CWNP recently released the CWAP study guide. We have every CWNP book in the office here at MetaGeek. These study guides are rich with 802.11 knowledge as you’ll see in the exam objectives pdf . Whether you’re using it as a reference or studying for certification the Certified Wireless Analysis Professional Study Guide deserves a spot on your shelf.
One of the chapters in the CWAP study guide discusses best practices of spectrum analysis. Most of the content is spot on. I plan on recommending the CWAP to any new users of Chanalyzer who want to get the most out of their spectrum analysis software. So don’t take this post the wrong way when we offer a supplement to the spectrum analysis chapter.
Before Wi-Spy, a spectrum analyzer was this clunky card that had a-bunch-of-graphs-I-don’t-understand and oh yeah device classification. They would also cost you about as much as a “used Toyota Corolla” (in Mark’s relative form of measurement). Most of the users looked at the device identification and forgot the graphs. Turns out the information provided by these spectrum analyzers was pretty useful and device identification was less-than perfect. Running a spectrum analyzer for 15 minutes to see 30 different instances of “generic narrow band transmitter” can only help so much right?
So here’s my point (and the CWAP study guide agrees with me): To discover WLAN interference you’ll need to recognize the patterns wireless devices create in the spectrum without the aid of device identification. I have a few issues with heavy reliance on device identification, and most of them are also discussed in the CWAP study guide as well.
- If the software claims there is a transmitter, you should be able to match its claim to the data in the FFT, density and or duty cycle.
- Quite often, interference is not from non-Wi-Fi but from channel-overlapping Wi-Fi. Device Identification doesn’t list Wi-Fi as potential WLAN interference.
- Device identification has no listing for sideband carrier interference. (but it can tell you when there’s an x-box controller in your office!)
- Device identification software lists numerous instances of interfering devices which are more than likely all the same device.
This is where you may ask, “so how am I supposed to find interference???” The CWAP study guide provides a great start in understanding spectrum analysis without device identification. As a Chanalyzer user, you’ll have a few features that go above and beyond what is discussed in the study guide so we’re providing a supplement to the chapter. Here are a few features Chanalyzer users get that others don’t:
- Data Logging – As soon as you plug in the Wi-Spy, Chanalyzer is saving the spectrum data. You can pause it like a DVR and Chanalyzer will continue to log. This is advantageous to users needing to drill down while they’re on-the-go.
- Unified Time Segment – You can select any range of time whether it is 10 seconds or 4 hours, at the start or anywhere in the recorded data, and get an instant snapshot of the waterfall view, density view, duty cycle or channels table. This is extremely useful for showing how bad the cordless phone interference was from 2:16 to 2:37.
- Density Map – This is the most accurate density map of spectrum activity available. The density map should be your first stop in identifying any kind of wireless interference.
The density view in Chanalyzer is a better method of displaying what an interference-causing device looks like than a traditional FFT graph. It basically creates a heat map of what parts of the spectrum are being used the most and at what amplitude level. For example, a cordless phone will constantly transmit in the same pattern again and again. Over time this pattern should appear brighter, or redder in a density map. These are better representations of wireless devices since most devices transmit quicker than a spectrum analyzer can read.
Examine these images and compare them to the CWAP study guide. If you haven’t looked into any of the reading material from CWNP.com, check out the site. Your WLAN will be glad you did. 🙂