Wireless MIMO test development strategy
Introduction Limited bandwidth and the increasing demand for new wireless services have opened the way for the adoption of new technologies in the communications field. These non-traditional technologies have effectively increased data capacity. One of these newly adopted technologies is a multi-input, multi-output (MIMO) system architecture that uses multi-antenna design. MIMO utilizes the spatial diversity technology between the transmitting and receiving antennas—the generation of multiple signal paths caused by signal fading and multipath environments—to increase data throughput without additional bandwidth. However, compared with the traditional single-stream architecture MIMO, the system complexity has increased a lot, which has brought greater test challenges and requires unique equipment and test methods.
This article introduces the different types of MIMO measurement, including the damage of noise and interference to the channel, and provides some picture examples for everyone to understand the measurement results.
For the latest wireless communication standards, high data throughput is the most basic requirement. These new standards MIMO are involved, including IEEE 802.11n WLAN, IEEE 802.16e mobile WiMAX Wave 2 and 3GPP Long Term Evolution (LTE). These new systems combine the use of MIMO and OFDM or OFDMA (Orthogonal Frequency Division Multiple Access) to achieve increased data throughput without increasing channel bandwidth.
SISO and MIMO are compared in the traditional single input, single output (SISO) communication system (as shown in Figure 1a), for example, the traditional IEEE 802.11a / b / g wireless local area network (WLAN) system, a wireless link is used Single transmitter and single receiver. Multiple antennas may be used on each communication link terminal, but only one set of antennas is used at the same time, and only one carrier transmits a single stream of data. In an ideal communication channel, wireless signals are only transmitted through a single path from the transmitter to the receiver, but obstacles (such as buildings and various terrains) and movement effects in the wireless channel produce multipath effects. Received multiple signals. The reflected signal will be affected by attenuation and delay because it has a longer propagation path than the directly transmitted signal. Because of the different transmission paths, the phases of these reflected signals are also different. Therefore, the reconstruction of the receiver signal is difficult, which will cause the fluctuation of the received signal strength. Strong multipath effects can reduce throughput or cause data loss.
Figure 1 Traditional SISO architecture wireless signal link (a), using a pair of antennas to transmit and receive at the same time while MIMO system (b) uses multiple signals and multiple antennas at the same time
Because OFDM is usually combined with MIMO to enhance data throughput in a given communication channel, it is important to understand OFDM before discussing the concept of MIMO. For example, OFDM is adopted in IEEE 802.11g (Wi-Fi) and IEEE 802.16e WiMAX systems. Based on MIMO, the use of OFDM can further improve data throughput without increasing bandwidth or changing the modulation order-for example, from 16QAM to 64QAM system.
OFDM-modulated wireless signals are essentially composed of a series of mutually orthogonal sub-carriers, which form the best isolation from each other, so when a modulated sub-carrier is at maximum power, it is close to the modulated sub-carrier It is right at the zero-crossing point or where the power is minimum, and some sub-carriers are used as guard bands to achieve isolation and prevent adjacent channel interference. To enhance robustness, OFDM used in many communication standards uses a small attenuation interval to allow multiple signal components to decay over time, so that these signals will not interfere with the transmitted symbols received by the next receiver.
By using inverse Fourier transform to perform digital signal processing on OFDM sub-carriers, it can be combined into a signal stream for transmission and the original signal can be restored. Because the relative phase and frequency relationship of multi-stream signals is retained, these signal streams can be transmitted in parallel on a single channel, so that data throughput can be improved without increasing bandwidth.
Compared with the SISO communication system, the MIMO system (Figure 1b) uses multiple wireless signals and multiple antennas at the same time, and multiple data streams are transmitted on the same communication channel. These multiple data streams are coordinated by the media access control (MAC) layer at both ends of the communication link. MIMO systems do not require a symmetrical arrangement of antennas. For example, two transmitters must be equipped with two receivers (2 × 2) or four transmitters must be equipped with four receivers (4 × 4). You can perform “unbalanced†configurations, such as four The transmitter is equipped with three receiving 4 × 3 configurations.
To increase the data throughput of the SISO system, a more complicated modulation method is required, or the bandwidth is increased, or a combination of the two is performed. The easiest way to double the throughput of the SISO system is to double the bandwidth. To increase the throughput of a MIMO system, the number of transmitters, receivers and corresponding antennas needs to be increased. By using spatial multiplexing technology with multiple antennas and signal propagation paths, MIMO systems can increase throughput by approximately 3.5 times without increasing channel bandwidth.
MIMO systems use changes in received signals to increase data throughput. The received signals are treated as simultaneous equations of unknown signals (symbols transmitted). The diversity of multiple signal paths makes these simultaneous equations easier to solve and improves throughput.
How does SISO's channel capacity compare to MIMO systems? Shannon ’s Law specifies the channel throughput of the SISO communication system as
C = BLog2 (1 + S / N)
Where: C is the channel capacity (unit b / s), B is the channel bandwidth (unit Hz), S is the total signal power (unit W or V2) over the bandwidth, and N is the total noise power (unit W or V2). When this formula is used for MIMO applications:
C = ABlog2 (1 + S / N)
Where: A is the number of transmitting antennas.
This equation points out the direct relationship between the number of transmit antennas and the channel capacity in a MIMO system. A MIMO system uses spatial multiplexing technology to transmit multiple data streams with multiple antennas on the same physical channel. The data streams are transmitted on multiple transmitters without changing the symbol rate. By adding more transmitters and transmitting antennas, the throughput of the system is improved without changing the bandwidth.
Modeling a MIMO system must consider the number of multiple data streams, including the direct and reflected signals arriving at the receiver. According to the traditional method, the transmitter is represented as Tx1, Tx2, ..., Txn, and the receiver is represented as Rx1, Rx2, ..., Rxn, a MIMO communication system can be represented by a matrix signal vector hxy, where x represents the transmission The number of machines, y represents the number of receivers. For example, h21 represents two transmitters and one receiver, and h22 represents two transmitters and two receivers (as shown in Figure 2). In this way, a MIMO channel can be modeled like this:
y = H * x + n
Where: y is the received signal vector, H is the channel matrix (hxy signal element), x is the transmitted signal vector, and n is the noise vector.
Figure 2 The wireless channel in a MIMO system can be represented by a series of different vectors
Different channels have an effect on the received signal, for example, the attenuation and multi-warp effects can be corrected by the same algebraic equation, the relationship is
Rx = H * Tx + n
Where: Rx represents the Rx1, Rx2, ..., Rxn matrix of the receiving antenna, and Tx represents the Tx1, Tx2, ..., Txn matrix of the transmitting antenna. For a 2 × 2 MIMO system, the relationship is shown in the matrix of FIG. 2.
The signals in these relations contain amplitude, frequency and phase components, so it is practical to use vector representation. In simple terms, it is also practical to use vectors to represent these signals in a measurement system.
Measurement challenges
The increase in data throughput of MIMO technology increases the complexity of the system and brings new design challenges to test and measurement equipment for evaluating MIMO systems and components in the system. Before deciding on the best MIMO measurement instrument, it may be necessary to determine a measurement type that describes the performance of the MIMO communication channel. MIMO measurement can generally be divided into system-level measurement, channel response measurement, and functional measurement of components used in MIMO systems.
It has been explained that the MIMO signal is defined by frequency, amplitude and corresponding phase components. The measurement of the MIMO signal must accurately and truly determine the above three signal characteristic components. In addition, MIMO systems are usually based on zero-IF down-conversion of received signals to baseband I and Q signal components. To obtain high modulation accuracy, the fidelity of the I and Q signal components must be maintained. This requires all components of the signal path to have high performance and low distortion, including amplifiers, filters, mixers, I / Q modulation and demodulation And other components.
In many wireless systems, error vector magnitude (EVM) is a standard parameter for evaluating performance and is widely used in MIMO systems. EVM is usually considered to be the error of the received signal constellation (RCE), because RCE is intuitively displayed in the constellation, RCE is actually the vector difference between the ideal signal and the measured signal, and can be used as the MIMO transmitter modulation accuracy and Direct measurement of signal quality and receiver performance. The EVM measurement captures signal amplitude and phase errors and reduces many parameters that define the distortion of the transmitted RF signal to a single parameter, allowing comparison between individual transmitters. Other important MIMO transmitter tests include evaluation of group delay and changes in group delay, phase noise, amplification and compression, and component I / Q mismatch in signal processing. The signal distortion caused by the above factors can generally be seen through the EVM on the constellation diagram.
In the constellation diagram EVM, for an ideal signal, all constellation points should exactly coincide with the ideal position. But the signals and components are not perfect. Factors such as phase noise and carrier leakage can shift the constellation points on the constellation diagram from the ideal position. EVM is the measurement of this offset. In addition to the overall EVM as a MIMO system test parameter, EVM as a frequency and EVM as a time function can also provide analysis of MIMO transmitter performance. In addition, the comparison of the carrier and symbol displayed by the EVM can provide further details of the performance of the MIMO transmitter.
The precise point positioning on the constellation EVM shows the performance of an excellent MIMO system. In a 2 × 2 MIMO system using OFDM and 64QAM, colors are used to distinguish different transmitter signals and pilot carriers. In the constellation shown in Figure 3, the red and blue dots represent the two signals in the 2 × 2 MIMO system, Tx0 and Tx1, which are overlaid on the white dots. The white dots represent the ideal positions of subcarriers. The yellow dot represents the pilot carrier and coincides with the white dot representing the ideal pilot carrier.
Figure 3 The EVM constellation diagram provides a schematic diagram of potential MIMO system problems. These problems include noise (fuzzy dots), I / O imbalance (offset dots), and phase noise (dots become circles)
Such color-defined charts make it very easy to locate transmitted signal problems. For example, if the red or blue subcarrier constellation points deviate from the ideal white point, it indicates I / Q imbalance, and if the constellation points are blurred, it indicates that the transmission signal is noisy, and the constellation points are circular, which means too Phase noise.
Along with the more common XY plots, a series of channel measurements shows the health of the plot matrix and signal matrix of the relative subcarriers in the MIMO system. The measurement of the system capability of channel inversion and symbol transmission in FIG. 4 can be used to determine the orthogonality of each signal stream in a MIMO system. By transmitting inverted symbols, the coverage of the system can be analyzed, and by transmitting parallel symbols, the system throughput can be evaluated.
Figure 4 XY diagram shows the orthogonality of the MIMO channel subcarriers, indicating the subcarrier situation
The channel response measurement shows the flatness of the subcarrier, which is the subcarrier. For example, a measurement on an IEEE 802.16e OFDM channel (shown in Figure 5), the green trace shows the power of the signal from the first transmitter (Tx0) to the first receiver (Rx0); the red trace above The power of the signal from the second transmitter (Tx1) to the second receiver (Rx1); the blue trace shows the power of the signal from the first transmitter (Tx0) to the second receiver (Rx1); The red trace below shows the power of the signal from the second transmitter (Tx1) to the first receiver (Rx0). The power level of the corresponding subcarrier indicates the channel flatness, and the difference between the main signal and the indirect signal shows the channel isolation (less than 40dB in the legend). These measurements are made by directly connecting the transmitter and receiver with coaxial cables.
Figure 5: By directly connecting the MIMO transmitter and receiver, the channel flatness and channel isolation can be evaluated. The example is a 2 × 2 MIMO system
A series of measurements in the time and frequency domains can show that MIMO performance will change in different situations. For example, EVM measurements corresponding to OFDM symbol time can indicate interference problems or performance changes over time. The EVM measurement of the corresponding subcarrier can be used to analyze the effects of in-band noise, for example, glitches. The power measurement for OFDM symbol time can separate the in-band amplitude deviation. Frequency measurement for OFDM symbol time can be used to check the frequency accuracy and isolate the problem of frequency drift within a period of time in a packet.
Hardware Construction The test system for MIMO measurement must accurately simulate the operation of the MIMO system, can generate the required signal frequency, amplitude, and phase, and can capture and analyze the signal in the test equipment (DUT). The test system must support the modulation format used and all the modulation bandwidths under test. For the test signal generation process, an arbitrary waveform generator or vector signal generator (VSG) needs to provide control of the actual test signal, and a vector signal analyzer (VSA) can be used as a test receiver. All test systems designed for MIMO systems should be able to provide the number of test signal sources and signal analyzers required for matching the number of transmitters and receivers, and should also be able to meet future upgrade requirements. For example, the MIMO test system provided by Keithley can be upgraded from a single VSG and VSA to an 8 × 8 channel system, and the signal source and analyzer can be flexibly configured in that range.
If the synchronization of multiple signal sources and analyzers is the most basic in MIMO measurement, then these instruments also need an ordinary reference oscilloscope. For example, in Keithley Corporation's 2 × 2 MIMO measurement system shown in Figure 6, VSA and VSG devices require special synchronization components. These components provide some common signals, such as local oscillation, common clock and precise triggering, provide low sampling and RF carrier phase jitter, which is very necessary for accurate and repeatable measurement of OFDM MIMO signals. In particular, the sync component provides peak-to-peak jitter below 1 °.
Figure 6 This MIMO test system is based on a multi-channel vector signal generator (VSG), vector signal analyzer (VSA), and computer-controlled synchronization components and custom measurement software.
The effective and simple use of the MIMO test system also depends on the system's test software. With the continuous adoption of MIMO technology in wireless communication systems, off-the-shelf test software (off-the-shelf test software) is widely used in simplifying system and channel measurement. For example, Keithley ’s SignalMeisterRF communication test kit software Model 290101 provides complex signal generation and signal analysis capabilities for MIMO applications such as WLAN 802.11n and WIMAX 802.16e Wave 2. This software package works seamlessly with Keithley ’s VSG, VSA, and MIMO synchronization components to build a complete measurement system for complex communication systems. In addition to EVM and MIMO channel response measurements, the software can also respond to SISO system evaluations.
The tests and measurements we currently discuss can be used to evaluate the performance of MIMO communication systems and components in the system under ideal conditions. But what about the performance of the MIMO system when the signal is weak? In this case, different types of test types are required, for example, a channel simulator. It provides analysis methods for MIMO systems and components in the case of channel impairments. These impairments include signal attenuation, Gaussian white noise (AWGN), channel crosstalk, and even Doppler effects—usually by in-vehicle communication terminals for mobile produce.
The channel simulator must act as a transmitter and receiver in a MIMO system, and must also have the ability to simulate real-world environments such as weakening signals and increasing delay. A qualified channel simulator also provides software-defined radio modules, such as ITU M.1225 A and B in WiMAX. A practical channel simulator must exceed the performance of the system under test and provide the capability for production testing when needed. The simulator also needs to have a bidirectional function so that it can provide both uplink and downlink testing. By additionally providing reciprocal calibration tests (calibrated reciprocal tests), the simulator is very useful for testing MIMO systems using beamforming technology. Finally, although the examples given in this article are for 2 × 2 MIMO systems, an effective channel simulator can also support 4 × 4 MIMO systems to achieve complete support for various MIMO systems. For example, Azimuth Systems' ACE 400WB channel simulator is a bidirectional component that supports 4 × 4 MIMO system testing.
What suddenly appear into your mind when hear Mini Gaming PC? Is there quality heat-releasing fan and box design of Mini Gaming PC Build? Does it run stable and smoothly when handle heavier jobs, like Photoshop, Pr, engineering design and drawing, 3d Max, big games, etc. ? now we can be responsible to answer [ Yes". No matter cheap mini gaming pc or Mini Gaming PC Under $500, equips with quality fan to heat releasing. Therefore, no need to worry that again.
To processor, can do from intel celeron j4125, N5105 up to i3, i5 i7 10th 11th 12th with or without video graphics. To slots, same rich as traditional computer tower, like above 4 or 6 usb ports, 2 Rj45, VGA, PD, slot, etc.
You can also find Mini Gaming PC, j4125 Mini PC ,Custom All In One PC, windows 10 Education Laptop,Quad Core Processor Laptop, 15 Inch Gaming Laptop ,windows Yoga Laptop , 8 inch Android Tablet, etc.
If any other special requirements interest, you can also let us know, will try our best support you.
Meet your unique demand in this field is our mission, so just feel free contact us whenever you have different idea.
Mini Gaming PC Build,Cheap Mini Gaming PC,Mini Gaming PC Under $500,Mini Gaming Desktop
Henan Shuyi Electronics Co., Ltd. , https://www.sycustomelectronics.com