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Interpolation in Digital Modems-Part II: Implementation and Perform

ance

Lars Erup, Member, IEEE, Floyd M. Gardner, Fellow, IEEE, and Robert A. Harris, Member, IEEEIEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 41, NO. 6, JUNE 1993Adviser：高永安

Student：林柏廷

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Outline

Introduction Polynomial-based filter Filter structures Performance simulation Closed-loop simulation Summary

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Introduction (I)

Part I of this work[1] presented a fundamental equation for digital interpolation of data signals ：

Where ： basepoint index i ： filter index ： fractional interval

2

1

])[(])[(

])[()(I

IiskIsk

skki

TihTimx

TmykTy

[1] F.M. Gardner, “Interpolation in digital modems- Part I: Fundamentals,” IEEE Trans. Commun., vol. 41, pp. 502-508, Mar. 1993.

km

k

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Introduction (II)

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Introduction (III)

This paper, in Section II, explores properties of simple polynomial-based filters - a category that includes the classical interpolating polynomials.

Several digital structures for performing the computations in (1) have been considered, and are discussed in Section III.

Simulations have been run on several candidate filters to evaluate their effect on modem performance, with results given in Section IV.

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Introduction (IV)

The design of a complete timing control loop, based on the NCO method devised in [l], is illustrated in Section V.

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Polynomial-based filter (I)

An extensive literature exists on polynomial interpolation, which is a small but important subclass of polynomial-based filters.

They are easy to describe. Good filter characteristics can be achieved. A special FIR structure (to be described below)

permits simple handling of filter coefficients. The structure is applicable only to polynomials.

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Polynomial-based filter (II)

Any classical polynomial interpolation can be described in terms of its Lagrange coefficients.

The Lagrange coefficient formulas constitute the filter impulse response hI(t), so that the latter is piecewise polynomial for classical polynomial interpolation.

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Polynomial-based filter (III)

The time-continuous impulse response associated with a linear interpolator is simply an isosceles triangle, with hI1(0) = 1,and basewidth of 2Ts ; see Fig. 1.

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Polynomial-based filter (IV)

Its frequency response is plotted in Fig. 2.

2

1

sin

fTs

TsTfH sI

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Polynomial-based filter (V)

Fig. 3 shows a signal at 2 samples/symbol with 100% excess bandwidth, together with the frequency response of the cubic interpolator.

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Filter structures (I)

Linear interpolation, or the four-point piecewise-parabolic impulse response provide simple formulas and are well-suited for rapid direct evaluation of filter coefficients.

Linear interpolation filter can be performed by the simple formula

kkkk

kkkki

mxmxmx

mxmxkykTy

1

11)(

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Filter structures (II)

Another approach, better suited for signal interpolation by machine, has been devised by Farrow[10]. Let the impulse response be piecewise polynomial in each Ts,-segment, i = I1 to I2 :

lk

N

llskII ibTihth

0

)(

[10]C.W. Farrow, “A continuously variable digital delay element,” in Proc. IEEE Int. Symp. Circuits & Syst., Espoo, Finland, June 6-9, 1988, pp. 2641-2645.

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Filter structures (III)

For a cubic interpolation

imxiblvlv

imxib

ibimxky

k

I

Iil

N

l

lk

k

I

Iil

N

l

lk

lk

N

ll

I

Iik

2

1

2

1

2

1

,

)()(

0

0

0

01}23{)( vvvvky kkk

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Filter structures (VI)

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Filter structures (V)

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Filter structures (VI)

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Performance simulation (I) Fig. 7 shows, as an example, the frequency respons

es of a 註 1 filter with and without compensation for the CUF of a linear interpolator at 2 samples/symbol.

100%CRO

註 1 = root cosine roll-off filter with 100% excess bandwidth.100%CRO

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Performance simulation (II)

Most results were obtained using semi-analytic error probability estimation, using BPSK modulation and averaging over 1023 symbols (the data source was a PN sequence generated by a 10-stage shift register).

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Performance simulation (III)

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Performance simulation (IV)

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Performance simulation (V)

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Closed-loop simulation (I)

we employed the Data-Transition Tracking Loop (DTTL) algorithm [11,12] to compute the timing error signal . For BPSK, the expression is

[11] F.M. Gardner, “A BPSWQPSK timing-error detector for sampled receivers,”IEEE Trans. Commun., vol. COM-34, pp. 423429, May 1986.[12] W.C. Lindsey and M.K. Simon, Telecommunications Systems Engineering. Englewood Cliffs, NJ: Prentice-Hall, 1973, ch. 9.

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11

kxkxkxku dd

u

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Closed-loop simulation (II)

Where is the real part of the sampled baseband signal, is a hard-decision on , is an symbol index.

xdx 1

x k

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Closed-loop simulation (III)

Fig. 14 shows (additive noise-free)s-curves of the DTTL algorithm for BPSK signals with unit power at the input to the receiver filter.

101010…. Data sequence

pseudorandom sequence

with 50% CRO filtering

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Closed-loop simulation (IV)

Consider the loop in tracking mode, with the NCO receiving a constant control value and overflowing at regular intervals 註 2.

Input to the loop is a phase step of one-quarter symbol period. Fig. 15-17.

Response to a step in symbol frequency of 0.1%. Fig 18-20.

1W

註 2 The NCO in [1] had modulus 1 whereas the one considered here has modulus Ti/Ts.

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Closed-loop simulation (V)

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Closed-loop simulation (VI)At 4 samples per symbol, 0.1% of the symbol rate corresponds to a shift of 1 sample in 250 symbols. This is confirmed by Fig. 20.

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Summary (I)

The Farrow structure [l0] appears to be particularly interesting for high-speed operation as it only requires the transfer of a single control value and uses a small number of multiplications.

For more critical applications, four-point interpolation using piecewise-parabolic interpolating filters (α = 0.5) gives excellent performance with only moderate complexity increase.

[10]C.W. Farrow, “A continuously variable digital delay element,” in Proc. IEEE Int. Symp. Circuits & Syst., Espoo, Finland, June 6-9, 1988, pp. 2641-2645.

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Summary (II)

Closed-loop simulations have shown that the NCO-based control system has many similarities to a conventional analogue phase-locked loop.