RF Predictive Modeling – Part I

Qualitative vs. Quantitative
Modeling Methodologies Trade-offs – Who knew?

Anyone who has worked in the field of wireless understands that engineering the performance of a radio system is as much an art form based on experience and historical data as well as science based on mathematics and physics of how a radio signal propagates. The key to this statement is understanding that a radio frequency (RF) engineer bases their predictions on as much qualitative data as they do quantitative data. I will admit that we engineers do attempt to minimize the qualitative aspects of our recommendations, but they are in every analysis we perform. This discussion intends to highlight those qualitative aspects and how much performance (uptime and throughput) you as a user of such radio technologies wants to risk.

The biggest issue with radio signal propagation is we can’t physically see it. Many people believe that if one can see the other end of the radio link (line of site or LOS), then that’s good enough, which is as far from the truth as one can be. Radio signals do not propagate like electrical or light signals do, down a cable that is contained within the confines of the ‘pipe’ they are traversing. Radio signals reflect off most surfaces and naturally spread through the air and experience additional reductions in strength in differing air quality conditions. Most of the previous examples of how a radio signal is affected during transmission are what constitutes some of the qualitative engineering aspects leveraged during a radio system design.

Mathematics and physics integrated into radio signal predictive modeling software assist greatly with analyzing the propagation characteristics of radio signals. Unfortunately, there are many different scientific methods of predictively modeling a radio signal like Vigants-Barnett (VB) or ITU standards. Each method has its advantages and disadvantages based on what is trying to be proven. In general, VB seems to offer a more restrictive result in terms of predicted availability than ITU – a calculation of predicted uptime of a radio link over the same path with the same hardware using the same backend databases for terrain and clutter. Subsequently, you, as a user, need to understand how your solution was modeled – in a more risk-averse model like VB or a less risk-averse model like ITU. If a user indicates they want five 9’s of availability/uptime (99.999% which equates to 0.001% of downtime over a year or about 5 minutes total), a VB model will result in higher transmit signal levels and larger dishes than an ITU model would to show the same availability/uptime numbers. Where do you as a user want to risk the uptime of your radio system – in the modeling of the radio system which may show a better than can be expected performance or in the additional costs of higher power radios and larger dishes which require larger structures to support? Only experience can tell which approach works best in ‘your neck of the woods’ or if you have redundancy built into your overall solution.

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