Communication Theory. I. Frigyes 2009-10/II. http://docs.mht.bme.hu/~frigyes/hirkelm hirkelm01bEnglish. Topics. (0. Math. Introduction: Stochastic processes, Complex envelope) 1. Basics of decision and estimation theory 2. Transmission of digital signels over analog channels: noise effects
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Communication Theory I. Frigyes 2009-10/II.
Topics • (0. Math. Introduction: Stochastic processes, Complex envelope) • 1. Basics of decision and estimation theory • 2. Transmission of digital signels over analog channels: noise effects • 3. Transmission of digital signels over analog channels: dispersion effects • 4. Analóg jelek átvitele – analóg modulációs eljárások (?) • 5. Channel characterization: wireless channels, optical fibers • 6. A digitális jelfeldolgozás alapjai: mintavételezés, kvantálás, jelábrázolás • 7. Elvi határok az információközlésben. • 8. A kódelmélet alapjai • 9. Az átvitel hibáinak korrigálása: hibajavító kódolás; adaptív kiegyenlítés • 10. Spektrális hatékonyság – hatékony digitális átviteli eljárások
Stochastic processes • Also called random waveforms. • 3 different meanings: • As a function of ξnumber of realizations: a series of infinite number of random variables ordered in time • As a function of time t:a member of a time-function family of irregular variation • As a function of ξ and t: one member of a family of time functions drawn at random
f(t,ξ3) f(t,ξ2) ξ f(t,ξ1) f(t1,ξ) f(t2,ξ) f(t 3,ξ) t Stochastic processes • example: 2 1 3
Stochastic processes: how to characerize them? • According to the third definition • And with some probability distribution. • As the number of random variables is infinite: with their joint distribution (or density) • (not only infinite but continuum cardinality) • Taking these into account:
Stochastic processes: how to characerize them? • (Say: density) • First prob. density of x(t) • second: joint t1,t2 • nth:n-fold joint • The stochastic process is completly characterized, if there is a rule to compose density of any order (even for n→). • (We’ll see processes depending on 2 parameters)
Stochastic processes: how to characerize them? • Comment: although precisely the process (function of t and ξ) and one sample function (function of t belonging to say ξ16)are distinguished we’ll not always make this distinction.
Stochastic processes: how to characerize them? • Example: semi-random binary signal: • Values : ±1 (P0=P1= 0,5) • Change: only at t=k×T • First density_: • Second::
45o 45o Continuing the example: In two distinct time-slots In the same time-slot
Stochastic processes: the Gaussian process • A stoch. proc. is Gaussianif its n-th density is that of an n-dimensional vector random variable • m is the expected value vector, K the covariance matrix. • nth density can be produced if are given • are given
Stochastic processes: the Gaussian process • An interesting property of Gaussian processes (more precisely: of Gaussian variables): • These can be realizations of one process at different times
Stochastic processes: stationary processes • A process is stationary if it does not change (much) as time is passing • E.g. the semirandom binary signal is (almost) like that • Phone: to transmit 300-3400 Hz sufficient (always, for everybody). (What could we do if this didn’t hold?) • etc.
Stochastic processes: stationary processes • Precise definitions: what is almost unchanged: • A process is stationary (in the strict sense) if for the distribution function of any order and any at any time and time difference • Is stationary in order n if the first n distributions are stationary • E.g.: the seen example is first order stationary • In general: if stationary in order n also in any order <n
Stochastic processes: stationary processes • Comment: to prove strict sense stationarity is difficult • But: if a Gaussian process is second order stationary (i.e. in this case: if K(t1,t2) does not change if time is shifted) it is strict sense (i.e. any order) stationary. As: if we know K(t1,t2)nth density can be computed (any n)
Stochastic processes: stationarity in wide sense • Wide sense stationary: if the correlation function is unchanged if time is shifted (to be defined) • A few definitions:. • a process is called a Hilbert-process if • (That means: instantaneous power is finite.)
Stochastic processes: wide sense stationary processes • (Auto)correlation function of a Hilbert-process: • The process is wide sense stationary if • the expected value is time-invariant and • R depends only on τ=t2-t1 for any time and any τ.
Stochastic processes: wide sense – strict sense stationary processes • If a process is strict-sense stationary then also wide-sense • If at least second order stationary: then also wide sense. • I.e.:
Stochastic processes: wide sense – strict sense stationary processes • Further: if wide sense stationary, not strict sense stationary in any sense • Exception: Gaussian process. This: if wide sense stationary, also in stict sense.
Stochastic processes: once again on binary transmission • As seen: only first order stationary (Ex=0) • Correlation: • if t1 and t2 in the same time-slot: • if in different:
e T Stochastic processes: once again on binary transmission • The semi-random binary transmission can be transformed in random by introducing a dummy random variable e distributed uniformly in (0,1) • like x:
Stochastic processes: once again on binary transmission • Correlation: • If |t1-t2|>T, (as e T) • if |t1-t2| T • so
-T T τ Stochastic processes: once again on binary transmission • I.e. :
Stochastic processes: other type of stationarity • Given two processes, x and y, these are jointly stationary, if their joint distributions are alle invariant on any τtime shift. • Thus a complex process is stationary in the strict sense if x and y are jointly stationary. • A process is periodic (or ciklostat.) if distributions are invariant to kT time shift
Stochastic processes: other type of stationarity • Cross-correlation: • Two processes are jointly stationary in the wide sense if their cross correlation is invariant on any time shift
Stochastic processes: comment on complex processes • Appropriate definition of correlation for these: • A complex process is stationary in the wide sense if both real and imaginary parts are wide sense stationary and they are that jointly as well
Stochastic processes: continuity • There are various definitions • Mean square continuity • That is valid if the correlation is continuous
Stochastic processes: stochastic integral • x(t) be a stoch. proc. Maybe that Rieman integral exists for all realizations: • Then s is a random variable (RV). But if not, we can define an RV converging (e.g. mean square) to the integral-approximate sum:
Stochastic processes: stochastic integral • For this
Stochastic processes: stochastic integral - comment • In σs2 the integrand is the (auto)covariancie-function: • This depends only on t1-t2=τ if x is stationary (at least wide sense)
Stochastic processes: time average • Integral is needed – among others –to define time average • Time average of a process is its DC component; • time average of its square is the mean power • definition:
Stochastic processes: time average • In general this is a random variable. It would be nice if this were the statistical average. This is really the case if • Similarly we can define
Stochastic processes: time average • This is in general also a RV. But equal to the correlation if • If these equalities hold the process is called ergodic • The process is mean square ergodic if
Stochastic processes: spectral density • Spectral density of a process is, by definition the Fourier transform of the correlation function
Stochastic processes: spectral density • A property: • Consequently this integral >0; (we’ll see: S˙(ω)>0)
x(t) y(t) FILTERh(t) Spectral density and linear transformation • As known in time functions output function is convolution • h(t): impulse response
Spectral density and linear transformation • Comment.: h(t<0)≡ 0; (why?); and: h(t) = F-1[H(ω)] • It is plausible: the same for stochastic processes • Based on that it can be shown : • (And also )
x(t) y(t) FILTERh(t) H(ω) Sy(ω) (its integral is negative) Spectral density and linear transformation • FurtherS(ω) ≥ 0 (all frequ.) • For: if not, there is a domain where S(ω) <0 (ω1, ω2) Sx(ω)
H(ω) ω Spectral density and linear transformation • S(ω) is the spectral density (in rad/sec).As:
Modulated signals – the complex envelope • In previous studies we’ve seen that in radio, optical transmission • one parameter is influenced (e.g. made proportional) • of a sinusoidal carrier • by the modulating signal . • A general modulated signal:
Modulated signals – the complex envelope • Here d(t) and/or (t) carries theinformation – e.g. are in linear relationship with the modulating signal • An other description method (quadrature form): • d, , a and q are real time functions – deterministic or realizations of a stoch. proc.
Modulated signals – the complex envelope • Their relationship: • As known x(t) can also be written as:
Modulated signals – the complex envelope • Here a+jq is the complex envelope. Question: when, how to apply. • To beguine with: Fourier transform of a real function is conjugate symmetric: • But if so: X(ω>0)describes the signal completly: knowing that we can form theω<0 partand, retransform.
↓„Hilbert” filter Modulated signals – the complex envelope • Thus instead of X(ω) we can take that: • By the way: • The relevant time function:
Modulated signals – the complex envelope • We can write: • The shown inverse Fourier transform is 1/t. • So • Imaginary part is the so-callerd Hilbert-transzform of x(t)
Modulated signals – the complex envelope • Now introduced function is the analyticfunction assigned to x(t) (as it is an analytic function of the z=t+ju complexvariable). • An analytic function can be assigned to any (baseband or modulated) function; relationship between the time function and the analytic function is
Modulated signals – the complex envelope • It is applicable to modulated signals: analytic signal of cosωct is ejωct. Similarly that of sinωct is jejωct. So if quadrature components of the modulated signal a(t), q(t) are • band limited and • their band limiting frequency is < ωc/2π (narrow band signal) • then NB. Modulation is a linearoperation in a,q: frequencydisplacement.
Modulated signals – the complex envelope • Thus complexenvelope determines uniquely the modulated signals. In the time domain • Comment: according to its name can be complex. (X(ω) is not conjugate symmetric around ωc.) • Comment 2: if the bandwidt B>fc, is not analytic, its real part does not define the modulated signal.) • Comment 3: a és q can be independent signals (QAM) or can be related (FM or PM).
X(ω) X(ω) X˚(ω) X̃(ω) ω Modulated signals – the complex envelope • In frequency domain? On analytic signal we saw.