Often, in obtaining the estimates, the impairment (noise or interference) is assumed to be white and Gaussian distributed to simplify the estimation process. However, in wireless-communication systems, the impairment might be caused by a strong interferer, which is colored. For example, in OFDM systems, where the channel bandwidth is wide and the interference is not constant over the whole band, it is very likely that some part of the spectrum is affected more by the interferer than the other parts. Therefore, not only the average CINR, but also, carrier-based CINR and symbol-based CINR are important to provide the quality of the signal received noise for each carrier and for each OFDM symbol.

Since both desired signal's channel and interferer conditions might change rapidly (especially for wireless measurement applications), depending on the application, both short-term and long-term estimates would be desirable. Biterrorrate (BER), symbol-error-rate (SER), frame-error-rate (FER), and cyclic-redundancy-check (CRC) information are examples of the measurements in this category. BER (or FER) is the ratio of the bits (or frames) that have errors relative to the total number of bits (or frames) received during the transmission. The CRC indicates the quality of a frame, which can be calculated using parity check bits through a known cyclic generator polynomial. In FCH decoding, we obtain CRC information. FER can be obtained by averaging the CRC information over a number of frames. In order to calculate the BER, the receiver needs to know the actual transmitted bits, which is not possible in practice. Instead, BER can be calculated by comparing the bits before and after the decoder. Assuming that the decoder corrects the bit errors that appears before decoding, this difference can be related to BER. Note that the comparison makes sense only if the frame is error-free (good frame), which is determined by result of CRC check. In testing a DUT with the standard defined data (specified by the standard), the BER calculation is easy, since it is known what is being transmitted, and it an be compared against what is being received to obtain BER performance.

As mentioned earlier, although these estimates provide excellent measures, reliable estimates of these parameters require observations over a large number of frames. Especially, for low BER and FER measurements, extremely long transmission intervals will be needed. Therefore, for some applications these measures might not be appropriate. Note also that these measurements provide information about the actual operating condition of the receiver. For example, for a given RSSI or CINR measure, two different receivers which have different performances will have different BER or FER measurements. Therefore, BER and FER measurements also provide information on the receiver capability as well as the quality of DUT.

Channel-frequency-response (CFR) estimates provide information about the desired signal's power variation across frequency carriers. It is a much more reliable estimate than RSSI information, as it does not include the other impairments as part of the desired signal power. However, it is less reliable than CINR (or SINR) estimates, since it does not provide information about the noise and/or interference powers with respect to desired signal's power. However, for white noise (like AWGN), channel-frequency-response estimate can also provide an idea about CINR expected at each carrier, and hence expected EVM.

For wireless measurements, CFR provides information about the dispersion (selectivity) of the medium. For measurements where the receiver is connected to the DUT with cable, CFR can provide an idea about the filter responses used at the transceivers. CFR is also useful for measuring spectral flatness, which is a mandatory measurement required by the standard. Measurements on I/Q require a detailed discussion for multicarrier systems and will be the subject of a future report.

The accuracy of the receiver algorithms affect the measurement performance. For example, if the channel estimation algorithm is not designed properly, one might observe worse spectral flatness measure, and then, might conclude that the filters that are used at the transmitter do not have good spectral properties. However, the problem might not be the filter that is used at the transmitter or receiver, it could very well be the channel-estimation algorithm that is used. Similarly, one might observe large EVM at the constellations due to the improper receiver algorithm design. Ideally, we would like EVM to reflect errors due to the device under test (DUT), not due to the low-performance receiver algorithms. Therefore, in testing and measurement world, it is desired to implement "THE" optimal performance receiver algorithms to reduce the errors contributed by these algorithms. On the other hand, we have to be careful not to increase the computational complexity and measurement delay. Both fast and accurate measurements are preferable.

For the same reason, when measuring a DUT, the other impairments caused by other parts of the transceiver chain must be compensated (or calibrated). The calibration or compensation should be done in such a way that we don't compensate the impairments caused by the DUT unintentionally.

Another important point is regarding to the location in the receiver chain where a particular measurement should be performed. Ultimately, the goal is to identify the impairment caused by the DUT using the measurements. If the receiver algorithms are correcting or changing the structure of the impairment, then, reliable measurements are not possible. For example, if there is a sample clock compensation algorithm at the receiver, and if we try to measure the sample clock error after the compensation, we will not be able to identify the error. This was an obvious and easy example, but, for some tricky measurements like I/Q impairments measurements, one has to know where to make the measurement to obtain the most accurate results. It is possible to make a measurement in two different locations and get similar results. However, in most cases, there is a preferable point where a specific measurement makes most sense for obtaining better performance, less computational complexity, and for other possible reasons.

Editor's Note: This is the last installment of a three-part article series on WiMAX measurements. For the previous two articles, please refer to the July and August 2006 issues of Microwaves & RF or visit the magazine website at www.mwrf.com

ACKNOWLEDGMENT
The authors would like to thank Dr. Larry Dunleavy for his comments and for the review of this article prior to publication.