Shown are hightly correlated fading effects on four channels (2x2 MIMO) with time.
Multiple input/multiple output (MIMO) is the use of multiple antennas at both the transmitter and receiver to improve communication performance. Performance is the operative word here, in terms of capacity, bandwidth, throughput, range, or a host of other parameters that enhance the user experience.
Advanced wireless communications standards such as Wi-Fi (IEEE 802.11), WiMAX (IEEE 802.16), and LTE (Long- Term Evolution from 3GPP) use multiple transmit and receive antennas in different configurations to squeeze out more performance. All of these emerging commercial communications protocols have one key factor in common: As transmission and reception conditions change, they dynamically use the multiple transmit and receive antennas to achieve different performanceenhancing effects.
Thus, testing MIMO wireless communications takes on a new perspective from previous generations of short- and longrange wireless solutions. That’s because the most important factor is the ability to deliver performance, despite the conditions. Central to this testing is a tool called a channel emulator. It can reproduce dynamically changing transmission conditions to stress the MIMO scenario and ensure optimal performance and interoperability.
MIMO CONFIGURATIONS MIMO exploits two key factors in wireless. First, thanks to semiconductor technology, it’s relatively easy and cost-effective to put two, three, four, or even more transmitters and receivers on a single chip. Second, at microwave frequencies, wavelengths are short (inches), making it easy to space antennas a wavelength or more apart to achieve the effects offered by MIMO.This gives rise to a variety of configurations. The most common is probably 2 × 2 MIMO with two transmitters and two receivers (Fig. 1). Many other configurations are possible, such as 2 × 1, 2 × 3, 3 × 2, 3 × 3, 4 × 4, and other more elaborate schemes. In general, the greater the number of antennas involved, the greater the performance. The configuration decision is based on the type of equipment (basestation, handset, or laptop), the practical side of implementing the number of antennas, and cost.
MIMO TECHNIQUES The broad category of MIMO encompasses three core performance-boosting techniques. Spatial multiplexing transmits different data from each antenna, but on the same frequency and time. The conditions for transmission on each antenna are different, enabling the receiving antennas to split the transitions out to discrete data streams at the receiving end. This technique generally doubles the throughput in the same RF space.Another technique called diversity effectively combats fading. Diversity has two flavors: transmit (Tx) diversity and receive (Rx) diversity.
Tx diversity can transmit the same data on multiple antennas at the same time, but with different coding schemes. The receivers can then decode the streams with more confidence because they can be compared. Also, in many cases, if one stream contains an error, the second will be able to provide the correct data, greatly enhancing the reliability of the data.
Rx diversity captures the data transmitted from a single source on two receive chains. Each chain has a different antenna that may be positioned differently and have different characteristics. So as the two receive signals are added together, there’s a greater chance of the correct error-free data being received.
Finally, beamforming is an advanced technique that can steer the antenna array. Correctly phasing the antennas in the array will reinforce the waves (peaks and troughs will sum). What results is a stronger wave to be received in that line of transmission and a longer transmission in a specific direction. Correspondingly, beamforming can be used to reduce interference by phasing the antennas to cancel signals arriving from undesired transmitters (receive beamforming) or to prevent waves from being received strongly at an undesired receiver (transmit beamforming).
Each of the MIMO techniques will be most effective in different conditions (see the table). The net outcome of these techniques is to overcome the effects of overthe- air transmission and motion to deliver higher throughput, better coverage at the cell edge, and greater range.
CHANNEL EMULATION Before MIMO, traditional wireless testing focused mostly on the quality of the receiver. The better the receiver and its ability to decode the lowest-power signal under the highest noise conditions, the better the system would operate. MIMO takes wireless to new extremes, though, so while receiver capabilities are important, that’s only a part of the system’s ability to deliver performance.Constantly changing conditions in the real world bring new challenges for devices under test. Changing signal strength (amplitude), distance (amplitude and phase), and direction of the transmitter and receiver (angle of arrival and departure), as well as the two devices’ adaptibility to these conditions and ability to implement the best MIMO techniques at any given moment, all impact performance.
In the real world, the ability to operate while in motion is a basic requirement for mobile wireless broadband. But it isn’t easy to recreate it in a test lab so the test is stable, reproducible, and realistic and so the test bed represents the dynamic and changing conditions of a real-world scenario. This is where a MIMO channel emulator comes into play. It delivers three core capabilities, and depending on the quality of the solution, it can provide multiple additional test features:
• Fading: As a mobile device transmits to the receiver, the over-the-air transmission effects and obstructions in its path over the band will cause the signal strength to vary rapidly. Fading is frequencyselective; the signals may momentarily fade high at one point in the band, while a nulling effect (down fade) may simultaneously occur in another part of the signal bandwidth. Since the signal in broadband wireless orthogonal frequency- division multiplexing (OFDM) consists of multiple subcarriers over the band, the frequency-selective fading may negatively affect one carrier and have no effect on another. This scenario, called fast fading, is typically influenced by the type of environment and the speed of the user equipment.
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• Multipath: The signal may pass directly via line of sight from one antenna to the next, but it will also reflect off buildings, vehicles, terrain, and other objects. Consequently, multiple copies of the signal occur with different signal strength and some delay. There’s also some phase difference because of the longer path that’s received at the destination antenna at the same time. Multipath is a result of the environment—an urban and rural environment each may have both multipath and fading, but of different delay and strength.
• Spatial correlation or antenna correlation: Since these antennas are relatively close to one another, their proximity and orientation will also define how similar the signals sent by one and received by two antennas, or sent by two and received by one antenna, will be to one another (or the extent of path correlation). The greater the similarity or diversity there exists between signals, the greater the effect on the ability to decode, sum, or differentiate between the signals at each receive antenna. In most cases, the benefits of MIMO become more evident when the signals are less correlated. Yet creating uncorrelated antennas in a form factor like a handset, for example, isn’t simple.
The channel emulator connects the basestation and the mobile device and recreates these effects digitally to test the system in emulated real-world conditions. Because the channel emulator and the devices under test can be controlled, repeated, measured, and replayed, the setup costs less and is much more effective as a test environment than live field testing.
STATISTICAL MODELING The effects of a single specific area can easily be captured and played back in a channel emulator. This brings some benefit as the test can be performed in the lab, but doesn’t bring out the full potential of channel emulation like that of a statistical model.Research shows that the fading effects and multipath, as well as the correlation of the antennas, can be modeled using Doppler distribution models. Thus, it’s possible to create instances of these models that vary constantly over time, producing hundreds or thousands of representations of possible combinations of frequency-selective fading, multipath conditions, and spatial correlation effects. In applying such a model to the signal passed from a basestation to the mobile station and vice versa, the pair is tested in every possible real-world scenario while never leaving the lab.
Over the years, typical urban, vehicular, pedestrian, and rural (hilly terrain) channel models have emerged. Each technology standards and certification group has selected sets of these models for use in its standard conformance testing. However, scores of different models and instantiations exist, and all can be used as seen fit by testers for their specific performance needs.
What has changed for MIMO testing with respect to single-antenna testing involved correlation matrices defining the relationship between the signals received at each of the antennas. The correlation matrix is defined for each path (and multipath), and it accounts for the antenna spacing, type, and correlation to define the similarity or difference between the signals when they’re combined at the “far end” of the link.
Specifically, the latest commercial MIMO wireless standards like 802.11n Wi-Fi, 802.16e Mobile WiMAX, and 3GPP LTE have adopted sets of MIMOspecific models for testing compliance and performance. Figures 2 and 3 show examples of fading effects.
ADVANCED TESTING CAPABILITIES Beyond the ability to apply channel conditions to the signal in its path from source destination, a good channel emulator will offer much more functionality. Starting from simplicity of use and tools to easily connect, configure, set up, and run the modeling, a world-class channel emulator will include enhanced features to test corner conditions of the real world and evaluate equipment performance in conditions like:• High-speed train conditions
• AWGN noise conditions
• Frequency offsets
• Pure Doppler conditions
• Birth/death scenario conditions
• Moving propagation delay conditions
• Slow fading conditions
The ACE MX supports WiMAX, LTE, and 802.16m nextgeneration broadband wireless technologies as well as 2G and 3G testing. Channel modeling features enable standard channel models or custom designed models to be loaded and run on the real-time, dynamic, digital-signal-processing engine.
The scalable, modular architecture of the ACE MX enables configuration for single and multi-link MIMO. Channel emulation scales for testing single-channel devices up to as much as 8 × 4 MIMO. The scalable design will allow time-division duplex (TDD) or frequency-division duplex (FDD), SISO or MIMO, and unidirectional or bidirectional testing, as well as point-tomultipoint configurations for handoff testing.
CONCLUSION The most critical condition that MIMO wireless adds to the testing mix is the ability to exploit dynamic channel conditions and to use those conditions to increase throughput, enhance range, and deliver higher performance that can truly be called mobile broadband. This places the burden of testing on evaluating performance— and not just being satisfied with conformance certification.To ensure that the devices operate as expected in real-world dynamic conditions, a channel emulator with full MIMO characteristics is a critical component as well as the vehicle toward delivering customer satisfaction. Customer satisfaction will be central to the success of WiMAX and LTE as users watch movies while commuting; conduct conference calls while walking about town; and download presentations while driving to meetings over the MIMO network.