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process, the better the receive sensitivity. Think of this as being at a hockey game. There is an ambient level of noise that exists from everything around you. There is a certain volume that you have to speak at for your neighbor to hear you. That level is the receiver sensitivity.
It is the weakest signal that the transceiver can decode under normal circumstances. With that said, if the noise in a particular area is louder than normal, then the minimum level you have to yell gets louder.
In WLAN equipment, receive sensitivity is usually defi ned as a function of network speed. Wi-Fi vendors will usually specify their receive sensitivity thresholds at various data rates; an example vendor specifi cation for a 2.4 GHz radio is listed in Table 3.3. For any given receiver, more power is required by the receiver radio to support the higher data rates.
Different speeds use different modulation techniques and encoding methods, and the higher data rates use encoding methods that are more susceptible to corruption. The lower data rates use modulation-encoding methods that are less susceptible to corruption.
TA B L E 3 . 3 Receive sensitivity thresholds (example)
Data rate Received signal amplitude
MCS7 –77 dBm
MCS6 –78 dBm
MCS5 –80 dBm
MCS4 –85 dBm
MCS3 –88 dBm
MCS2 –90 dBm
MCS1 –90 dBm
MCS0 –90 dBm
54 Mbps –79 dBm
48 Mbps –80 dBm
36 Mbps –85 dBm
24 Mbps –87 dBm
18 Mbps –90 dBm
Data rate Received signal amplitude
12 Mbps –91 dBm
9 Mbps –91 dBm
6 Mbps –91 dBm
The 802.11-2012 standard defi nes the received signal strength indicator (RSSI) as a relative metric used by 802.11 radios to measure signal strength (amplitude). The 802.11 RSSI measurement parameter can have a value from 0 to 255. The RSSI value is designed to be used by the WLAN hardware manufacturer as a relative measurement of the RF signal strength that is received by an 802.11 radio. RSSI metrics are typically mapped to receive sensitivity thresholds expressed in absolute dBm values, as shown in Table 3.4. For exam- ple, an RSSI metric of 30 might represent –30 dBm of received signal amplitude. The RSSI metric of 0 might be mapped to –110 dBm of received signal amplitude. Another vendor might use an RSSI metric of 255 to represent –30 dBm of received signal amplitude and 0 to represent –100 dBm of received signal amplitude.
TA B L E 3 . 4 Received signal strength indicator (RSSI) metrics (vendor example)
RSSI
Receive sensitivity
threshold Signal strength (%) Signal-to-noise ratio
Signal quality (%)
30 –30 dBm 100% 70 dB 100%
25 –41 dBm 90% 60 dB 100%
20 –52 dBm 80% 43 dB 90%
21 –52 dBm 80% 40 dB 80%
15 –63 dBm 60% 33 dB 50%
10 –75 dBm 40% 25 dB 35%
5 –89 dBm 10% 10 dB 5%
0 –110 dBm 0% 0 dB 0%
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terms, the signal quality could be a measurement of what might affect coding techniques, such as the Barker code or Complementary Code Keying (CCK), which relates to the trans- mission speed. In Chapter 6, “Wireless Networks and Spread Spectrum Technologies,” you will learn about coding techniques. Anything that might increase the bit error rate (BER), such as a low SNR, might be indicated by SQ metrics.
Information parameters from both RSSI and SQ metrics can be passed along from the PHY layer to the MAC sublayer. Some SQ parameters might also be used in conjunction with RSSI as part of a clear channel assessment (CCA) scheme. Although SQ metrics and RSSI metrics are technically separate measurements, most Wi-Fi vendors refer to both together as simply RSSI metrics. For the purposes of this book, whenever we refer to RSSI metrics, we are referring to both SQ and RSSI metrics.
According to the 802.11-2012 standard, “the RSSI is a measure of the RF energy received. Mapping of the RSSI values to actual received power is implementation depen- dent.” In other words, WLAN vendors can defi ne RSSI metrics in a proprietary manner.
The actual range of the RSSI value is from 0 to a maximum value (less than or equal to 255) that each vendor can choose on its own (known as RSSI_Max). Many vendors publish their implementation of RSSI values in product documents and/or on the vendor’s web- site. Some WLAN vendors do not publish their RSSI metrics. Because the implementation of RSSI metrics is proprietary, two problems exist when trying to compare RSSI values between different manufacturers’ wireless cards. The fi rst problem is that the manufactur- ers may have chosen two different values as the RSSI_Max. So WLAN vendor A may have chosen a scale from 0 to 100, whereas WLAN vendor B may have chosen a scale from 0 to 30. Because of the difference in scale, WLAN vendor A may indicate a signal with an RSSI value of 25, whereas vendor B may indicate that same signal with a different RSSI value of 8. Also, the radio card manufactured by WLAN vendor A uses more RSSI metrics and is probably more sensitive when evaluating signal quality and SNR.
The second problem with RSSI is that the manufacturer could take their range of RSSI values and compare them to a different range of values. So WLAN vendor A may take its 100-number scale and relate it to dBm values of –110 dBm to –10 dBm, whereas WLAN vendor B may take its 60-number scale and relate it to dBm values of –95 dBm to –35 dBm.
So not only do we have different numbering schemes, we also have different ranges of values.
Although the way in which Wi-Fi vendors implement RSSI may be proprietary, most vendors are alike in that they use RSSI thresholds for very important mechanisms, such as roaming and dynamic rate switching. During the roaming process, clients make the deci- sion to move from one access point to the next. RSSI thresholds are key factors for clients when they initiate the roaming handoff. RSSI thresholds are also used by vendors to imple- ment dynamic rate switching (DRS), which is a process used by 802.11 radios to shift between data rates. Roaming is discussed in several chapters of this book, and DRS is dis- cussed in greater detail in Chapter 12, “WLAN Troubleshooting.”
Can an 802.11 Network Card Truly Measure the Noise Floor and SNR?
It should be understood that the earlier 802.11 wireless network interface cards (NIC) were not spectrum analyzers, and though they could transmit and receive data at a prodi- gious rate, they could not see raw ambient RF signals. Since the only things getting past the NIC’s encoding fi lter were bits, all of the information reported by the NIC had to come from the bits they received. If you turned on a microwave oven near a wireless NIC, there were no data bits being generated by the microwave, so the NIC would always report a noise variable of zero. In the absence of encoded RF signals coming from other 802.11 devices, the noise variable could not be used to report the noise fl oor. The only device that could truly measure non-encoded RF energy was a spectrum analyzer.
We know that you may have seen many screens generated by your various 802.11 devices that displayed signal (from the RSSI variable) and another value displayed as signal-to- noise ratio (SNR), showing the comparison between the RSSI and the noise fl oor. The developers of the wireless NICs knew that the RF folks out there “lived, breathed, and died” by signal, noise, and signal-to-noise ratio data.
WLAN professionals demanded a noise variable in order to perform site-survey calcu- lations, so various Wi-Fi vendor organizations came up with unique ways to guess the noise fl oor. Because 802.11 wireless NICs could only process bits, they needed to come up with algorithms to calculate a noise variable based on the bits going through the NIC.
As with RSSI measurements, different vendors that manufactured 802.11 equipment calcu- lated noise in different ways. Some vendors fl atly refused to make up a number for noise only based on bits. Other vendors developed sophisticated algorithms for calculating noise.
More recently 802.11 chip manufactures fi gured out how to turn off the encoding fi lters and use the RF signals coming through the antenna to become rudimentary spectrum ana- lyzers. However, this was in lieu of being an 802.11 NIC capable of processing data. Typi- cally, these newer chips could be either a lightweight spectrum analyzer or a Wi-Fi card processing data, but usually not both at the same time, since the front-end fi lter would identify an 802.11 signal and pass it on to the 802.11 protocol stack, not the spectrum ana- lyzer. Some newer APs can operate in what is sometimes termed “hybrid” mode. These APs can perform both 802.11 and spectrum analysis functions at the same time, although there is often a degradation in WLAN performance. Some of the access point vendors also are using these extra-capable Wi-Fi chips and are adding spectrum analysis as an option for an access point with the appropriate software to take advantage of this extra
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ability. Please understand that an 802.11 access point is not your best tool to evaluate the noise fl oor during a site survey. So what is the best tool to measure the noise fl oor in any environment? A high quality portable spectrum analyzer. A high quality portable spectrum analyzer uses a spectrum analyzer chipset capable of measuring non-encoded RF energy, and the portability of it makes it the best tool to measure the noise fl oor.
If you would like to learn more about the differences between 802.11 NICs and spec- trum analyzers, read CWAP Certifi ed Wireless Analysis Professional Offi cial Study Guide (Sybex, 2011).