The continuing goal in today's data-centric wireless technology is to improve data rates and fidelity in the most cost-efficient way. Recent advances in antenna technology have introduced wide-scale deployment of antenna arrays used in multiple-in, multiple-out (MIMO) configurations.
Next-generation wireless technologies such as WiMAX, Long-Term Evolution (LTE), and Ultra Mobile Broadband (UMB) all make use of multiple-antenna-array technologies. Now they also can utilize beamforming, a more advanced technique.
Beamforming is a signal-processing technique that requires estimating channel information and adaptively shaping beams to enhance some signals and suppress others. To implement beamforming, three basic techniques are used: adaptive beam steering, maximum ratio transmission (MRT), and Eigen beamforming (EBF).
Adaptive beam steering can be implemented when the mobile device has a single antenna; MRT and EBF are MIMO beamforming techniques. All three methods can dynamically adjust the relative amplitude and phase, the antenna weights, on each element of the base-station antenna array. Table 1 compares and contrasts the three approaches.
Table 1. Beamforming Techniques
The result is that lobes of constructive interference or beams can be created and steered on the transmitted link. On the received link, beams can be created and aimed toward desired users while nulls can be steered toward sources of unwanted interference. Figure 1 shows the basic concept of a beamforming system.
Figure 1. Adaptive Beamforming System
Adaptive Beam Steering
In single-user-antenna beamforming, the mobile device periodically sends a channel sounding signal. Each base-station antenna element receives this signal at a slightly different phase offset and amplitude so that algorithms can estimate direction-of-arrival (DoA) for each user based on a unique spatial signature.
The system then adjusts its excitation to the array by carefully controlling the antenna weights. The algorithms used to calculate antenna weights are called adaptive antenna algorithms, and the technique is called adaptive beam steering.
The accuracy of DoA estimation is critically important but can be impaired when reflective scatterers near the user enlarge the signal's angular spread (AS). Signals scrambled by scatterers may not add coherently at the user's antenna so beamforming gain is significantly weaker. This makes single-user antenna beamforming most suitable for line-of-sight (LOS) environments with small AS.
MIMO Beamforming
In addition to existing open-loop MIMO, MIMO beamforming or closed-loop MIMO is another technique using multiple antenna arrays. The term MIMO implies that there are multiple-antenna arrays at both the base station and the mobile device.
Using MIMO techniques means that base stations must track a channel characteristic called channel correlation. This parameter, actually a complex matrix used to describe the connection, quantifies the relationship between each path from each transmitting antenna element to each receiving element. As a very rough definition, high correlation between two links in a MIMO channel means that the links will suffer the same effects from environmental conditions.
Nonbeamforming MIMO is well suited to channels with low correlation because an impairment on one link may barely affect a different link. MIMO beamforming works best on channels with high degrees of correlation.
MRT is designed to maximize the signal-to-noise ratio (SNR) at the receiver. While adaptive beam steering only uses directional information, MRT requires more accurate channel data. To calculate MRT antenna weights, the base station needs to very accurately estimate the channel correlation coefficients.
MRT maximizes SNR and outperforms adaptive beam steering at large AS. However, the wideband OFDM-based signals used in WiMAX and LTE are made up of numerous subcarriers at slightly different frequencies. Since MRT has to compute antenna weights for each subcarrier, it is mainly suitable to stationary or low-speed applications.
In contrast, EBF relies only on the statistical characteristics of the channel. Here, antenna weights are calculated for an entire band rather than individual subcarriers.
While it is suboptimal, EBF has smaller measurement delay and requires less frequent measurements. It outperforms MRT in applications with high mobile velocity and low SNR. As EBF only adapts to significant changes in the environment, updates take place only rarely, such as when a mobile user moves from a rural to a suburban area.
As a result, spatial beams can be created and aimed to more accurately transmit and receive information. Just as the code and frequency domains can be used to add apparent gain to desired connections and mitigate the effects of interferers, beamforming lets systems use the spatial domain for the same purposes.
Testing Base Stations Under Mobility Conditions
Mobility testing ensures base-station performance characteristics during operation with moving wireless terminals. Without mobility testing, base-station design-verification testing (DVT) inadvertently adds a bias toward unrealistic static receivers.
Specialized pieces of test equipment called channel emulators are required since an emulated channel is more controllable and more repeatable than field-testing conditions.
As mobility testing is applied to beamforming base stations, the industry has discovered that new test requirements are needed to maximize coverage potential and minimize operational costs. Early investigation also points out critical parameters in both the system under test and the test equipment being used.
Because beamforming is a function of the physical radio link, the best approach to developing a test plan is to use a bottom-up methodology. At this level, there are two types of tests that serve different purposes but have equal importance:
• Functionality verification, which ensures that the beamforming system works as fundamentally designed. This kind of testing is an effective base line and can be performed under artificially favorable conditions.
• Performance evaluation, which is intended to evaluate and quantify performance. It helps engineers understand how well a system works under realistic or adverse conditions.
The goal may differ depending on the tester's field of interest. For example, network operators may use performance evaluation tests to plan network deployment and quantify the variability in performance of a single link in the system chain. Base-station manufacturers may want to plan feature roadmaps or identify points of product differentiation for marketing campaigns. System developers need to evaluate trade-offs between system complexity and performance. These separate groups, each with different goals, all require the same kind of information to be effective.
Location and tracking become much more difficult under mobility conditions. Accordingly, one key challenge in the development of beamforming base stations is to develop effective, efficient algorithms for DoA estimation and adaptive beamforming. Most systems work well in static scenarios, but performance in realistically dynamic scenarios differentiates well-engineered systems from mediocre ones.
These technical differences directly affect both the base-station and the operator markets. Base-station manufacturers that deliver performance under extreme conditions can adopt premium pricing and still provide economic advantages via increased range to their customers. For network operators, delivering differentiated performance under dynamic conditions leads to word-of-mouth marketing which directly translates to lower churn and increased subscriber adds.
These challenges in development lead to challenges in testing. Thorough base-station testing must stress these critical algorithms in the contexts of adaptability to subscriber motion, change of channel conditions, and multi-user processing.
Adaptability to Motion
A subscriber motion relative to the antenna array results in a variation in both DoA and Doppler shift. After the DoA is estimated, the base station must compute antenna weights and apply them to the antenna array to steer the beam. Again, this is something of a challenge in static systems, but a dynamic system adds the classic feedback-control-system problem of having to accomplish this within a workable timeframe.
If DoA is not estimated accurately or the beam is not able to track the moving subscriber, beamforming gain cannot be optimized. In the worst case, the communications link will be lost because the subscriber is moving faster than the array can track or react.
The impact of DoA error on beamforming gain is significant. In Figure2a, a radiation pattern of an eight-antenna uniform linear array is shown with a DoA error of ε degrees. Figure 2b shows that an error of 8 degrees results in 10-dB loss of beamforming gain and that an error of 14 degrees could cause the loss of the link.
Figure 2. Phase Error and Its Impact on Beamforming Gain
Adaptability to Channel
Mobility implies not only the change of geometric parameters, but also a change in propagation environment. The degree of randomness in DoA is a function of both the subscriber's motion and the environmental conditions. Motion causes variability in reflections that is much more complex than the variability in LOS signals. As a result, the estimation of DoA at the base station becomes much more difficult compared with the stationary case.
In an open environment such as a rural area, there are few scatterers near the user. As a result, the uplink signal arrives at the antenna array with small AS. In addition, the channel exhibits very high path-to-path correlation with few variations so it is relatively easy to deliver coherent signals to the user. DoA estimation is relatively easy and accurate using adaptive beam steering.
As the user moves into an environment with more numerous and significant scatterers, the uplink signal arrives at the base station with larger AS. If the user is moving relatively slowly, the channel becomes a candidate to use MRT. If either condition is not met, then EBF can be used to deliver even higher system gain than MRT.
To reap the optimal beamforming gain, it is critical for beamforming base stations to be aware of any change in channel characteristics and use the appropriate beamforming scheme. Testing the beamforming system's adaptability to the channel is an integral part of understanding the base-station's performance characteristics.
Multi-User Scenarios
One goal of beamforming is to increase system capacity. As a result, a suitable plan must include testing system performance in multi-user scenarios, which requires more complicated signal processing and faster processing speed than single-user cases.
Two basic multi-user beamforming tests are depicted in Figure 3. In the first case, both mobiles are desired users that should see enhanced signals from the base station. The base station must locate and track both mobiles and compute antenna weights as before, but now the base station must form a radiation pattern with two steered beams.
The second case creates a steered beam for the user but adds a steered null. This tests the system's capability to suppress interferers while continuing to enhance the signal to the desired user.
Adaptability to both motion and channel must be accounted for and stressed in test plans for beamforming base stations. Failure to address these topics will undoubtedly cause issues upon deployment.
Considerations in Test Systems
Test equipment must never inadvertently affect testing, and the testing of beamforming base stations under mobility conditions imposes even more stringent requirements on the equipment being used. Two critical areas of concern are the RF performance and the dynamic control of channel parameters.
RF Performance
Amplitude balance among RF channels, phase calibration accuracy, and stability are of primary importance to the validity of beamforming performance tests and even more critical in beamforming testing than in testing traditional wireless technologies.
Beamforming base stations use sophisticated signal processing algorithms to locate users and form radiation patterns. The effectiveness of these algorithms relies on channel reciprocity or symmetry between the uplink and downlink channels for each transmit/receive antenna pair, the phase difference between antenna elements, and signal amplitude. If test equipment introduces significant error in any of these areas, the equipment can seriously and misleadingly degrade the performance of good algorithms.
Any good channel emulator will have a phase calibration routine. Before performing calibration, the system should be powered up and allowed to stabilize for a defined period of time. This is good practice whenever a channel emulator is used, but it is absolutely critical when testing mobility scenarios with beamforming.
A less obvious but more insidious effect is phase drift in the equipment. Phase stability is a function of time, temperature, and humidity, but it can be controlled with proper test equipment design. Certain techniques and choices made in the mechanical design of a channel emulator will affect how the emulator reacts to environmental conditions. An RF emulator can be electrically designed to counteract phase drift.
At a basic level, amplitude imbalance is a function of the test equipment's overall output accuracy. However, the test equipment should be designed so that accuracy between RF paths is an order of magnitude better than spec since the slightest error here also can have a significant effect on results.
Dynamic Channel Control
Mobility testing with beamforming implies dynamic control of the channel emulation to test the system's adaptability to motion and channel. Two aspects of dynamic channel emulation must be addressed to test the beamforming base-station's performance under mobility conditions: dynamic change of DoA, which simulates the movement of the user, and the dynamic simulation of channel correlation to mimic the effect of environmental changes on propagation.
Dynamic DoA Simulation
To simulate DoA changes in a channel emulator, the phase of each radio link connecting each mobile antenna element to each base-station antenna element must be carefully calculated and accurately adjusted. Accuracy of phase control is critical, but so is the speed at which the angle can be adjusted. Usually, the transition of the phase change on both uplink and downlink temporarily breaks channel reciprocity, but a channel emulator must minimize the time spent applying phase changes.
Typically, these phase changes are applied sequentially, but a better practice is to change all of them simultaneously. For example, the Spirent SR5500 Wireless Channel Emulator uses a proprietary approach to realize simultaneous phase changes on all radio links. Not only is transition time minimized, but it also is independent of the number of antenna elements.
Dynamic Channel Correlation Simulation
To test the beamforming system's adaptability to the channel, the channel emulator must simulate a change of channel characteristics. Transition from small AS to large AS translates into a change in the channel correlation matrix. As the user moves from a LOS environment to an area with scatterers, the channel correlation matrix varies from full correlation to high correlation; the larger the AS, the lower the channel correlation.
Either MRT or EBF beamforming can be tested easily under static conditions, and the requirement to test under changing conditions is an obvious one. It also is necessary to test under conditions where the environment forces changes in the technique being used. Test plans should include forcing the system to change from MRT to EBF and back.
MRT is a better beamforming scheme for a mobile device moving at low speed and using channels with high correlation. As a subscriber accelerates, MRT gives way to EBF. While using EBF, variation in the correlation matrix tests the EBF adaptability to the channel. Consequently, the channel emulator being used should control dynamic channel correlation simulation in conjunction with real-time mobile velocity control.
Summary
Beamforming has the potential to add great value to 3.5G and 4G offerings being made by base-station manufacturers and network operators. Just as MIMO offered a quantum step forward over existing technologies, beamforming can add another leap over MIMO in terms of increased data rates, data fidelity, enhanced range, and more efficient resource usage.
With this new technology come new predeployment pitfalls to be avoided. Mobility testing is important in all wireless technologies, but beamforming adds new fine points to be considered when creating mobility test plans and choosing channel emulation equipment.
About the Authors
Kang Chen is a senior applications specialist at Spirent Communications. Prior to joining the company in 2007, Mr. Chen held senior engineering positions at Agilent Technologies and Alcatel. He earned a BEng from Chongqing University of Posts and Telecom, and an M.S.E.E. from Rutgers University where he researched MIMO and cooperative communications. e-mail: [email protected]
Randy L. Oltman is the product segment director for channel emulation products at Spirent Communications. He joined Spirent in 1997 and has led numerous wireless product efforts. Mr. Oltman earned a B.S.E.E. from Rensselaer Polytechnic University and M.S.E.E. and M.B.A. degrees from Rutgers University. e-mail: [email protected]
Michael McKernan is a product marketing manager at Spirent Communications. Before coming to Spirent in 2000, Mr. McKernan spent many years in telecom and communications engineering. He has a B.S.E.E. from NJIT and an M.B.A. from Rutgers University. e-mail: [email protected]
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