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20100925-msgs | ||
avg.c | ||
evscan | ||
fft.c | ||
fscan | ||
Makefile | ||
peak.c | ||
plpk | ||
plscan | ||
range | ||
README |
Antenna measurements ==================== The objective of antenna measurements is to determine how much energy the antenna transfers at different frequencies. For this, we set up a sender, a receiver, connect one to the antennas being tested, and the other to an arbitrarily chosen lab antenna. Since none of the items (sender, receiver, lab antenna) are calibrated, we can only compare antennas but we cannot determine any absolute characteristics. Preparing a measurement run --------------------------- Before measuring the characteristics of an antenne, we need to set up the test environment and obtain a number of filtering parameters. The filters are used to reduce the effect of noise on the measurements and to suppress contamination from other sources. 1) Install transmitter and receiver. The transmitter is an atusb or atusd board, the receiver an USRP2+XCVR2450 with the antenna to test. (The same setup may also work with a USRP1 or UN210, and a RFX2400 board.) Both should be spaced at least twenty times the wavelength (12.5 cm at 2405 MHz), or 2.5 m apart. For test runs that can be compared with each other, antenna placement and orientation have to be exactly the same. The sender runs tools/atrf-txrx/atrf-txrx, the receiver runs utilities from gnuradio. 2) Obtain baseline performance values. For example, activate the sender with atrf-txrx -f 2455 -p 0.5 -T +0.5 Emit a constant wave at 2455+0.5 MHz with a power of 0.5 dBm or 1.1 mW. Monitor the received signal with usrp2_fft.py -f 2455.5M -d 16 Record the range in which the frequency peak falls. Variations of a few dB are to be expected. 3) Generate a series of sample for a specific setting. Example: The following script sets up the transmitter, lets it "warm up" for ten seconds, then takes 100 measurements, stored in files tmp00 through tmp99 in a directory $PWD/100/. In this setup, the receiver's gnuradio runs on a different host than the sender. Therefore we use ssh and pass the directory from $PWD. atrf-txrx -f 2455 -p 2.6 -T +0.5 \ 'sleep 10; for a in 0 1 2 3 4 5 6 7 8 9; do for b in 0 1 2 3 4 5 6 7 8 9; do ssh ws usrp2_rx_cfile.py -d 16 -f 2455.5M -g 46 -N 1124 \ '$PWD'/100/tmp$a$b done done' Each measurement obtains 1124 samples, 1024 samples for the FFT and 100 samples to cut off (see below). 4) Determine the shape of the captured waves in the time domain, e.g., with gnuplot gnuplot> plot "<./avg 1 <100/tmp00" with lines "avg" outputs the magnitude of the recorded wave, averaging over the specified number of sample. Some waves will probably show a peak in the first few samples. We need to cut off these peaks in the later processing steps. In this example, we will skip the first 100 samples. Besides the initial peak, the waves should be of comparable amplitude. 5) Verify the distribution in the frequency domain and determine the noise floor. gnuplot> plot "<./fft -s 100 -d <100/tmp00" with lines ^ skip initial peak The spectrum should be U-shaped, with narrow peaks tens of dB above the noise floor near the beginning and the end. Note that the noise floor is curved and not perfectly flat. From this, we pick level of the noise floor. The value should be at or slightly below the highest peaks of the noise between the large peaks at the end of the spectrum. This noise floor value is used to filter uninteresting samples later on, removing a constant bias from the results. In this example, we'll use a noise floor value of -50 dB. 6) Determine the "interesting" frequency range. For this, we consider all the spectra of the measurements: gnuplot> plot "<for n in 100/tmp*; do ./fft -s 100 -d <$n;echo;done" \ with lines There should be a thick noise band in the middle, with pronounced narrow peaks at the edges. If there are one or two signals on top of the noise band, some measurements have been compromised and need to be removed or redone. We will do this in the next step. When zooming into the left peak, the "bins" which contribute to the peaks can be identified. The range should be chosen with some tolerance, since the frequency may shift a bit during the measurement process. By not considering bins far from the peak, less noise is included in the final result, complementing the filtering by noise threshold from step 4). Restricting the bins also eliminates the second peak at the end of the spectrum. In this example, we'll use a range from 0 to 20. 7) Obtain the peaks from all measurements gnuplot> plot "<for n in 100/tmp*; do ./fft -s 100 0 20 50 <$n;done" \ with lines ^ ^ ^ ^ | | | | skip, from step 4 | | threshold, 5) lowest bin highest bin This should yield a jagged more or less horizontal line with values differing by not more than 1-2 dB. If there are any large outliers, they have been contaminated and should be dropped. 8) The final result for one measurement run can be obtained as follows: for n in 100/tmp*; do ./fft -s 100 0 20 50 <$n;done | ./range -v 2 In this example, "range" eliminates all outliers more than 2 dB from the average and reports this. The output are three numbers: the average (after eliminating outliers), the minimum, and the maximum.