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    这是一个关于移动通信工程ppt,主要介绍Random Variables Related to Mobile-Radio Signals、Phase-Correlation Characteristics、Characteristics of Random FM、Click-Noise Characteristics、Simulation Models。欢迎点击下载哦。

    CHAPTER7 Received-Signal Phase Characteristics
    A signal s0(t) received at the mobile unit can be expressed:
                               s0(t) = m(t)s(t) (7.1)
    The long-term-fading factor m(x) is extracted from s0(t) and the resultant
    can be expressed:
                                  s(t) = r0ejψ(t) (7.2)
    where r0(t) and ψ(t) are the envelope and phase terms, respectively. The
    characteristics of r0(t) have been discussed in Chap. 6. The phase and
    time derivative of ψ(t), ψ・ (t) = dψ(t)/dt, is the random FM that is
    described in this chapter.
    Assume that aj(t) is the jth wave arrival, then s(t) represents the sum
    of all wave arrivals, as has been shown in Eq. (6.10):
                                  s(t) = ∑a(t)=X1 + jY1 (7.3)
    where X1 and Y1 are as defined in Eqs. (6.11) and (6.12), respectively.
    Hence ψ1(t) can be defined:
    The terms X1 and Y1 of the signal s(t) are two independent Gaussian
    variables with zero mean and a variance of σ2. This means that:
                                       E[X1Y1] = 0 (7.5)
                                     E[X12] = E[Y12] = σ2 (7.6)
    But when two signals s1 = s(t) and s2 = s(t + τ), expressed
                               s1(t) = X1 + jY1 = r1ejψ1 (7.7)
    and                  s2(t) = X2 + jY2 = r2ejψ2 (7.8)
    are correlated, then E[X1X2] and E[X1Y2] are not necessarily zero. Consequently,
    it is first necessary to find the covariance matrix for these
    random variables, and then the characteristics of ψ(t) and ψ (t) can beintroduced.
    7.1 Random Variables Related to Mobile-Radio Signals
    7.2 Phase-Correlation Characteristics
    7.3 Characteristics of Random FM
    7.4 Click-Noise Characteristics
    7.5 Simulation Models
    7.1.1Finding the covariance of random variables
    Since the ergodic process is always applied to random variables in the mobile-radio environment, then Eq. (2.80) can also be used here:
    From Eq. (7.9), the following expressions are obtained:
    Rc(τ) = E[X1X2] = E[Y1Y2]              (7.10)         
    Rs(τ) = E[X1Y2] = −E[Y1X2]            (7.11)
    Equations (7.10) and (7.11) have been used in Chap. 6 for calculating
    the covariances of random variables. Also E[s1s*2] can be expressed as in
    Eq. (2.99):
    where S( f) df is the average power that lies in the frequency range f,
    f + df. S( f) is often given in order to specify the spectrum of a given
    noise. Since the signals s1 and s2 shown in Eqs. (7.7) and (7.8) contain
    four random variables—X1, Y1, X2, and Y2—then the following expressions
    of covariances can be derived from Eq. (7.12):
    Following Rice’s notations
    R′c(τ) = E[X1X2] = E[Y1Y2] = −E[X1X2] = −E[Y1Y2] (7.15)
    R′cs(τ) = E[X1Y2] = E[Y1X2] = −E[X1Y2] = −E[X1Y2] (7.16)
    R″c (τ) = −E[X1X2] = −E[Y1Y2] (7.17)
    R″cs(τ) = E[Y1X2] = −E[X1Y2] (7.18)
    When τ = 0, the moments can be found from these correlation functions:
    The other functions we can see  in the p184
    7.1.2Power spectra of a signal s(t )
    E[s1s*2] can also be obtained from S( f) in Eq. (7.12). Sometimes it is
    much easier to calculate E[s1s*2] by using S( f) than by time-averaging
    from Eq. (7.9) as described in Chap. 6. Therefore, the expression of S( f)
    must be determined.
    Assume that all waves are traveling in the horizontal plane and that the antenna is pointing in a horizontal direction with an azimuthal antenna beam angle of γ. The power contributed to the the received signal by waves arriving in the horizontal plane within the angle dφ is the power arriving in that angular interval that would normally be received by an isotropic antenna of the same polarization:
                             Ss(φ) dφ = Ap(φ)G2(φ − γ) dφ (7.23)
    where A is a constant that will be defined later on, p(φ) is the angular
    distribution of wave arrival, and G(φ − γ) is the antenna pattern. The
    power spectral density Ss( f) is [2]
                             Ss( f) = Ss(φ) | dφ /df|           (7.24)
    The covariance E[s1s*2 ] can be obtained by Eq. (7.12), and the average
    power is                                                                     
    7.1.3Covariance matrix
    7.2 Phase-Correlation Characteristics
    There are several characteristics that can be used to describe ψ(t).
    The terms X and Y in Eq. (7.4) are the real and imaginary components
    of the mobile-radio signal s(t). These two components are Gaussian relationships, as shown in Eqs. (7.5) and (7.6). Then, the distribution
    of the random process ψ(t) can be found from Eq. (2.40):
                                              0 ≤ ψ ≤ 2π     others 0              (7.46)
    Hence, ψ is uniformly distributed, as shown in Fig. 7.2.
    When there are two phase angles, ψ1 and ψ2, of the signals s1 and s2,
    then the distribution of ψ1 and ψ2, assuming the two signals are correlated,
    where p(r1, r2, ψ1, ψ2) is similar to, but not the same as, the form shown
    in Eq. (6.88).
    Assuming that
    E[X12] = E[Y12] = E[X22] = E[Y22] = σ2 (7.48)
    Phase-correlation characteristics of ψ(t)
    If there is  correlation between s1 and s2 :
                                                               0 ≤ ψ1, ψ2 ≤ 2π                                                          
                 others 0                                                                   (7.49)
    If there is no correlation between s1 and s2, then Rc(τ) and Rcs(τ) are
    zero. Thus, Q = 0, from Eq. (7.53), and |Λ|1/2 = σ4, from Eq. (7.45). When
    these two values are inserted into Eq. (7.52), p(ψ1, ψ2) becomes:
                                     p(ψ1, ψ2)= 1/4π2=p(ψ1)p(ψ2)                 (7.51)
    which is what is normally expected.
    Phase correlation between frequency and time separation
    In expressing the phase correlation between frequency separations Δω
    and time separations τ, the phase correlation can be expressed:
    where p(ψ1, ψ2) is as shown in Eq. (7.52) and the covariance matrix elements are obtained from Eq. (7.44), but with Q expressed differently
    from Eq. (7.53), as follows:
                                     φ = tan−1 (−Δω Δ)                                (7.57)
    and Rcs(Δω, τ) = 0
    can be obtained from Eqs. (6.123) through (6.125). The integral of Eq.
    (7.55) cannot be carried out exactly, but an approximation can be
    shown [4]:
    where ρc = ρc(Δω, τ) is the normalized correlation ρc = Rc(Δω, τ)/σ2, which
    equals sqrt(ρr) in Eq. (6.126), expressed as:
    The function Ω(ρc) is plotted in Fig. 7.3. Equation (7.59) can be solved
    by using the information contained in Fig. 7.3.
    If it is assumed that p(ψ) = 1/2π, then the mean average values of ψ1
    and ψ2 are:
    and therefore the correlation coefficient can be expressed
    The function of Eq. (7.63) is plotted in Fig. 7.4.The coherence bandwidth Bc = (Δω)c/2π at values of τ = 0 and ρψ = 0.5 can be found from the data given in Fig. 7.4, where:
                                      ρψ(Bc, 0) = 0.5                                         (7.64)
    And                             Bc =1 /4πΔ                                              (7.65)
    The coherence bandwidth, according to the phase correlation, is onehalf
    of the envelope correlation value previously shown in Eq. (6.128).
    7.3 Characteristics of Random FM
    Probability distribution of random FM
    The term p(r, r・, ψ,ψ・ ) has been defined in Eq. (6.61), and therefore the
    probability density function (pdf ) of a random FM signal ψ・ , p(ψ・ ), can
    be derived from p(r, r・, ψ,ψ・ ) in combination with the following integrals:
    From Eq. (6.15) and Eq. (6.55), σ2 and ν2 for an E field signal are expressed:             σ2 = E[X2] = N                                                 (7.67)
    Then                                                                                               (7.68)
    The correlation coefficient p(ψ) of Eq. (7.68) is plotted in Fig. 7.5(a).
    The distribution of random FM can be expressed:
    By substituting the results of Eq. (7.67) for σ and ν of an E field signal
    into Eq. (7.69), the following is obtained:
    The response curve for the correlation coefficient for P(ψ・ ≤ Ψ・ ) is plotted in Fig. 7.5(b).
    Power spectrum of random FM
    The power spectrum of random FM can be obtained from the autocorrelation function described by Rice [1] and expressed:
    Assume that the angular wave arrival is uniformly distributed, that is,
    that p(φi) = 1/2π. Then the expression of Rc(τ) in Eq. (7.71) can be found
    from Eq. (6.108) for the case of the Ez field. The first and second derivatives, R′c(τ) and R″c (τ), can be obtained as follows:
    The power spectrum can be expressed as the Fourier transform of Rψ・ (τ)
    as follows:
    Equation (7.75) can be integrated either approximately or numerically.
    Figure 7.6 shows the curve obtained by integrating the autocorrelation
    function over three regions in the time domain [2].
    and therefore
    The corresponding baseband output noise due to random FM in an
    audio band (W1, W2) is:
    Hence, the average power of the random FM is a function of vehicular
    speed V and the audio bandwidth (W1, W2).
    In Fig. 7.6, the power spectrum of the random FM drops very drastically
    as soon as reaching the frequency
    Therefore, we can use this criterion to calculate the impact of the random
    FM versus the mobile speed. The criterion of considering the
    impact of random FM is
    Level-crossing rate (LCR) of random FM
    In order to obtain the lcr of random FM, it is first necessary to find
    The lcr of the random FM can then be found [1]:
    where b0, b2, and b4 are as shown in Eqs. (7.20) through (7.22).
    For the case where the noise is constant, with a value of η across the
    frequency band from f − β/2 to f + β/2, then the terms b0, b2, and b4 can
    be found by applying Eq. (7.19), as follows:
    7.4 Click-Noise Characteristics
    As the mobile unit moves through a mobile-radio field, the received signal
    fluctuates in both amplitude r and phase ψ. In a conventional FM
    discriminator, the fluctuations in phase ψ due to multipath fading generate
    noise in the signal. Such noise is called “random FM noise.” The
    random FM noise is essentially concentrated at frequencies less than 2
    to 3 times the maximum Doppler frequency. Outside this range the
    power spectrum falls off at a rate of 1/f. Davis [5] has found that random
    FM noise due to fading can be considered a secondary effect in the
    reception of mobile-radio signals.
    Another kind of noise, called “click noise,” may be found at the output
    of the FM discriminator.
    Click noise:
    7.5 Simulation Models
    Many radio-propagation models have been proposed that can be used to predict the amplitude and phase of radio signals propagated within a mobile-radio environment [8.20]. These models can be classified into two general categories. The first category deals only with multipathfading phenomena, whereas the second category deals with both multipath- and selective-fading phenomena. The following paragraphs describe some of the features associated with these two general categories of simulation models.
    Rayleigh multipath fading simulator:
    (A) Hardware simulator. Based on analyses of the statistical nature of a
    mobile fading signal and its effects on envelope and phase, a fading simulator can be configured either from hardware or a combination of hardware and software. Figure 7.12 shows a simple hardware configuration for a Rayleigh multipath fading simulator that consists of two independent Gaussian noise generators (GNG), two variable low-pass filters (VLPF), and two balanced mixers (BM). The cutoff frequency of the low-pass filter is selected on the basis of the frequency fb and the assumed average speed of the mobile vehicle V. The output of the simulator represents the envelope and phase of a Rayleigh-fading signal.
    (B) Software simulator. In a software-configured simulator [10], the
    model described in Sec. 6.2 is based upon, and Eqs. (6.10) through
    (6.13) are used to simulate, a Rayleigh-fading signal. In both Eg. (6.11)
    and Eq. (6.12), there are N Gaussian random variables for Ri and Si.
    Each of them has zero mean and variance one. Since a uniform angular
    distribution is assumed for N incoming waves, we let φi = 2π i/N. To
    simplify the simulator model, the direction α of the moving vehicle can be set to α = 0° without loss generosity, then the parameter ξi of the ith
    incoming wave is
    We have found that summing six or more incoming waves will perform
    a Rayleigh fading signal. We use nine evenly angular distributed
    waves, each of them is separated by 40° from the next wave arrival.
    Let the parameter t in Eq. (7.107) be
                                                 t = k ⋅ Δt                                             (7.109)
    and Δt is the sample interval that can be set as small as 10 sampling
    points in every wavelength.
                                                 V ⋅ Δt=λ/10                                        (7.110)
    Now Eq. (6.11) and Eq. (6.12) become
    The kth sample point of a Rayleigh envelope is:
    Equation (7.113) is the simulated Rayleigh fading signal plotted in
    Fig. 7.13 as the increment number k started from one. For our demonstration,a frequency of 850 MHz, the wavelength of which is 0.353 m,
    is given, and the sampling interval is λ/10 = 0.0353 cm. Each second
    will have m samples depending on the velocity of the mobile unit. The
    following table lists the mobile speeds that correspond to the number
    of samples per second.
    For any different mobile speed, the Rayleigh fading curve shown in Fig.7.13 remains unchanged. Only the number of samples on the x-axis is rescaled according to the different mobile speed conditions indicated above.The application of the software fading simulator (fader) [19] is described as follows. Since the random FM generally does not have an impact on the received signal, only the Rayleigh fading is considered.The modulated signal at the baseband is first digitized at its data rate Rb and then sent through the software fading simulator. For those data bits N1 above the threshold level of the simulator (fader), no errors
    occur. Those data bits N2 below the threshold will cause the errors depending on the states M of the modulation (M = 2L). The error probability for each bit is.
    Assume that the total number of bits is N; if so, the BER (bit error rate)
    can be obtained by
    The speed of the vehicle should not have an impact on the BER (see
    Sec. 16.14).We may verify this statement by using this simulator. The
    simulator also can evaluate the WER (word error rate) or FER (frame
    error rate) by grouping a sequence of bits called a frame or word. If  FEC (forward error correction) can correct 2 errors in a frame, then the
    frame has no error unless 3 or more errors occur. The simple signal
    process can generate the FER through this software fader very easily.
    Other similar software configuration simulations are shown by
    Smith [20] and Arredondo [21]. The main advantage in a softwareconfigured
    simulator is the ability to quickly change the operational
    parameters by merely making changes in the software program.
    Multipath- and selective-fading simulator
    A simulator of multipath and selective fading can also be configured
    either from hardware or a combination of hardware and software. The
    hardware version uses an arrangement of components similar to those
    shown in Fig. 7.12; the components are duplicated several times, and a
    delay line is added to each assembly [22]. The number of assemblies
    and delay lines is determined by the kind of environment that is being
    studied. The several delayed output signals from the Rayleigh-fading
    simulators are summed together to form a multipath- and selectivefading
    The software version [13, 17] is based on a model developed by Turin
    [17], in which the mobile-radio channel is represented as a linear filter
    with a complex-valued impulse response expressed:
    where δ(t − tk) is the delta function at time tk, and ψk and ak are the
    phase and amplitude, respectively, of the kth wave arrival. The terms
    ak, ψk, and tk are all random variables. During transmission of a radio
    signal s(t), the channel response convolves s(t) with h(t). Figure 7.14
    shows the mathematical simulation generated by the software for this
    model [14]. The simulator enables evaluation of mobile-radio system
    performance without the need for actual road testing, since it simulates
    the propagation medium for mobile-radio transmissions.
    Thank you


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