Wireless System PerformanceWe have seen a number of standards so far in this text, as well as techniques used to provide robust wireless communications. These standards are successfully deployed throughout the world, and it may be interesting to study how well they perform. This chapter reports on the performance seen in tests conducted on several standards in different environments.

11.1 Wi-Fi System Performance

Wi-Fi systems and services are evolving from simple stand-alone access points (AP) to complex networks. It may be interesting to review some initial technologies evaluations, which showed promising data rates, and examine some extensions to larger systems.

11.1.1 Data Rates

Wi-Fi systems rely on IEEE 802.11 a, b, g, and more recently n. Early systems use frequency hopping schemes and direct spread spectrum, but most systems now focus on the OFDM physical layer (802.11a at 5.8 GHz and 802.11g at 2.4 GHz). Their data rate of is determined by the type of modulation and coding: from BPSK 1/2 to QAM64 3/4 (64QAM 5/6 for 11n). Physical data rates for 802.11a/g are quoted up to 54 Mbps, but maximum payload or user data throughput cannot exceed 36 Mbps, and actual measured throughput vary with suppliers (up to 30 Mbps); and interoperability between suppliers introduce even greater variations.

Table 11.1:

Wi-Fi maximum rates, theoretical and actual measured throughput in indoor lab, with controlled environment and low interferences for 802.11g, 20 MHz cannels at

2.4 GHz. (Actual rates are an average over different brands of access points and client devices).

Modulation 20MHz sensitivity SNR Phy. rate Max payload Actual(dBm) (dB) (Mbps) (Mbps) (Mbps)

BPSK 1/2 -90.6 6.4 6 5.2 2.8BPSK 3/4 -88.6 8.5 9 8.1 4.3QPSK 1/2 -87.6 9.4 12 10.6 6.0QPSK 3/4 -85.8 11.2 18 16.0 9.016QAM 1/2 -80.6 16.4 24 19.0 11.816QAM 3/4 -78.8 18.2 36 25.9 18.164QAM 2/3 -74.3 22.7 48 31.6 24.064QAM 3/4 -72.6 24.4 54 36.2 26.8

Table 11.1 summarizes typical data rates observed in a 20 MHz TDD 802.11g channel. These results were measured with low interferences and all client devices near the AP and within the same room, leading to system operations at maximum modulation. Perfect conditions will allow throughputs between 25 and 30 Mbps in some cases, but in most conditions network engineers should keep in mind throughput ranges as those of table 11.1 and figure 11.2 as opposed to the often quoted 54 Mbps.

A recent addendum to the standard, 802.11n, increases throughput from the 54 Mbps PHY rate to 150, 300, and even 600 Mbps by various new schemes such as: higher coding rate, 40MHz channel width, and MIMO. Here again actual throughput are lower (of the order of half the PHY rate).

Figure 11.1:Wi-Fi throughput measured for a variety of access points and client cards at close

indoor range, for an increasing number of client PC’s.

Figure 11.2:

Wi-Fi one-way delay measured for a variety of access points and client cards at close indoor range, for an increasing number of client PC’s per AP. (Chart shows

average delay and error bars represent a standard deviation in each direction)

11.1.2 Municipal Wi-Fi

Municipalities have tried in recent years to deploy cheap wireless networks free to use for their citizens. The common opinion is that Wi-Fi networks would be cheaper than other mobile wireless products. That opinion relies mainly on two arguments: 1) spectrum is free and 2) Wi-Fi client devices are free since they are part of laptops and tablets. Other arguments include 3) access points are cheap (when compared to cellular equipment) and 4) they are easier and cheaper to install (when compared to cellular towers)

The first two facts are a great advantage for Wi-Fi since they are an important part of the cost of providing service to the general population, the other two however hide more subtle aspects of wireless systems operations. [127]

Case study: Longmont

The city of Longmont contracted Kite Network in late 2006 to provide Wi-Fi Internet access throughout the city. The network was built to covered an ambitious 22 square miles of Longmont, and was completed in less than 90 days, which is much more successful than many other initiatives. 1 [128] The city of Longmont was at the time also covered by several cellular networks, including an EV-DO network, which is used in our comparison.

Network Cost Cost of rollout, spectrum, and recurring expenses for both EVDO and Wi-Fi can be estimated from various sources: EVDO rollout costs can be calculated using typical urban cell site costs (seen above) as if the network were rolled out in a greenfield manner. Of course, a cellular network operator would incur less cost by reusing real-estate and poles, but we compare here a greenfield build-out of the two networks. Wi-Fi numbers can be estimated in a similar manner, and in this case were published 2. It turns out that the build-out cost for both networks (5 EV-DO sites, and 600 Wi-Fi APs) are fairly similar.

Cost of spectrum is estimated from published auction results in that area prorated for the Longmont population. Spectrum is of course a high cost for EVDO, while free for Wi-Fi (with its inherent uncertainties).

Recurring cost such as power, real-estate, wired backhaul, is significant for both networks: EVDO needs a few high-cost, large towers; Wi-Fi needs for a similar coverage cheaper but more numerous AP locations.

Operational Cost The operational cost of maintaining wireless networks is often overlooked: it shows a main difference between networks: Wi-Fi costs more than $4 million annually (per SEC filing EVDO on the other hand is much cheaper: the network has fewer sites and requires fewer field technicians, and less NOC (Network Operations Center) manpower and tech support to resolve maintenance issues. We estimate that cost to $2.7 million for a city the size of Longmont; in many case, that cost is part of a more extensive operation (over a greater region, or even nationwide). Several factors contribute to the operational cost differences between the two types of networks. The costs of repair for instance are very different: repairing a Wi-Fi access-point usually requires bucket-crews since they are located high above on the traffic poles or street lamps while EVDO electronic equipment is on the ground. Trends also seem to show that the

frequency of repair of a relatively cheap access-point is far higher than that of a more expensive EVDO cell site. Fewer cell-site locations and less equipment is always an operational advantage for software or hardware upgrade, and general maintenance issues. Finally scaling up a network to much wider areas and many more cities is more manageable with a network requiring fewer sites. The comparisons are summarized in table 11.2.

Table 11.2: Network cost comparison between EVDO and Wi-Fi for the city of Longmont.

Item EVDO Wi-Fi

Spectrum $1.4 million $0No. of cell sites 5 600Tower $200k $0kSite prep $50k $500Equipment $120k $3k

Total Build-out $2 million $2 million

Power $15k $80Real estate leases $3k $0Backhaul $2.4k per site $14k tot

Monthly Recurring Cost $102,000 $62,000

Field techs 2 5NOC techs 1 1Tech support 2 3

Yearly Operations $2.7 million $4 million

The comparison is actually rather close, and does not show one roll-out as a clear winner. In practicality, however, lower operational expenses, clean spectrum insuring reliable service make a big difference that seem to outweigh the free aspects of client devices and spectrum. Since then, EV-DO evolved into LTE, while in January 2008, Kite Network could no longer manage the Wi-Fi network, opened it for free use, and put it on the auction block. 3

Other Successes and Failures

Other municipal initiatives were reported in the news in past years: nearly every city has released a request for proposal to deploy city-wide Wi-Fi. Some requests were targeted on public safety respondent, including use in a specific spectrum block available for free for that purpose (4.9GHz). Some requests requested complete city coverage extending over hundredth of square

miles. For instance in Albuquerque and surrounding cities, a request even included the build-out of areas where no homes were yet built: it was argued that the presence of wireless network would be an incentive for builders. Of course designing a wireless network before buildings are in place makes no sense. 4

Not all large Wi-Fi coverage failed though. Some cities and large campuses were fairly successful. The successful initiatives seem to have a few facts in common: 1) a reasonable target build-out, such as campus, or city public buildings, and 2) an anchor tenant, such as city employees, insuring some minimum revenue stream to sustain operational expenses.

Other Factors

Of course our business analysis is limited here to the roll-out of the existing networks as we tested them. Many additional performance optimizations methods could be considered for both networks. Another important cost aspect is the cost of acquisition of new customers, including activation, credit check, billing, and the distribution of client device. Again, the latter gives a significant advantage to Wi-Fi given the major penetration of Wi-Fi devices.

Other considerations should include usage needs and patterns, especially around indoor coverage. The majority of data need is still indoor (at home or at work ). Indoor coverage from outdoors access points are very difficult to build reliably (recall link budgets and indoor penetration).

Outdoor needs have been well met by mobile devices, but recent demand for capacity is pushing again for small cells (including Wi-Fi). Outdoor usage is of course limited by weather and temperature, which limit revenue opportunities.

Table 11.3:Typical temperatures and weather conditions in major US cities show the seasonal

nature of demand for capacity (gathered from www.weatherbase.com).

City Sunny days Rainy days Days above 90F Days below 32F

Denver 115 89 34 155Dallas 136 79 100 39San Francisco 140 67 2 30San Diego 147 42 4 0Seattle 71 150 1 20

11.1.3 Radio Parameters Analysis and Modeling

Wi-Fi coverage and capacity considerations are similar to those of any cellular network, and even more stringent since only 3 non-overlapping channels are available for a reuse factor of 3 (Although reuse factors of 4 with channels 1, 4, 8, 11 have been rolled-out, e.g. in Longmont).

Figure 11.3: Wi-Fi access points in a Longmont residential area.

In an initial design phase, a simple one-slope model and low-resolution terrain data suffice for a rough estimate to qualify customers. As operations progress, actual measurements should be compared to predictions and the process should be refined further.

The variations in RSSI as a function of distance were measured in a residential neighborhood and are shown here. Note that measurements taken in the street may show a fairly low slope (due to the street corridor effect).

Figure 11.4:Received power level signal strength indicator (in dBm) of a Wi-Fi system as a

function of distance (on a logarithmic scale).

Figure 11.5: Wi-Fi physical modulation rate as a function of distance (on a logarithmic scale).

11.1.4 Throughput Measurements

Having now characterized RF levels we focus on data throughput. Throughput is affected by distance, shadowing, and interferences. The parameter of importance is the signal to noise ratio (SNR); it can be estimated from RSSI and ambient noise measurements, or can usually be reported in some form by the RF equipment.

The standard deviation of measured signal strength is also interesting to consider. In most cellular trials, mobile data is collected, which makes it impossible to quantify variations over long periods of time for a given location. In a population of fixed location, however, a measured standard deviation over a long period may be useful in predicting seasonal changes in the radio channel.

11.2 WiMAX System Performance

Service providers are in an intensive phase of trials and performance evaluation for fixed WiMAX systems and services. Initial technical evaluation showed promising data rates, and a number of more wide-scale trials were conducted on larger customer base throughout the world, in Europe, Asia, and the Americas.

11.2.1 Data Rates

IEEE 802.16 and WiMAX profiles allow for several radio channel bandwidths, which lead to very different data rates. In a given profile, physical layer data rate of a WiMAX system is determined by the type of modulation and coding: from BPSK 1/2 to QAM64 3/4. Theoretical data rates are quoted in standards or by manufacturers, but actual throughput vary with suppliers: a degradation of 40% to 50% is often observed. Table below summarizes typical data rates observed in a 3.5 MHz FDD channel (also see figure 11.12 on page §). That seemingly large difference is mainly due to timing delays necessary for scheduling and collision avoidance between users. Actual data results vary with suppliers, and interoperability between suppliers introduce even greater variations. Nevertheless the great value of WiMAX certified products is

to guarantee some minimum performance: a service provider may rely on the fact that WiMAX certified products will work well with other suppliers certified for the same profile.

Table 11.4:WiMAX 3.5 MHz channel maximum theoretical and actual measured throughput

(at 3.5 GHz).

Modulation 3.5MHz sensitivity SNR Theoretical Actual(dBm) (dB) (Mbps) (Mbps)

BPSK 1/2 -90.6 6.4 1.41 0.86BPSK 3/4 -88.6 8.5 2.1 1.28QPSK 1/2 -87.6 9.4 2.82 1.72QPSK 3/4 -85.8 11.2 4.23 2.5816QAM 1/2 -80.6 16.4 5.64 3.4416QAM 3/4 -78.8 18.2 8.47 5.1664QAM 2/3 -74.3 22.7 11.29 6.8864QAM 3/4 -72.6 24.4 12.71 7.74

These results are for one direction 3.5 MHz channel, a full duplex FDD system may see up to twice as much throughput in the total 7 MHz bandwidth. Of course different profiles and channel widths lead to different throughput results. An unlicensed TDD 10 MHz channel profile for instance has the advantage of adapting to asymmetrical data demand. Similar benchmark tests show that such a system is also capable of throughput around 8 Mbps (see §11.2.7 and specifically figure 11.13).

Interferences from other cells (co-channel interferences) strongly impact actual rates [129] [130]. And in unlicensed cases, unwanted interferences in the band are also a concern: minimum signal to noise ratios listed in the table must be maintained for given throughput.

In order to compare system performance in diverse environments, tests are usually conducted with traffic load generators and fading emulators. Service providers can thus create repeatable benchmark tests, in a controlled environment, in order 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. Terrain Types are (from [25]) defined as follows. A: the maximum path loss category, hilly terrain with moderate-to-heavy tree densities; B: the intermediate path loss category, hilly with light tree density or flat with moderate-to-heavy tree density; C: the minimum path loss category, 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.

Table 11.5:

Typical parameters for SUI-1 to 6 channel models (delay spread values estimated for 30-degree antennas azimuthal beamwidths, and Ricean K-factors are for 90%

cell coverage — from [26]).

Channel TerrainRMS Delay Spread Doppler Ricean K

Model Type (μs) Shift (dB)

SUI-1 C 0.042 (Low) Low 14.0SUI-2 C 0.069 (Low) Low 6.9SUI-3 B 0.123 (Low) Low 2.2SUI-4 B 0.563 (High) High 1.0SUI-5 A 1.276 (High) Low 0.4SUI-6 A 2.370 (High) High 0.4

11.2.2 Experimental Data

Fade emulators can be used in lab environment can recreate the above SUI fading profiles. Radio systems can then be evaluated in different fading environments.

Figure 11.6:

Throughput variations in time of a WiMAX radio system at 5.8 GHz in two different SUI fading models: SUI-1 (left) and SUI-3 (right). Radio system

modulation was fixed to 64QAM-2/3.

Radio system under test comprises one base station (BS) and several subscriber stations (SS’s). The air interface is a short direct line of sight and is then sent through the fading emulator. Different fading channels are programmed in the emulator. The Fading emulator emulates two separate channels (forward and reverse links), each comprised of several multipaths, each of which is independently faded and delayed. Fade statistics for the direct path are either Rayleigh or Ricean, delayed paths are attenuated and Rayleigh faded as specified by SUI models. As in many wireless LAN devices, our radio devices are TDD and have duplex ports: transmit and received signals are cabled to the same antenna. In this test, because of the unidirectional nature of the fade emulator, transmit and receive paths are separated by circulators and faded by two independent channels. Additional attenuation (pad) is added where necessary.

Finally a traffic generator is connected (via 100bT Ethernet) to the BS and laptops are connected to SSs for data collection. Figure 11.7 shows the detailed setup.

Figure 11.7: Fade emulator setup between base and several clients.

11.2.3 Field 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 wireless 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 targeted 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 a 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 meter 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 sub-meter accuracy can be obtained at 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 these drive-test optimizations, are usually based on mobile data.)

11.2.4 Other Trial Considerations

In many cases trial data are published and compared to existing models, or (if extensive enough) they are used to create a new propagation model. Many other aspects of major customer trials are important to service providers, such as: customer qualification, installation, support, troubleshooting, and overall estimation of customer satisfaction.

The overall trial goal makes a significant difference in trial results: the customer selection process for instance may focus on capacity limitations in a specific area, or it may be geared towards testing distance limits of a radio system; clearly trial results will be different.

Trial architectures vary. Most WiMAX radio systems use Ethernet network interfaces, but many systems require a mixture of backhaul or longhaul transport, which include microwave, copper, or fiber links, over TDM T1, T3, SONET, etc.

Integration to a monitoring system is also a major portion of a technical trial. Major network element (including customer devices) should be monitored. Maintenance, repairs, and upgrades should be performed in a low-intrusion maintenance window in order to limit the impact of down time.

Most network elements should be controlled remotely and centrally from a network operations center. Good control of network elements, including customer equipment, is precious for system support, especially when it reaches large scale.

Data collection is highly important for a trial. As a successful trial moves into production, ongoing data analyses are still important for network optimization.

Customer satisfaction surveys and focus groups are also an integral part of a complete trial; they should also continue into production phase and be compared to network quality metrics.

11.2.5 Radio Parameters Analysis and Modeling

In an initial design phase, a simple one-slope model and low-resolution terrain data suffice for a rough estimate to qualify customers. As operations progress, actual measurements should be compared to predictions and the process should be refined further.

For instance an initial selection process leads to the chart on figure 11.8. Actual measurements show the right trend, but some variations are very large (sometimes in excess of 20 dB). Better modeling and drive testing should be considered in this case.5

Figure 11.8:Actual RSSI (in dBm) measured at customer locations versus predicted RSSI (in

dBm) from planning model.

During trial, a received signal strength indicator (RSSI), in dBm, is logged at all customer locations. A plot of RSSI as a function of the logarithm of distance is graphed in figure 11.9. The logarithmic scale for the distance is simply justified by the fact that a one-slope model will show a linear approximation on the graph. Many propagation studies use this scale since it allows for easy comparison of path loss exponents. The variations in RSSI for a given customer location are represented by error bars at each point. Each error bar represents a standard deviation; that is, the total width of the error bar shows two standard deviations.

Figure 11.9:Received power level signal strength indicator (in dBm) as a function of distance

(on a logarithmic scale).

The next step in data analysis is a comparison between the data set and typical models. For that comparison, a path loss estimate should be derived from the empirical system. The RSSI

measurement provides one term of the path loss. The other is in the transmitted power level, which depends on base station power, cable loss, antenna pattern, and even (to a small extent) on the deviation from boresight of the sector’s antenna.6 Path loss estimates are represented in figure 11.10.

Figure 11.10:Empirical path loss (in dB) as a function of distance (on a logarithmic scale), and

comparison to prediction models.

Approximation of path loss to a one-slope model leads to the following equation:

(11.1)

with d0 = 1 km. The trial environment is compared to typical cellular models as discussed below.

Path loss exponent is approximately n = 2.7. The Walfish-Ikegami model for line of sigh in urban corridors predicts n = 2.6. Other reports have shown similar results for 3.5 GHz: [59] reports values of n between 2.13 and 2.7 for rural and suburban environments, [61] reports n = 3.2. But many other models predict higher exponents n between 3.5 and 4.5. (See path loss exponents in table 1.1).

Otherwise, approximations are fairly good with Erceg-B and C models. Erceg-B is the best fit and is represented on figure 11.10.

The most popular method to compute slope estimate is a least square error estimate. In that method a set of error terms {ei} is defined between each data point and a linear estimate. Minimizing the sum of these errors yields the slope and intercept, which intuitively gives a good approximation of the data set. That method also benefits from the following important properties [131]:

1. Least square estimated slope and intercept are unbiased estimators. 2. Standard deviations of the slope and intercept depend only on the known data points and

the standard deviation of the error set {ei}.

3. Estimated slope and intercept are linear combinations of the errors {ei}.

From the last point, if we assume that the errors are independent normal random variables (as in a log-normal shadowing situation) the estimated slope and intercept are also normally distributed. If we assume more generally that the data points are independent, the central limit theorem implies that for large data sets, the estimated slope and intercept tend to be normally distributed.

For the last assumption to be true, very low correlation of the wireless channel must exist between data points. This is the case when data points are measured at fixed locations tens or hundreds of meters apart — in which case measurements show very low correlations between the respective fading channels. Similarly, this is the case even in a mobile cellular environment, from one cell to another.

The important conclusion is that path loss exponent is approximated by a normal (or Gaussian) random variable.

We also verify a few more key findings as in [25], for a 3.5 GHz fixed link:

1. Free-space approximation (PL0 = 20×log ) works well within 100 m. 2. Path loss exponent depends strongly on height of transmitter (mobile height being more

or less constant throughout). 3. Variations around median path loss are Gaussian within a cell (Log-normal shadowing),

with standard deviation σ ≈ 11.7 dB. 4. Unfortunately, our limited number of cells does not allow us to quantify the nature of the

variations of σ over the population of macro cells.

11.2.6 Throughput Measurements

Having now characterized RF levels we focus on the parameter of most interest: data throughput. Throughput is affected by distance, shadowing, and interferences. The parameter of importance is the signal to noise ratio (SNR); it can be estimated from RSSI and ambient noise measurements, or can usually be reported in some form by the RF equipment. The SNR has a direct impact on the modulation used by the link7 and therefore on the throughput of that link. That throughput is graphed as a function of distance in figure 11.11.

Figure 11.11:Throughput in Mbps measured at customer locations as a function of distance to

base station, with ten point moving average, and logarithmic fit.

In fact, modulation and throughput change from time to time. It may be important to study the statistical distribution of the resulting throughput, as in figure 11.12 (and 11.13). These figures show the probability of reaching a certain throughput, over the population of fixed location under test. These plots may be compared to plots representing fixed modulations and controlled fading environments described in 11.2.1. Fading statistics in suburban areas show close correlation with SUI models 3 and 5, and throughput density functions near those of 16QAM 3/4 in such fading environments [132].

Finally we report on the standard deviation of measured signal strength. In most cellular trials, mobile data is collected, which makes it impossible to quantify variations over long periods of time for a given location. In a population of fixed location, however, a measured standard deviation over a long period may be useful in predicting seasonal changes in the radio channel. Typical standard deviations in fixed links over several months vary between 1 and 6 dB; when deciduous trees are present, the value increases in the spring as leaves come out. Trial data also show that the standard deviation tends to increase with distance. A median value of the standard deviation of path loss is given by:

(11.2)

with d0 = 1 km.

Seasonal variations are especially noticeable as leaves come out. The impact on the link budget has been reported for fixed wireless links [36] and in different wind conditions [33]. We measure some variations of the path loss exponent, the intercept, and the log-normal shadowing. In many cases the wireless system can adapt to these variations, but in some marginal locations where link budget nears the maximum allowable path loss, throughput is affected. As shown on figure 11.12, low bit rates are affected the most by changes in foliage.

Figure 11.12:Throughput cumulative distribution statistic measured in various foliage

conditions in a 3.5 MHz FDD channel at 3.5 GHz.

11.2.7 Unlicensed WiMAX

Although 806.16-2004 WiMAX equipment is certified conform at 3.5 GHz, other profiles exist, and it is important to mention unlicensed profiles at 5.8 GHz. Unlicensed operations were initially seen as a wonderful opportunity to achieve economies of scale. These profiles were scheduled to be standardized as a second wave after the 3.5 GHz profiles; unfortunately, as of summer 2007, they seem to have been put on hold indefinitely.

Nevertheless suppliers manufacture equipment at 5.8 GHz that follow these profiles, and have all the WiMAX properties except for certified interoperability between suppliers. The main profiles of these products is the following: 5.8 GHz, TDD 5 or 10 MHz channels, 256 or 512 FFT. Their performance is illustrated in this section.

Note that comparison between field and lab data may be interesting for further service predictions. One experiment made downtown Denver shows that the environment behaves like a SUI-3 model, and that adaptive modulation seems to maintain a radio link between QPSK and 16QAM.

Figure 11.13: Throughput cumulative distribution statistic measured in a 10 MHz TDD channel

at 5.8 GHz, for data measured in the field and data emulated in the lab.

Figure 11.14:Throughput statistical distribution in SUI-3 model at 5.8 GHz for several

modulations BPSK-1/2, QPSK-3/4, 16QAM-3/4, and 64QAM-2/3.