Wimax standards and Security The Wimax

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• TABLE 5.7
• Doppler Shift Ricean K factor (dB)
• Experimental Data

Modulation 3.5 MHz sensitivity (dBm) SNR (dB)	Theoretical (Mbps)
Actual (Mbps)
BPSK 1/2 −90.6 6.4	1.41	0.86
BPSK 3/4 −88.6 8.5	2.1	1.28
QPSK 1/2 −87.6 9.4	2.82	1.72
QPSK 3/4 −85.8 11.2	4.23	2.58
16QAM 1/2 −80.6 16.4	5.64	3.44
16QAM 3/4 −78.8 18.2	8.47	5.16
64QAM 2/3 −74.3 22.7	11.29	6.88
64QAM 3/4 −72.6 24.4	12.71	7.74

TABLE 5.7
Typical Parameters for SUI-1 to 6 Channel Models

Channel Model	Terrain Type	RMS Delay Spread (µs)	Doppler Shift	Ricean K factor (dB)
SUI-1	C	0.042 (Low)	Low	14.0
SUI-2	C	0.069 (Low)	Low	6.9
SUI-3	B	0.123 (Low)	Low	2.2
SUI-4	B	0.563 (High)	High	1.0
SUI-5	A	1.276 (High)	Low	0.4
SUI-6	A	2.370 (High)	High	0.4

Delay spread values estimated for 30-degree antennas azimuthal beamwidths, and ricean K-factors are for 90% cell coverage.
Source: IEEE 802.16 Broadband Wireless Access Working Group, 2003.

Interferences from other cells (cochannel interferences) strongly impact actual rates [35,36]. And in unlicensed cases, unwanted interferences in the band are also a concern: minimum signal to noise ratios listed in Table 5.6 must be maintained for a given throughput.

To compare system performance in diverse environments, tests are usu- ally conducted with traffic load generators and fading emulators. Service providers can thus create repeatable benchmark tests, in a controlled environ- ment, to compare equipment performance under different conditions. These tests quantify the different access performances in large rural areas, suburban areas, or dense urban cores, both for fixed access and full mobility.
Stanford University Interim (SUI) models are used to create a small number of models that emulate different terrain types, Doppler shift, and delay spread as summarized in Table 5.7. Terrain types are (from Ref. 13) defined as follows: the maximum path loss category (A), hilly terrain with moderate-to-heavy tree densities; the intermediate path loss category (B), hilly with light tree density or flat with moderate-to-heavy tree density; the minimum path loss category (C), mostly flat terrain with light tree densities. In some cases, these

terrain categories are used to refer to obstructed urban, low-density suburban, and rural environments, respectively.

2. 
   Experimental Data
   

As an example, let us illustrate the above with data for fixed broadband access in a residential suburban area. Unlike mobile cellular systems, a fixed wire- less access system needs a careful selection process for qualifying customers. Propagation tools and terrain data are used in that process, but the level of detail is a matter of choice. A precise qualification process leads to better tar- geted mailing and may avoid miscalculated predictions. Service providers cannot afford to be too optimistic nor too pessimistic in their predictions: false negatives are a missed revenue opportunity, and false positives lead to wasted technician time and unhappy customers. It is therefore time well spent to refine selection criteria and tools as much as possible.
A simple selection process consists of geocoding customers’ addresses and correlating them to terrain data as well as to a simple propagation model for an initial estimate. Address geocoding, however, is far from a perfect process. A customer address may not give reliable longitude and latitude, and will rarely hint on where an outdoor antenna may be in good RF visibility of a base station. Some manual processing and even some local knowledge of the area may be required; and in the end, a site visit may still discard a possible location. The quality of terrain data and RF modeling is of course also of high importance. Terrain data can be obtained at no cost from U.S. geological surveys (100 or 30 m accuracy), which is useful for path loss prediction, but it will not accurately predict shadowing in all areas. More granular data, including building data, with submeter accuracy can be obtained at a much higher cost. Another alternative is to drive-test around the area of interest and to optimize a propagation model in a given area. Many software packages allow for such model optimization, which significantly improve prediction tools. (Of course these models, as well as the drive-test optimizations, are usually based on mobile data.)

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