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usrp/README: description of the preparation for antenna measurements.
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Antenna measurements
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====================
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The objective of antenna measurements is to determine how much energy the
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antenna transfers at different frequencies. For this, we set up a sender,
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a receiver, connect one to the antennas being tested, and the other to an
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arbitrarily chosen lab antenna.
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Since none of the items (sender, receiver, lab antenna) are calibrated,
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we can only compare antennas but we cannot determine any absolute
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characteristics.
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Preparing a measurement run
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---------------------------
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Before measuring the characteristics of an antenne, we need to set up the
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test environment and obtain a number of filtering parameters. The filters
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are used to reduce the effect of noise on the measurements and to suppress
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contamination from other sources.
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1) Install transmitter and receiver. The transmitter is an atusb or atusd
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board, the receiver an USRP2+XCVR2450 with the antenna to test.
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(The same setup may also work with a USRP1 or UN210, and a RFX2400
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board.)
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Both should be spaced at least twenty times the wavelength, or 2.5 m
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apart. For test runs that can be compared with each other, antenna
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placement and orientation have to be exactly the same.
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The sender runs tools/atrf-txrx/atrf-txrx, the receiver runs utilities
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from gnuradio.
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2) Obtain baseline performance values. For example, activate the sender
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with
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atrf-txrx -f 2455 -p 0.5 -T +0.5
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Emit a constant wave at 2455+0.5 MHz with a power of 0.5 dBm or 1.1 mW.
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Monitor the received signal with
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usrp2_fft.py -f 2455.5M -d 16
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Record the range in which the frequency peak falls. Variations of a few
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dB are to be expected.
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3) Generate a series of sample for a specific setting.
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Example:
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The following script sets up the transmitter, lets it "warm up" for ten
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seconds, then takes 100 measurements, stored in files tmp00 through
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tmp99 in a directory $PWD/100/.
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In this setup, the receiver's gnuradio runs on a different host than
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the sender. Therefore we use ssh and pass the directory from $PWD.
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atrf-txrx -f 2455 -p 2.6 -T +0.5 \
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'sleep 10;
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for a in 0 1 2 3 4 5 6 7 8 9; do
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for b in 0 1 2 3 4 5 6 7 8 9; do
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ssh ws usrp2_rx_cfile.py -d 16 -f 2455.5M -g 46 -N 1124 \
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'$PWD'/100/tmp$a$b
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done
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done'
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Each measurement obtains 1124 samples, 1024 samples for the FFT and
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100 samples to cut off (see below).
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4) Determine the shape of the captured waves in the time domain, e.g.,
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with
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gnuplot
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gnuplot> plot "<./avg 1 <100/tmp00" with lines
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"avg" outputs the magnitude of the recorded wave, averaging over the
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specified number of sample.
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Some waves will probably show a peak in the first few samples. We need
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to cut off these peaks in the later processing steps. In this example,
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we will skip the first 100 samples.
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Besides the initial peak, the waves should be of comparable amplitude.
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5) Verify the distribution in the frequency domain and determine the noise
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floor.
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gnuplot> plot "<./fft -s 100 -d <100/tmp00" with lines
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^
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skip initial peak
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The spectrum should be U-shaped, with narrow peaks tens of dB above
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the noise floor near the beginning and the end. Note that the noise
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floor is curved and not perfectly flat.
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From this, we pick level of the noise floor. The value should be at or
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slightly below the highest peaks of the noise between the large peaks
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at the end of the spectrum.
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This noise floor value is used to filter uninteresting samples later
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on, removing a constant bias from the results.
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In this example, we'll use a noise floor value of -50 dB.
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6) Determine the "interesting" frequency range. For this, we consider all
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the spectra of the measurements:
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gnuplot> plot "<for n in 100/tmp*; do ./fft -s 100 -d <$n;echo;done" \
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with lines
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There should be a thick noise band in the middle, with pronounced
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narrow peaks at the edges. If there are one or two signals on top of
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the noise band, some measurements have been compromised and need to be
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removed or redone. We will do this in the next step.
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When zooming into the left peak, the "bins" which contribute to the
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peaks can be identified. The range should be chosen with some
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tolerance, since the frequency may shift a bit during the measurement
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process.
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By not considering bins far from the peak, less noise is included in
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the final result, complementing the filtering by noise threshold from
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step 4). Restricting the bins also eliminates the second peak at the
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end of the spectrum.
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In this example, we'll use a range from 0 to 20.
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7) Obtain the peaks from all measurements
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gnuplot> plot "<for n in 100/tmp*; do ./fft -s 100 5 15 50 <$n;done" \
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with lines
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^ ^ ^ ^
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skip, from step 4 | | threshold, 5)
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lowest bin highest bin
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This should yield a jagged more or less horizontal line with values
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differing by not more than 1-2 dB. If there are any large outliers,
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they have been contaminated and should be dropped.
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8) The final result for one measurement run can be obtained as follows:
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for n in 100/tmp*; do ./fft -s 100 0 200 50 <$n;done | ./range -v 2
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In this example, "range" eliminates all outliers more than 2 dB from
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the average and reports this.
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The output are three numbers: the average (after eliminating
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outliers), the minimum, and the maximum.
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