Identifying Frequency Usage and Patterns







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Spectrum analysis is one part science and one part deduction. Although the measurement and plotting of RF energy on a two-dimensional plot (called a spectrograph) may be based on objective algorithms and calculation, determining what devices made those patterns and the proximity of any given device is more a matter of logical deduction and educated guesses than definitive answers. Spectrum analysis can be used to determine which parts of the RF spectrum are heavily utilized, either for targeting purposes or for avoidance purposes.

Consider the example shown in Figure 8-11.

Is the RF pattern clustered around the 2.412 GHz band over the last two minutes caused by an AP operating in the next office with a low transmit power setting, or in the next building but with a high transmit power setting? Or is it even an AP at all?

RF spectrum analyzers (the devices used to receive, record, and plot RF energy in a given frequency band) are only capable of measuring RF energy received at a given point in time and its intensity at that point in time. In their purest form, they do not understand higher-level protocols such as 802.11 and 802.16. Identifying the source of the RF plot depends on factors such as source proximity, source technology type, and source operating mode/emission pattern. Gauging source position is even harder if you are in a static location. Walking around with the analyzer provides the best indication of the location of a likely RF source since the closer you walk to the source, the more intense the RF energy and thus the more spikes on your spectrograph.

Even beaconing APs have different plots compared to APs that are communicating with wireless stations. Beacons will show up as defined points on a spectrograph, similar to what's shown in Figure 8-11. An AP communicating with a client during a heavy data transfer session will show clearly defined bands that spread out plus-minus 5 MHz on either side of the actual channel the AP is set to, due to a combination of the encoding algorithm used in injecting the data into RF energy and also because the air medium being injected into is nondigital in nature and thus cannot be injected into precisely at the set channel's frequency. On a spectrograph plotting RF received over elapsed time, this displays as a 45-degree slanted line.

However imprecise any conclusions may be that are derived from any given spectrograph, RF spectrum analysis has benefits. Chief among these is identifying spectral efficiency. For example, if many APs are operating in the 2.412 GHz region, those devices are fighting for a slice of the same spectrum pie. The noise level is going to be much higher in that frequency band. It therefore makes sense to use a different frequency in order to achieve higher throughput because the AP won't have to share the limited "airtime" with so many other devices or suffer degradation due to the increased noise in the frequency band. Even if all frequencies are heavily utilized by existing devices, by identifying the concentration of the entire frequency range, the AP can be configured to use a band with the lowest relative utilization. Also, spectrum analysis comes in handy when trying to identify the cause of throughput drops at particular times of the day (e.g., office microwave oven use at lunchtime impacting channel 11 users).

Though various types of spectrum analyzers are on the market, most are expensive and/or only operate under Windows. However, there is a neat open-source solution that revolves around the WiSpy USB RF spectrum analyzer dongle ( (You can download it at As of version 2006-09-R1, Spectrum-Tools enables the dongle to be used on Linux distributions that do not implement USB device detachment support in their kernels (e.g., Fedora Core 5) so that those who want to use the dongle don't have to mess around with reverting their kernel to vanilla versions if they don't like that sort of thing.

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