Yet another comparison of two 40m quarter wave verticals with elevated radials over a 14,700km path using QRSS

This article reports an experiment to compare a quarter wave vertical with four and eight elevated radials using a reference antenna being a nearby quarter wave vertical with three elevated radials on 40m over a 14,600km path using QRSS.

Popular ham opinion is that more radials improves gain (by way of efficiency improvement), whether elevated radials or buried radials. This opinion is reinforced by on air discussions propagating folk lore, and ham literature. For example, (Devoldere 2005) seems to sit on the fence reporting material from both sides of the argument, but with a leaning to buried radials with the following, his Buried or Elevated, Final Thoughts.

• Providing the possibility of installing a decent ground system under very unfriendly circumstances, such as over rocky ground.
• More flexibility in matching, since the real ground is not resonant. An elevated radial system using only a few radials—maximum of four—can be made inductive or capacitive, which may be an asset in designing a matching system.
For using elevated radials I would propose the following guidelines:
• Put the radials up as high as possible.
• Use as many radials as possible, since this makes the radial system non-resonant.
• If you use a small number (< 16), install a ground screen.
If you have the space and if the ground is not too unfriendly, I would suggest you use buried radials however.

(Christman 1988) reports the results of a range of models that suggest that four elevated radials delivers performance very close to a much larger number of radials.

(Duffy 2010) reports the results of a series of models based on NEC4 which is more capable of modelling conductors in or near real ground.

The objective of the experiment is to determine the advantage of eight elevated radials over four over this long distance path to validate model predictions.

Configuration

Experiment design

This experiment is a refinement of a set of prior experiments

The experiment design is for two parts conducted on successive days. In each part, a large number of measurements of S/N at the receiving station will be recorded for the subject antenna (8 elevated radials (Antenna B) on the first day, and 4 elevated radials (Antenna C) on the second day) and of the reference antenna (Antenna A).

Receiving

The receiving station was W4HBK at Pensacola, FL (USA).

Transmitting

The transmitting station was VK2DVK located about 100km S/SW of Sydney (Australia), and the antennas were two adjacent quarter wave verticals over elevated ground planes. The two antenna systems are:

• a 10m high vertical of 40mm aluminium tubes over a set of eight or four copper wire radials at variable height averaging 3.5m (0.083λ), fed with 80m of RG213 coax, a 1:1 Guanella balun; and
• a 10m high vertical of 20mm steel tubes over a set of three copper wire radials at variable height averaging 1m (0.023λ), fed with 60m of RG6 coax, a 1:1 Guanella balun.

At the time of the tests, the path elevation angle at the transmitter was very low, in the range 2° to 6°, and bearing is 78°.

Keyer

A QRSS keyer that facilitates antenna switching based on special characters embedded in the message was constructed. It was used with a quite standard Icom IC7410 in CW mode adjusted for 5W output.

Fig 1 shows the internals of the QRSS keyer. The keyer is described in detail at Another Morse beacon keyer - A/B RF switching.

The message is structured to send 1min key down (Antenna A), 1min key up, 1min key down (Antenna B or C), 1min key up, 12WPM ID then at the start of the next minute it recycles. So one cycle every 5 minutes, two every 10min.

The receiver used Spectrum Lab to gather and present a view of received signal and noise.

Two kinds of charts are used for analysis:

• waterfall charts; and
• watch plots.

Additionally, a text export from the watch list plotter configured to capture the signal power, noise power and calculated S/N ratio was used.

Data is presented below for the hour from 08:30UTC on 12/01/13.

Waterfall charts

Fig 2 shows a set of six graphics cropped from the full waterfall charts. They show VK2DVK's signal at the top of each chart over the hour under study.

Watch plots

The watch plots below plot the calculated Signal/Noise ratio in blue, and this is the best indicator of the relative performance of each transmitting antenna at the time. The green and red lines are Signal and Noise respectively.

Fig 3 shows a set of six graphics cropped from the full watch plots. They show VK2DVK's signal over the hour under study.

Data is presented below for the hour from 08:30UTC on 13/01/13.

Waterfall charts

Fig 4 shows a set of six graphics cropped from the full waterfall charts. They show VK2DVK's signal at the top of each chart over the hour under study.

Watch plots

The watch plots below plot the calculated Signal/Noise ratio in blue, and this is the best indicator of the relative performance of each transmitting antenna at the time. The green and red lines are Signal and Noise respectively.

Fig 5 shows a set of six graphics cropped from the full watch plots. They show VK2DVK's signal over the hour under study.

Statistical analysis of watch plot data

Introduction

If we were to make a pair of measurement of received signal power with Antenna X over this path, one after the other, we might expect them to be close but not exactly the same. There are several possible reasons for the variation, in this experiment ionospheric fading introduces a large variation.

If we make a large number of such measurements, the measured values with tend to cluster around some central value. (Duffy 2012) describes such an experiment that shows that the measured Signal in dB is approximately normally distributed, and parametric statistic techniques can be used to assist analysis.

Fig 6 shows an example of two large sets of measurements of normally distributed phenomena, and the probability that certain measured values occurred in the result. Data sets A and B look different to the eye, and being normally distributed we can calculate a mean (M) (the central tendency) and standard deviation (SD) (a measure of the variation within a data set) for each set. In this example, Ms are 19 and 21 and SD is 3.0 for both.

The question is whether the observed M and SD are due to an underlying difference in the two data sets (eg from antennas with different performance) or whether the difference could be a result of chance.

We cannot say it is one or the other, but we can determine the probability that the result was due to chance alone using Student's T-test which depends on the distribution of the means of normally distributed data.

In this case, if those results were from 100 A measurements and 100 B measurements, the T statistic can be calculated and is 4.7. Using the T distribution, the probability that the difference in means of 2 was due to chance alone is 0.0005%, very unlikely!

This technique will be used to analyse the measured data from this experiment.

The S/N is taken to be a log normal distribution (Duffy 2012), so S/N in dB a normal distribution.

Comparison of Antenna A and Antenna B

Part 1 of the test provided direct measurement data on which this comparison is based.

 Mean SD df Antenna A 10.8 3.4 1052 Antenna B 13.1 3.3 1043 Difference 2.3 3.4 2092

Table 1 shows the summary statistics for the measurement of Antenna B against Antenna A (the reference antenna common to both tests).

Comparison of Antenna A and Antenna C

Part 2 of the test provided direct measurement data on which this comparison is based.

 Mean SD df Antenna A 19.3 3.0 1042 Antenna C 22.1 3.0 1034 Difference 2.8 3.0 2076

Table 2 shows the summary statistics for the measurement of Antenna C against Antenna A (the reference antenna common to both tests).

Comparison of Antenna B and Antenna C

This comparison is based on the statistical summary of Part 1 and Part 2 measurements.

 Mean SD df Difference B-A 2.3 3.0 2092 Difference C-A 2.8 3.4 2076 Difference C-B 0.5 3.2 4168

Table 3 shows the summary statistics for comparison of Antenna C against Antenna B.

From this data, we can derive a t statistic which is 0.16, which with 4168 degrees of freedom gives a probability that the difference in the means of 0.5dB is due to chance alone is 87%, ie it is most likely that there is no difference in the Antenna B (8 elevated radials) compared to Antenna C (four elevated radials).

Model predictions

A series of NEC4 models were used to predict the behaviour of the subject antenna system at different heights above real ground.

 Height of radials Gain (dBi) Radials (m) (λ) 3 4 8 32 0.005 0.000119 -4.7 -3.6 -1.6 -0.3 1 0.023 -0.2 -0.2 -0.1 -0.1 3.5 0.083 0.22 0.12 0.1 0.13

Table 4 shows the results of the NEC4 models of the maximum gain of a 40m quarter wave with 3, 4, 8, and 32 radials at 0.005m, 1m and 3.5m above average ground.

It can be seen that the NEC4 models would suggest there will not be much difference between an antenna with 4 radials and another with 8 radials at 3.5m.

Conclusions

A redesign of earlier experiments to reduce noise and bias has delivered better experimental results.

In the presence of a large amount of statistical noise (mainly from ionospheric fading), statistical techniques can be used to reveal whether or not increasing elevated radials from four to eight gives a significant change in performance.

The result of the experiment is that the S/N ratio of four radials compared to eight radials over this path was the same with a confidence of 87%.

The experimental results were consistent with model predictions, and do not support ham folk lore that in the general case, more radials are better... elevated radial systems may be quite different to buried radial systems and the radial height need not be all that high for excellent performance.