We’ve seen in §3.3 the impact of multiple rays on propagation models: this effect of multipath causes deep fades within small distances and is referred to as smallscale fading. Another important yet different cause of small scale fading is that of small frequency variations such as Doppler effect. Both of these effects are studied in this section. ^{1}
Multipath fading is significant for both mobile and fixed wireless systems. Intuitively that type of fading varies with surrounding scatterers which reflect differently the wavefront between transmitter and receiver. Practically, it is very important to quantify that aspect of the propagation environment, and even to taylor the standard to perform well in such an environment: for instance we’ll see later that the length of a transmitted symbol will be depending on the multipath situation in which it has to perform well.
In the time domain, multipath parameters can be seen as the spread of the arriving waves. In the frequency domain, the concept is less intuitive and relates to a coherence bandwidth, that is the width of the spectrum that is attenuated by a fade. The main parameters are summarized in table 4.1.

Flat or frequencyselective fading: Depending on the values of these above parameters and how they compare to the speed of transmitted symbol, the wireless channel will have flat fading (over the entire bandwidth used) or frequencyselective fading. This is of course a frequency domain interpretation describing what happens to the signal: it is either faded over the entire spectrum, or selectively only over a portion of it.
High or low delay spread: Again depending on the values of the above parameters and how they compare to the length of transmitted symbol, the wireless channel is said to have high delay spread (or heavy multipath), or low delay spread (low multipath). This simply says in the time domain what flat vs. frequencyselective fading said in the frequency domain.
Another aspect of wireless communication, different from the above, is the concept of how fast things are changing in the wireless channel. In the time domain, that aspect is referred to as time dispersion and is measured by coherence time; the coherence time describes how fast the wireless channel is changing. That aspect is important for estimating the quality of communication; for instance if the channel has a certain property, how long can we count on it? This defines for instance how often training sequences should be sent to estimate the wireless channel.
In the frequency domain the effect is best described by the Doppler spread: it describes how fast transmitter, receiver, and scatterers inbetween are moving; the faster they are moving, the faster the wireless channel changes, and the more Doppler shift will be present. The instantaneous Doppler shift depends on the wave frequency and the maximum radial speed of the many scatters within the wireless channel; with a radial speed v_{r} and wave incident at an angle θ:
 (4.1) 
Doppler shift distribution varies, but an approximation of the maximum shift is simply: f_{m} = v_{m}∕λ, where v_{m} is the maximum speed of the mobile (and λ the wavelength of the signal). And the Doppler spread is defined as twice the maximum Doppler shift: B_{D} = 2f_{m}, since the shift can be positive or negative depending on the direction of the motion.

Fast or slow fading Depending on the values of these above parameters and how they compare to the length of transmitted symbol, the wireless channel will have fast fading (faster than a transmitted symbol) or slow fading (one or several transmitted symbol during a fade). This is of course a time domain interpretation describing how fading time compares to transmitted symbol time.
High or low Doppler Again depending on the values of the above parameters and how they compare to the speed of transmitted symbol, the wireless channel is said to have high or low Doppler spread. This simply expresses in the frequency domain what fast vs. slow fading said in the time domain. It is important to understand the nonintuitive equivalence: for a given transmission rate, slow fading means long fades, meaning high coherence time, therefore low Doppler spread.
Wireless engineers might talk about fast fading meaning all of the above types of smallscale fading, simply because its variations are large over a small distance; this however should be avoided, instead always refer to it as smallscale fading (as it might be fast or slow).
It is important to reiterate the difference between the types of fading presented in the previous two section, and to understand that the characteristics presented in these two sections are completely uncorrelated. A wireless channel can be fast or slow and flat or frequencyselective. In either case, parameters above have to be compared to the transmitted symbol period (in the time domain) or to the data symbol baseband frequency (in the frequency domain).
To summarize, remember the following:
One more important aspect of the wireless channel and its smallscale fading deals with the distribution of angle of incidence to the receiving antenna. It is also referred to as angle of arrival. The rootmeansquare (RMS) spread for angle of arrival has an impact on the statistical diversity of the received signal. It is related to how far apart antennas need to be placed for instance in the design of a MIMO system. The dual of angular spread is a coherence distance D_{c}, beyond which paths are decorrelated enough (see for instance reference [119], p. 68); an approximate correlation distance relates to the wavelength and the rootmeansquared average of angles of arrival by:
 (4.2) 
Smallscale fading is caused by different reflections of the signal (delayed, frequency shifted, constructive or destructive) and is usually modeled by a random variable with a certain probability distribution.
Rayleigh Rayleigh fading channels are widely used in theoretical approaches as well as in empirical urban studies. They are generally accepted to model multipath environments with no direct line of sight (LOS). Given two random variable x and y Gaussian and zeromean, that represent some central limit theorem of a large number of multipaths (practically more than six), it is shown that the signal envelope or amplitude α = is Rayleigh distributed [8]. This means that the channel amplitude follows the Rayleigh distribution:
 (4.3) 
where Ω = is the mean square value of the random variate α.
The signal power is then related to α^{2}. When the noise spectral density N_{0} is assumed to be onesided Gaussian, the SNR has exponential distribution [65]: let us use as a measure of signal to noise ratio, SNR: γ = α^{2}E_{s}∕N_{0}
 (4.4) 
with γ ≥ 0, and λ = 1∕E(γ).
We can estimate the parameter λ for the exponential distribution in some way, such as by matching the first two moments with sample data {X_{i}}, of mean m_{X} and standard deviation σ_{X}.
 (4.5) 
Rice The amplitude of a fading channel may have a dominant component; the faded amplitude is now given by α = . Its probability distribution is given by the Ricean distribution:
 (4.6) 
where I_{0}(z) is the modified Bessel function of the first kind of order zero. That channel model offers the advantage of having an additional parameter K that has a physical meaning; but the Bessel function makes its computationally difficult, and has no straightforward form for its power or SNR.
Nakagamim Similarly a Nakagamim fading channel is often used for fade channels:
 (4.7) 
α ≥ 0, the SNR then follows the distribution [8][65]:
 (4.8) 
γ ≥ 0, which is gamma distributed. The problem of estimating parameters is more complicated in this case (as discussed in [66]ch. 17.7). Still, moment matching estimates lead to:
 (4.9) 
Gaussian Although not usually used for fading, the normal (or Gaussian) distribution is given for comparison:
 (4.10) 
for which we may use the following simple estimates:
 (4.11) 
The estimate for is unbiased and corresponds to moment matching and maximum likelihood, and with large enough sample size the estimate for although biased is usually a good estimate.
Lognormal There is a general consensus that largescale fading may be approximated by lognormal distributions [12]. Its probability distribution is:
 (4.12) 
for which, the best estimates are simply obtained by change of variable Y = lnX and referring to the Gaussian case. A more complex approach would be to investigate Z = ln(X  Θ) but these estimations are more difficult and in many cases rather inaccurate ([66] ch. 14).
Weibull The flexibility and relative simplicity of Weibull distribution may also be convenient and leads to good data fit [67]:
 (4.13) 
To estimate parameters, the simplest approach is to follow Weibull’s method based on the first two moments about the smallest sample value [66]ch. 21.
The above probability distributions are useful for modeling fading in wireless channels. In some cases empirical measurements are taken, and finding the fading situation may be a difficult problem. We look in this section at a specific case of fading in urban cores, and measure different events.
Test drives are conducted throughout major US cities in which mobile handsets continually place calls on several major cellular service providers. For that purpose a van is outfitted with several handsets, each cabled to a roof antenna, these antennas are placed as far as physically possible from one another to limit interferences. A system is setup to place a 90second call on every handset, then remain idle for 30 seconds, and repeat the cycle. A wealth of data may be analyzed and compared; in particular we focus presently on the occurrence of dropped calls and call setup failures.
In this example we collect the rates of dropped calls and setup failures for major cities and service providers by driving between 1000 miles and 1500 miles (depending on the size of the city) on every major road and a portion of secondary roads. The data collected for dropped calls and setup failures is summarized in table 4.3.

Moment matching for the above probability density distributions lead to table 4.4.

A simple error estimate may be used to estimate differences between measured data and the different probability distribution functions covered above. The best fit (minimal error) is that of the gamma distribution; graphical representations also show a good fit.

Large scale shadowing is fading that occurs on a large scale, and which correspond to conditions that may vary as one turns a corner, moves behind a large building, or enters a building.
Largescale variations caused by shadowing of obstacles are shown to follow a lognormal distribution [12][13][14], which means that when measured in dB they follow a Gaussian distribution. Consequently shadowing effects are usually incorporated into path loss estimates by the addition of a zeromean Gaussian random variable, with standard deviation σ: N(0,σ), were σ is often estimated by empirical measurements. Commonly accepted values for σ are between 6 dB and 12 dB.
Measured values of σ itself seem to display Gaussian distribution as well, in their variations from one area to another, and depend on the radio frequency, the type of environment (rural, suburban, or urban), base station and subscriber station height. Many measurement campaigns have been conducted and reported in the literature, as summarized in table 4.5.

To describe a wireless link, one we often seek to establish link budgets: they provide detailed power information from transmitter to receiver; but they provide only a median value for these power levels, actual levels vary with many other parameters, including shadowing.

Usually a minimum signal strength is required to maintain service. In order to maintain signal strength above that level, an additional margin is added to the link budget. Jakes’ equation [64] is often used for an estimate of such excess margin, or fade margin. The assumption is that the shadowing statistic throughout the cell is lognormally distributed (i.e. values in dB are normally distributed):
 (4.14) 
The probability that x exceeds the threshold x_{0} (the receiver threshold that provides an acceptable signal) at a given radius R is
 (4.15) 
By integrating the probability density function from x_{0} to ∞, the edge reliability result is
 (4.16) 
Typically the threshold of interest x_{0} is lower than the median provided by the path loss model, and the value mx_{0} is an excess, positive amount (in dB), and is usually referred to as excess margin, or fade margin: F = m  x_{0}. Of course, for a fade margin of zero (m  x_{0} = 0) at a given R, the error function (erf) equals zero, resulting in 50% edge reliability.
Alternative representations of that formula sometime make use of the complementary error function or the Q function. ^{2}
 (4.17) 
Instead of an edge reliability, the reliability in the entire cell area is often more useful: the fraction of useful service area, F_{A}(R), within a circle of radius R where the received signal strength exceeds a threshold value x_{0} is the integration of the probability function over the area as shown below.
 (4.18) 
With the assumption that the mean value of the signal strength, m, behaves according to an r^{n} propagation law, then m = m_{R}  10nlog _{10}, where n is the propagation exponent value; and m_{R} (in dB) is the mean signal strength at the edge distance r = R (m_{r} is determined from the transmitter power, antenna heights, gain, and so on), thus the value m_{R} x_{0} in equation (4.19) corresponds to the excess margin at the edge). Substituting m into the probability density function gives the area reliability: (after substitution and integration by part):
 (4.19) 
where
 (4.20) 
where m_{R}  x_{0} is simply the excess margin at the edge.
Modulation techniques are a necessary part of any wireless system, without them, no useful information can be transmitted. Coding techniques are almost as important, and combine two important aspects: first to efficiently transmit the information, and second to deal with error correction (to avoid too many retransmissions).
A continuous wave signal (at a certain frequency f_{c}) in itself encodes and transmits no information. The variations of that signal (in phase, amplitude, or a combination thereof) is what transmits user information. In the frequency domain, these variations cause the occupied spectrum to increase, thus occupying a bandwidth around f_{c}. How to optimize the use of that bandwidth in various situations is an important part of a wireless system. Various modulation schemes and coding schemes are used to maximize the use of that spectrum for different applications (voice or high speed data), and in various conditions of noise, interference, and RF channel resources in general.
Classic modulation techniques are well covered in several texts [1][8], and we simply recall here a few important aspects of digital modulations (that will be important in link budgets).
The main digital modulations used in modern wireless systems are outlined in table 4.6.

Modulation is a powerful and efficient tool used to encode information. We’ll need a few simple definitions to clearly illustrate.
Higher order modulations can encode multiple bits in a symbol, but require higher SNR to decode. That tradeoff between bits encoded per symbol is often referred to as a measure in bits per Hertz (b/Hz), its relation to SNR is reminiscent of the Shannon bound seen earlier (§2.2.1).

Efficient coding schemes are the powerful engines behind the growth of the wireless industry. They have allowed wireless systems to be both spectrally efficient and robust in terms of error corrections. In particular, the cellular industry has used extensively families of very efficient coding schemes (e.g. convolutional coding in CDMA, turbo coding in 3G4G standards).
Second generation wireless systems like cdmaOne introduced the use of convolutional coding. The coding scheme provides an efficient redundant and errorcorrecting scheme. This is particularly useful for voice transmission, as errors might be recovered from without the need for retransmission that would cause deals and degrade voice quality.
Wireless data systems of higher rates often use turbo coding, which are a combination of two convolutional coders reading each other (the name comes from the turbocharged engine, which uses some of its output power to compress some air fed to the intake, and is somewhat reminiscent of the turbo coding diagram). Other coding schemes exist such as block coding, which use blocks of data, rather than continuous streams.


Convolutional coding and turbo coding are example of continuous coding schemes, where a bit stream is encoded into another bitstream, usually of greater speed (with a multiplier of 2, 3, 4 or more). The added number of bits can be seen as spreading the spectrum, and the information, which requires more data to transmit, but inherently contains useful redundancy properties (a form of time diversity). The decoding of such schemes was historically difficult and has become possible only with recent processing power (see for instance Viterbi algorithms [100]).
The combination of modulation and coding provides great flexibility between redundancy and throughput. Higher modulation increases spectral efficiency in good propagation condition; when conditions worsen, lower modulation help, but more redundant condign schemes are sometimes an efficient alternative. Combined, the two schemes can reach impressive efficiencies, close to Shanon’s limit (§2.2).
Link budgets are a convenient tool to compare power levels between different technologies and different systems. Terrain conditions for instance may cause great variations in how far a wireless system reaches; link budgets allows for good system comparison while removing some of these variations.
We will examine link budgets for popular new wireless access standards such as cdmaOne (IS95), cdma2000 (IS2000), EVDO (IS856) [68], LTE, and WiMAX. We will also discuss coverage and capacity tradeoffs, increasing throughput vs. capacity or coverage, soft handoff benefits and cost.
Link budgets from different radio manufacturers are sometimes difficult to compare because they use different terms and definitions (without always clearly specifying them). Always compare them to a common definition, and try to identify the following parameters.
ERP is smaller than EIRP by the amount of gain difference between an isotropic antenna and a dipole, that is 2.15 dB. (Indeed a dipole gain is 0 dBd=2.15 dBi, so any antenna gain G may be expressed in either unit with the simple conversion G(dBi) = G(dBd) + 2.15 dB, and EIRP=ERP+2.15 dB.)
Some care must be taken to define the receiver sensitivity, which is the lowest power level at which the received signal may be decoded, it is usually defined as a power level above ambient noise and interferences, and depends on several parameters such as bit rate, coding, error rate. It uses the following parameters:
 (4.21) 
Other similar and equivalent expressions my be derived for system sensitivity using the minimum SNR required in an RF channel or bandwidth B, in which case S = SNR ⋅ F ⋅ N_{0} or:
 (4.22) 
Different manufacturers present link budgets differently, and some analyses are required to reduce them to a common format. Still, transmitted EIRP, receiver sensitivity, fade or excess margin, and maximum allowable path loss can usually be found.
Equipment manufacturers typically claim a certain reverse link budget, which is studied by potential operator buyers in order to predict performance, coverage, capacity, and compare them with other equipment vendors. The reverse link lends itself well to a straightforward power budget, based on the mobile maximum transmit power and the base sensitivity level and the industry commonly admits that reverse link budgets are the basis for radio design, and the forward link is studied subsequently, simply in order to verify that it provides enough resources to be balanced with the reverse link.
Table 4.7 illustrates a CDMA reverse link budget. It compares the link budgets of cdmaOne (IS95, 2G) and cdma2000 (IS200, 3G), which are fairly similar on the reverse link (somewhat limited by backwards compatibility), the main difference shown here is in the required Eb/No, significantly improved by the presence of reverse pilot (see §8.3).
CDMA Reverse Link Budget

Equipment manufacturers sometimes do not provide forward link budgets, and argue that systems are usually reverselink limited. For voice systems, both reverse and forward link budgets should be balanced; the forward link budget should insure that a power allocation between devices within the cell is sufficient to provide enough capacity. For data systems, the revers link is typically used to define a maximum range, and the forward link allows to determine the corresponding download speeds.
Unlike in the reverse link, the entire power is not necessarily allocated to one remote client device: either a portion of orthogonal channels (CDMA or OFDMA) are allocated to it, or a certain percentage of the time (as in TDMA systems). The link budget should reflect the fact that only a portion of the transmitted EIRP is available (as seen in table 4.8).
CDMA Forward Link Budget

There are several differences between voice an data link budgets. Voice SNR requirements are typically higher, as higher data rates can benefit from increased coding efficiency (like turbocoding) and is less delaysensitive and can afford some retransmit. For instance a cdma2000 link budget for EVRC voice (9.6kbps) requires an Eb/No value around 4 to 5dB, which I can achieve a data rate around 38.4kbps (thanks to more efficient coding and retransmissions).
Data link budgets vary greatly with data rates, so it is important to specify the data rate expected given a certain channel bandwidth, modulation, and coding. Finally body loss associated with the handset near the head (or on the belt with a handsfree device) are usually omitted (of course this is somewhat debatable as the data device is still near the human body).
Whenever possible, operators use licensed spectrum for wireless communications. For instance CDMA systems are commonly used at PCS frequency (1.9 GHz) that allow operators to transmit high power levels. Link budgets in unlicensed bands are similar to the above but are usually limited by a lower maximum allowed EIRP set by government regulations.
Fixed WiMAX Reverse Link Budget

Assume that a call is dropped if the power received by all base stations is below a minimal power P_{min} = 110dBm. Assume that the system initiates handoff when base station B_{1} power drops below P_{HO} = 108dBm. The time required to complete the handoff is Δt=4s.
Question: above what speed v_{max} of the mobile would the call be dropped?
When the elevation angle α changes, slant path attenuation varies since the thickness of the atmosphere traversed by the radio link increases. That slant path attenuation is usually approximated by the cosecant law:

(Values are not in dB in the above equation).
Let us consider a system like Globalstar, operating at 1.6GHz, with satellites 1400km above ground, and with a rotation period of approximately 2 hours. (Also remember the average earth radius is approximately 6350km).
 (4.23) 
Assume the following values:
What is the noise floor of that conventional cdmaOne base station?
 (4.24) 
Assume the same values as above, and:
What is the noise floor of that system with LNA?