How to Calculate the Average RSSI for an 802.11
By Steve McDonnell
Wireless network interface cards use received signal strength indication -- or RSSI for short -- to monitor radio signal strength and check for other devices transmitting on the same channel. If the signal falls below a certain level, most devices will attempt to detect and connect to a new access point with a stronger signal. Many wireless network monitoring tools automatically calculate and display RSSI, but you can also calculate it manually by dividing the signal-to-noise ratio by the network card's RSSI maximum.
Download a free wireless networking tool with SNR or RSSI measurement capabilities. Install and run the wireless networking tool on your laptop or notebook with Wi-Fi enabled.
Locate the wireless router from which you want to measure average RSSI from the network monitoring tool's display pane. Find the signal strength indicator and determine if it is reported as SNR, measured in dBm, or in arbitrary RSSI units. Identify the frequency at which the utility reports signal strength.
Determine the amount of time you will use for the average RSSI calculation; for example, five seconds. Collect the SNR or RSSI values at each interval; for example, once per second for five seconds.
Determine the RSSI_Max for the vendor of your NIC by visiting the vendor's website if the wireless utility only provides SNR. Sample RSSI_Max values used by NIC vendors are 60 for Atheros, 100 for Cisco and 60 for Symbol.
Convert SNR to RSSI if you collected SNR measurements. Divide SNR by RSSI_Max and multiply by 100 to get a percentage for each observation. For example, if SNR is 40 dBm and RSSI_Max is 100, the RSSI value will be 40/100 * 100 = 40 percent.
Sum the RSSI values for each observation and divide by the total number of observations to get an average RSSI. For example, if the RSSI values were 40, 52, 43, 44 and 45, the average RSSI would be (40 + 52 + 43 + 44 + 45)/5 = 44.8 percent.
Steve McDonnell's experience running businesses and launching companies complements his technical expertise in information, technology and human resources. He earned a degree in computer science from Dartmouth College, served on the WorldatWork editorial board, blogged for the Spotfire Business Intelligence blog and has published books and book chapters for International Human Resource Information Management and Westlaw.