EE 179, Lecture 20, Handout #35 Reducing ISI: Pulse Shaping ◮ A time-limited pulse cannot be bandlimited ◮ Linear channel distortion results in spread out, overlapping pulses ◮ Nyquist introduced three criteria for dealing with ISI. The first criterion was that each pulse is zero at the sampling time of other pulses. ( 1 t=0 p(t) = 0 t = ±kTb , k = ±1, ±2, . . . Harry Nyquist, “Certain topics in telegraph transmission theory”, Trans. AIEE, Apr. 1928 EE 179, May 16, 2014 Lecture 20, Page 1 Pulse Shaping: sinc Pulse ◮ Let Rb = 1/Tb . The sinc pulse sinc(πRb t) satisfies Nyquist’s first crierion for zero ISI: ( 1 t=0 sinc(πRb t) = 0 t = ±kTb , k = ±1, ±2, . . . ◮ This pulse is bandlimited. Its Fourier transform is 1 f P (f ) = Π Rb Rb ◮ Unfortunately, this pulse has infinite width and decays slowly. EE 179, May 16, 2014 Lecture 20, Page 2 Nyquist Pulse Nyquist increased the width of the spectrum in order to make the pulse fall off more rapidly. The Nyquist pulse has spectrum width 12 (1 + r)Rb , where 0 < r < 1. If we sample the pulse p(t) at rate Rb = 1/Tb , then p(t) = p(t) IIITb (t) = p(t)δ(t) = δ(t) . The Fourier transform of the sampled signal is P (f ) = 1 = ∞ X P (f − kRb ) k=−∞ EE 179, May 16, 2014 Lecture 20, Page 3 Nyquist Pulse (cont.) Since we are sampling below the Nyquist rate 2Rb , the shifted transforms overlap. Nyquist’s criterion requires pulses whose overlaps add to 1 for all f . For parameter r with 0 < r < 1, the resulting pulse has bandwidth Br = 12 (Rb + rRb ) The parameter r is called roll-off factor and controls how sharply the pulse spectrum declines above 21 Rb . EE 179, May 16, 2014 Lecture 20, Page 4 Nyquist Pulse (cont.) There are many pulse spectra satisfying this condition. e.g., trapezoid: |f | < 12 (1 − r)Rb 1 1 1 b P (f ) = 1 − |f |−(1−r)R rRb 2 (1 − r)Rb < |f | < 2 (1 + r)Rb 0 |f | > 1 (1 − r)R b 2 A trapezoid is the difference of two triangles. Thus the pulse with trapezoidal Fourier transform is the difference of two sinc2 pulses. Example: for r = 12 , f f − 12 Λ 1 P (f ) = 23 Λ 3 2 Rb so the pulse is p(t) = 9 4 2 Rb sinc2 ( 23 Rb t) − 14 sinc2 ( 21 Rb t) This pulse falls off as 1/t2 . EE 179, May 16, 2014 Lecture 20, Page 5 Nyquist Pulse (cont.) Nyquist chose a pulse with a “vestigial” raised cosine transform. This transform is smoother than a trapezoid, so the pulse decays more rapidly. The Nyquist pulse is parametrized by r. Let fx = rRb /2. EE 179, May 16, 2014 Lecture 20, Page 6 Nyquist Pulse (cont.) Nyquist pulse spectrum is raised cosine pulse with flat porch. 1 1 !! |f | < 2 Rb − fx 1 f − 2 Rb |f | − 21 Rb | < fx P (f ) = 12 1 − sin π 2fx 0 |f | > 12 Rb + fx The transform P (f ) is differentiable, so the pulse decays as 1/t2 . EE 179, May 16, 2014 Lecture 20, Page 7 Nyquist Pulse (cont.) Special case of Nyquist pulse is r = 1: full-cosine roll-off. P (f ) = 12 (1 + cos πTb f )Π(f /Rb ) = cos2 ( 21 πTb f ) Π( 21 Tb f ) This transform P (f ) has a second derivative so the pulse decays as 1/t3 . p(t) = Rb EE 179, May 16, 2014 cos πRb t sin(2πRb t) sinc(πRb t) = 2 2 1 − 4Rb t 2πt(1 − 4Rb2 t2 ) Lecture 20, Page 8 Controlled ISI (Partial Response Signaling) We can reduce bandwidth by using an even wider pulse. This introduces ISI, which can be canceled using knowledge of the pulse shape. The value of y(t) at time nTb is an−2 + an−1 . Decision rule: y(nTb ) > 0 1 a ˆn−1 = 0 y(nTb ) < 0 ′ (ˆ an−2 ) y(nTb ) = 0 A related approach is decision feedback equalization: once a bit has been detected, its contribution to the received signal is subtracted. EE 179, May 16, 2014 Lecture 20, Page 9 Partial Response Signaling (cont.) The ideal duobinary pulse is p(t) = sin πRb t πRb t(1 − Rb t) The Fourier transform of p(t) is P (f ) = πf f 2 Π e−jπf /Rb cos Rb Rb Rb The spectrum is confined to the theoretical minimum of Rb /2. EE 179, May 16, 2014 Lecture 20, Page 10 Zero-ISI, Duobinary, Modified Duobinary Pulses Suppose pa (t) satisfies Nyquist’s first criterion (zero ISI). Then pb (t) = pa (t) + pa (t − Tb ) is a duobinary pulse with controlled ISI. By shift theorem, Pb (f ) = Pa (1 + e−j2πTb f ) Since Pb (Rb /2) = 0, most (or all) of the pulse energy is below Rb /2. We can eliminate unwanted DC component using modified duobinary, where pc (−Tb ) = 1, pc (Tb ) = −1, and pc (nTB ) = 0 for other integers n. pc (t) = pa (t + Tb ) − pa (t − Tb ) ⇒ Pc (f ) = 2jPa (f ) sin 2πTb f The transform of pc (t) has nulls at 0 and ±Rb /2. EE 179, May 16, 2014 Lecture 20, Page 11 Zero-ISI, Duobinary, Modified Duobinary Pulses (cont.) Zero−ISI 1 0.5 0 −5 −4 −3 −2 −1 0 1 2 3 4 1 2 3 4 2 3 4 Duobinary 1.5 1 0.5 0 −5 −4 −3 −2 −1 0 Modified Duobinary 1 0.5 0 −0.5 −1 −5 EE 179, May 16, 2014 −4 −3 −2 −1 0 1 Lecture 20, Page 12 Zero-ISI, Duobinary, Modified Duobinary Pulses (cont.) 150 100 50 0 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 −1.5 −1 −0.5 0 0.5 1 1.5 2 −1.5 −1 −0.5 0 0.5 1 1.5 2 200 150 100 50 0 −2 200 150 100 50 0 −2 EE 179, May 16, 2014 Lecture 20, Page 13 Partial Response Signaling Detection Suppose that sequence 0010110 is transmitted (first bit is startup digit). Digit xk 0 Bipolar amplitude -1 Combined amplitude Decoded values Decode sequence Partial response signaling is susceptible 0 1 -1 1 -2 0 -2 0 0 1 to error 0 1 1 0 -1 1 1 -1 0 0 2 0 2 0 0 2 0 1 1 0 propagation. If a nonzero value is misdetected, zeros will be misdetected until the next nonzero value. Error propagation is eliminated by precoding the data: pk = xk ⊕ pk−1 . EE 179, May 16, 2014 Lecture 20, Page 14 Eye Diagrams Polar Signaling with Raised Cosine Transform (r = 0.5) P (f ) = EE 179, May 16, 2014 1 1 2 0 1 − sin π f − 12 Rb Rb !! |f | < 14 Rb ||f | − 21 Rb | < 12 Rb |f | > 34 Rb Lecture 20, Page 15 Eye Diagrams (cont.) Polar Signaling with Raised Cosine Transform (r = 0.5) The pulse corresponding to P (f ) is p(t) = sinc(πRb t) cos(πrRb t) 1 − 4r 2 Rb2 t2 1.5 1 0.5 0 −0.5 −1 −1.5 −2 −1.5 EE 179, May 16, 2014 −1 −0.5 0 0.5 1 1.5 2 Lecture 20, Page 16 Eye Diagram Measurements ◮ Maximum opening affects noise margin ◮ Slope of signal determines sensitivity to timing jitter ◮ Level crossing timing jitter affects clock extraction ◮ Area of opening is also related to noise margin EE 179, May 16, 2014 Lecture 20, Page 17
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