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 main contributions of the paper are the following.
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.