Signal Processing - May 2017 - 104

10

y (n )

y (n )

5

5

5

0

0

0

20

30

40

0

20

30

Y (k )

40

0

"

0

20

20

10

10

10

20

30

0

40

∠Y (k )

2

10

20

30

0

40

"

10

∠Y (k )

2

0

0

0

-1

-1

-2

-2

-2

20

30
(a)

40

10

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∠Y ′(k )

1

-1

10

40

Y ′(k )

2

1

1

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30

Y (k )

20

0

y ′(n )

10

"

10

10

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30
(b)

40

10

20

30

40

(c)

Figure 2. An example of the uncalibrated and calibrated SRC performance (N x = 64, I/D = 3/4) . (a) The time-domain ideal output sequence y (n), the
amplitude, and the phase of its corresponding DFT; (b) the uncalibrated time-domain output sequence yt (n), the amplitude, and the phase of its corresponding DFT; and (c) the calibrated time-domain output sequence y l(n), the amplitude, and the phase of its corresponding DFT.

alternating direction method of multipliers [8], which will not be discussed
further for brevity.

Experimental results
In this section, we evaluate the accuracy
performance of the proposed optimization scheme in terms of the MSE. The
amplitude MSE is defined as
MSE a = 10 log

y - F - 1 Yl
Ny

2
2

(12)

and the phase MSE of the spectrum is
defined as
MSE z = 10 log

angle (Y) - angle (Yl )
Ny

2
2

.

(13)
In the following experiments, CVX
package [9] is used for the optimization.
In our experiments, the e 1 can all be set
to the value within 3-4 for various cases.
Figure 2 presents an example to demonstrate the outputs of the SRC before and
104

after the calibration process in the time
and frequency domains. As seen from this
figure, the proposed calibration process
can obtain a more accurate estimation
for both the time-domain amplitude and
frequency-domain phase. In particular,
the calibration method can reconstruct
the DFT phase of the calibrated output. In
the last row of Figure 2, the phase of the
calibrated DFT, +Y l(k), is much similar
to the ideal one, +Y (k), compared to that
of the uncalibrated DFT, +Yt (k).
The quantitative performance evaluation is presented in Figures 3 and 4 for
decimation and interpolation, respectively. For both decimation and interpolation, the MSE of amplitude decreases
as input signal length increases. This is
because the number of available samples
used for calibration in (7) increases with
the sequence length to enhance the calibration effects. The MSE of the phase,
however, remains about the same level for
different input signal lengths. Since the
IEEE Signal Processing Magazine

|

May 2017

|

number of the manipulated frequencydomain parameters increases with
the increase of signal length by a similar amount, the MSE of the phase is not
decreased with the increase of sequence
length. In general, the proposed calibration method can consistently obtain significant reduction of conversion errors
in both the time-domain amplitude and
frequency-domain phase, which validates its robustness of conversion accuracy performance.
The accuracy is degraded when the
SRC factor decreases from 3/4 to 3/8. This
can be explained by considering the number of samples that are ignored or inserted
according to (1) or (2) has increased. For
example, the number of points being
truncated by (1) or being inserted with
a value of C 2 in (2) is doubled so that
the errors produced by yt (k) are accordingly increased. Because the calibration
process has to cope with more errors
produced by the ignoring or inserting



Table of Contents for the Digital Edition of Signal Processing - May 2017

Signal Processing - May 2017 - Cover1
Signal Processing - May 2017 - Cover2
Signal Processing - May 2017 - 1
Signal Processing - May 2017 - 2
Signal Processing - May 2017 - 3
Signal Processing - May 2017 - 4
Signal Processing - May 2017 - 5
Signal Processing - May 2017 - 6
Signal Processing - May 2017 - 7
Signal Processing - May 2017 - 8
Signal Processing - May 2017 - 9
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Signal Processing - May 2017 - 20
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Signal Processing - May 2017 - 28
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Signal Processing - May 2017 - 103
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Signal Processing - May 2017 - 111
Signal Processing - May 2017 - 112
Signal Processing - May 2017 - Cover3
Signal Processing - May 2017 - Cover4
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