Understanding the Impact of Channel Effects on MIMO System Performance
By Wilkie Yu, Agilent Technologies

Multiple-Input Multiple-Output (MIMO) technologies hold the promise of higher data rates with increased spectral efficiency. Since commercial wireless systems operate in high multipath environments, they benefit greatly from the multipath characteristics of MIMO antenna systems. Because of the large potential improvement in wireless system performance that MIMO offers, many wireless communication standards committees have recently adopted or are considering its use.

Despite its appeal, MIMO is very complex and presents some unique test and measurement challenges when implemented in a wireless system. One such challenge lies with the wireless channel and channel correlation effects like path loss and multipath fading. In wireless communication systems, the wireless channel is the key factor in determining system performance. The goal is to ensure that the wireless channel is substantially different for each channel, since this difference is what allows multiple streams to be supported in the same time-frequency space. As a result, a good understanding of channel correlation effects is crucial to optimizing MIMO performance and requires a means of accurately testing MIMO components (e.g., receivers) and systems under real-world conditions and channels. Unfortunately, conducting such testing directly in a “real” wireless environment is neither effective nor practical due to factors like channel sensitivity and mobility requirements. Specialized equipment that is well suited to address this challenging test environment is now offering today’s R&D engineers the best hope for fully understanding and dealing with the various channel effects that may arise.

Back to Basics
To better understand the challenge created by channel correlation effects, it’s first necessary to review how and why MIMO works. In wireless communications systems, multiple antenna systems like MIMO take advantage of the spatial characteristics of the wireless channel obtained by placing separate antennas in a dense multipath scattering environment. MIMO uses multiple transceivers at both the transmitter and receiver to create multiple independent signal paths that can be recovered inside the receiver.

In MIMO terminology, the “Input” and “Output” are referenced to the wireless channel, which exists between the antennas. Performance gains are achieved as multiple transmitters simultaneously input their signal into the wireless channel and then combinations of these signals simultaneously output from the wireless channel into multiple receivers.

Several basic configurations for connecting the transmitters and receivers in a wireless system are shown in Figure 1. They include Single-Input Single-Output (SISO), Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO), and MIMO. In the figure, each individual colored arrow represents multiple signal paths between two antennas (including the direct Line of Sight (LOS) path if one exists), and the numerous multipath signals created from reflection, scattering and diffraction from the surrounding environment. In the 2x2 MIMO configuration, each transmitter antenna has two separate transmit channels, and each receiver antenna has two combined receive channels. Numerous other MIMO configurations using combinations of multiple antenna pairs, such as 3x3 and 4x4, are also possible. A MIMO system can even be configured with an unequal number of antennas at the transmitter and the receiver.

Uses for MIMO Systems
MIMO systems can be implemented either to combat signal fading or to improve capacity. Generally, there are three categories of multiple antenna techniques, including spatial diversity, spatial multiplexing and beamforming.

Spatial Diversity — A technique aimed at improving power efficiency by minimizing re-transmissions. It relies on such methods as delay diversity, Space-Time Block Codes (STBC) and Space-Time Trellis Codes (STTC).

Signal power in a wireless channel fluctuates rapidly over time and distance due to the rich multipath environment. When the signal power drops significantly at the receiver, the channel is said to be in a multipath fade. Diversity is often used in wireless channels to combat this fading effect. Antenna diversity combats fading by combining copies of signals from two or more independently faded channels. For example, in a SIMO system, receive antenna diversity will improve system performance when the receiver optimally combines signals from separate antennas so that the resultant signal exhibits a reduced amplitude variation when compared to the signal amplitude from any one antenna. Diversity is characterized by the number of independently fading channels, also known as diversity order, and is equal to the number of receive antennas in a SIMO configured system. It is important to note that if the fading channels are not independent, then antenna diversity may not improve the system performance.

Transmit diversity is applicable to MISO channels and has become an active area of research. If the channels from each transmit antenna to the single receive antenna have independent fading characteristics, then the diversity order is equal to the number of transmit antennas. If the transmitter does not have prior knowledge of the channel characteristics then a suitable design of the transmitted signal is required to achieve diversity gain at the receiver. One popular transmit diversity technique which has recently garnered attention is Space Time Coding (STC). This technique sends the same user data to both transmit antennas, but at different times, to improve the probability of successfully recovering the desired data. It effectively encodes the data in both space and time.

A simplified block diagram using Alamouti STC is shown in Figure 2. In this system, two different symbols are simultaneously transmitted from the two antennas during any symbol period.

Note that the STC diversity technique does not improve the system data rate, but rather the signal quality. The sequence shown in Figure 2 uses encoding performed in space and time (space–time coding). The encoding may also be done over the space and frequency domains. In this case, instead of two consecutive symbol periods transmitted from two separate antennas, two frequency carriers may be used (space–frequency coding).

Diversity in MIMO channels is a combination of the transmit and receive diversity described above. The diversity order would then be equal to the product of the number of transmit and receive antennas if the channel between each transmit-receive antenna pair fades independently.

Spatial Multiplexing — This technique uses spatial multiplexing, defined as MIMO, where independent data streams are simultaneously transmitted over different antennas to increase the effective data rate. Spatial multiplexing can offer an increase in the transmission rate while using the same bandwidth and power as in a traditional SISO system. The theoretical increase in capacity is linearly related to the number of transmit/receive antenna pairs added to the MIMO system. In configurations with an unequal number of antennas at the transmitter and the receiver, the capacity improvement is proportional to the smaller number, M or N.

Spatial multiplexing can also be applied in a multiuser format, known as Space Division Multiple Access (SDMA). Consider two mobile users transmitting their individual signals over the same wireless channel. Both signals arrive at a base-station equipped with two antennas where they are separated using spatial multiplexing. The increase in capacity is proportional to the number of antennas at the base-station or the number of mobile users, whichever number is smaller. While the user does not experience this capacity increase, the Service Provider benefits by having more users in the same space. This technique has been defined in the WiMAX Wave 2 standard and is termed Uplink Collaborative Spatial Multiplexing (UL-CSM).

Spatial multiplexing can only increase transmission rates when the wireless environment is very rich in multipath. Such an environment results in low correlations between the channels, making data recovery possible at the receiver. When the channels are highly correlated, the spatial multiplexing performance rapidly degrades

Beamforming — This technique exploits knowledge of the channel at the transmitter, also known as beamforming. In a traditional beamforming application, the same signal, or data symbol, is simultaneously transmitted from each antenna element following the application of a complex weight (magnitude and/or phase) to each signal path in order to “steer” the antenna array for optimal signal-to-noise ratio (SNR) over the wireless link.

In a beamformer optimized for spatial diversity or spatial multiplexing, each antenna element simultaneously transmits a weighted combination of two data symbols. This beamforming technique requires knowledge of the channel characteristics at the transmitter. It utilizes this information to build the beamforming (pre-distortion) matrices as pre- and post-filters at the transmitter and receiver to achieve capacity gain. In this case, it may be required to measure the channel at the receiver and send information back to the transmitter.

Evaluating the Challenge
Because the wireless channel plays such an integral role in the implementation of MIMO systems, fully understanding any effects that may adversely impact it is vital. Spatial diversity and spatial multiplexing can substantially improve performance, but only if the spatial dimension is properly configured to leverage the richness of the multipath environment. In the case of spatial diversity, the diversity gain achievable using STC is dependent on the channel diversity order. The channels between each transmit-receive antenna pair must fade independently for the channel diversity order to be equal to the product of the number of transmit and receive antennas. Alternately, if the channels between transmit-receive antenna pairs are highly correlated, then the achievable diversity gain is very limited.

Low-correlation channels are also required in spatial multiplexing MIMO applications. The different spatial signal streams can be well separated only under favorable channel conditions. This often requires proper positioning of the transmit and receive antennas in order to provide low channel-to-channel correlations between the antenna pairs.

Mitigating Channel Effects
While techniques like spatial diversity and spatial multiplexing provide a viable avenue for improving performance in the face of channel effects, they do not fully address the problem.

Various approaches can be used to accomplish this task. In a typical 2x2 MIMO configuration, for example, two separate SIMO channel emulators can be used to model the four separate channels that exist between the pairs of transmit and receive antennas. But SIMO channel emulators do not provide the correct correlation between MIMO channels — an important characteristic when testing system performance since real-world channels are correlated to some degree. The engineer might opt to test directly in a “real” wireless environment, but the channel is very sensitive, not controllable and not repeatable. This approach also is not practical in test situations where different environments are required or when mobility testing is necessary. Another option is to use software-based tools to create realistic MIMO channels, a time-consuming proposition that does not produce real-time results, although it does provide some indication of the correct operation of the RF and baseband functions.

Specialized instrumentation that emulates realistic MIMO channels provides the best solution for addressing these challenging test conditions. A channel emulator, such as the N5106A PXB MIMO Receiver Tester, which replicates real-world MIMO conditions using powerful digital signal processing technology, makes it possible to rapidly isolate performance issues early in the design, development and verification cycle (see Figure 3). The channel emulator also has the advantage that it can generate realistic fading scenarios including path and channel correlations, has a lower implementation cost and a faster calibration process.

Figure 4 shows a simplified configuration diagram for testing a 2x2 MIMO receiver. Here the measurement instrument is connected with two RF signal generators for signal upconversion. The instrument’s internal baseband generators create the standards-compliant waveforms such as WiMAX, LTE and WLAN signals. These baseband generators are easily connected to the channel faders through a software graphical user interface. Each fader can be independently configured with a standards-compliant fading model, such as a WiMAX ITU Pedestrian B, or custom configured model using a variety of path and fading conditions. In contrast to stand-alone faders, the instrument’s automated power calibration eliminates the tedious, time-consuming system setup required for fading.

Conclusion
Realizing the promise of MIMO’s application in wireless communication systems requires accurate testing of MIMO components and systems in a real-world environment. A specialized instrument like the PXB MIMO Receiver Tester provides engineers an ideal solution, offering a fast, accurate and scalable way to replicate real-world conditions and channels and perform real-time fading of MIMO signals. Not only do such capabilities allow the engineer to accurately isolate issues early in the lifecycle, but also help minimize design uncertainty, equipment and lab setup time and cost, while maximizing the performance and scalability needed to meet future test needs. As a result, such specialized test equipment is quickly becoming a critical tool for any R&D engineer developing and integrating MIMO components and systems.

About the Author
Wilkie Yu joined Agilent Technologies in 2000 and has held various positions in marketing, including sales development lead engineer for Asia and market development manager of China.  He is currently the Agilent PXB platform lifecycle manager.  Wilkie holds a bachelor of science degree in electrical engineering and computer science from UC Berkeley.

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