requirements and architectures for wireless broadband access European Telecommunications Standards Institute, Sophia Antipolis, France; January 1999.Google Scholar
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