U. Glaeser

FIGURE 42.7 Farrow structure.
FIGURE 42.8 Frequency response of polynomial-based interpolator (from [18]).
calculation, Erup et al. [16] found polynomial-based interpolation to yield satisfactory results while
minimizing the hardware complexity. In this approach, the weights of the input samples are given as
polynomials in the variable µ k and can be easily implemented in hardware using the Farrow structure
[17] shown in Fig. 42.7. In this sructure, all the filter coefficients are fixed and polynomials in µ k are
realized by nesting the multipliers as shown in Fig. 42.7.
The signal contained in the imageband will cause aliasing after resampling; however, proper choice of
the coefficients in the Farrow structure can help optimize the frequency response of the interpolator for
a particular application. An alternative method to determine the filter coefficients is outlined in (see Fig.
42.8) [18].
Conclusions
Section 42.2 reviewed trends in the wireless communications industry towards high speed data communications
in both the macrocellular and the microcellular environments. The implication of these trends
on the underlying digital circuits will move designers towards dedicated circuits and ASICs to meet these
demands. As such the paper outlined the major signal processing tasks that these ASICs will have to
implement.
References
1. K. Pahlavan, et. al., “Wideband local access: wireless LAN and wireless ATM,” IEEE Commun. Mag.,
pp. 34–40, Nov. 1997.
2. J. Mikkonen, et. al., “Emerging wireless broadband networks,” IEEE Commun. Mag., vol. 36, no. 2,
pp. 112–17, Feb. 1998.
© 2002 by CRC Press LLC
X(m)
Ts
Ts
Ts
b3(i)
µ k
b3(0)
b3(1)
b3(2)
b3(3)
b 2 (i) b 1 (i) b 0 (i)
Y(k)