Lecture 11: Discrete-Time Fourier Transform

1.1 Discrete-Time Fourier Transform The discrete-time Fourier transform has essentially the same properties as the continuous-time Fourier transform, and these properties play parallel roles in continuous time and discrete time. As with the continuous-time Four ier transform, the discrete-time Fourier transform is a complex-valued function whether or not the sequence is real-valued. Furthermore, as we stressed in Lecture 10, the discrete-time Fourier transform is always a periodic function of fl. If x(n) is real, then the Fourier transform is corjugate symmetric, which implies that the real part and the magnitude are both even functions and the imaginary part and phase are both odd functions. Thus for real-valued signals the Fourier transform need only be specified for positive frequencies because of the conjugate symmetry. Whether or not a sequence is real, specification of the Fourier transform over a frequency range of 2 7r specifies it entirely. For a real-valued sequence, specification over the frequency range from, for example, 0 to a is sufficient because of conjugate symmetry. The time-shifting property together with the linearity property plays a key role in using the Fourier transform to determine the response of systems characterized by linear constant-coefficient difference equations. As with continuous time, the convolution property and the modulation property are of particular significance. As a consequence of the convolution property, which states that the Fourier transform of the convolution of two sequences is the product of their Fourier transforms, a linear, time-it variant system is represented in the frequency domain by its frequency response. This representation corresponds to the scale factors applied at each frequency to the Fourier transform of the input. Once again, the convolution property can be thought of as a direct consequence of the fact that the Fourier transform decomposes a signal into a linear combination of complex exponentials each of which is an eigenfunction of a linear, time-invariant system. The frequency response then corresponds to the eigenvalues. The concept of filtering for discrete-time signals is a direct consequence of the convolution property. The modulation property in discrete time is also very similar to that in continuous time, the principal analytical difference being that in discrete time the Fourier transform of a product of sequences is the periodic convolution 11-1 Signals and Systems 11-2 rather than the aperiodic convolution of the individual Fourier transforms. The modulation property for discrete-time signals and systems is also very useful in the context of communications. While many communications systems have historically been continuous-time systems, an increasing number of communications systems rely on discrete-time modulation techniques. Often in digital transmission systems, for example, it is necessary to convert from one type of modulation system to another, a process referred to as transmodulation, and efficient implementation relies on the modulation property for discrete-time signals. As we discuss in this lecture, another important application of the modulation property is the use of modulation to effect a highpass filter with a lowpass filter or vice versa. This lecture concludes our discussion of the basic mathematics of Fourier series and Fourier transforms; we turn our attention in the next several lectures to the concepts of filtering, modulation, and sampling. We conclude this lecture with a summary of the basic Fourier representations that we have developed in the past five lectures, including identifying the various dualities. The continuous-time Fourier series is the representation of a periodic continuous function by an aperiodic discrete sequence, specifically the sequence of Fourier series coefficients. Thus, for continuous-time periodic signals there is an inherent asymmetry and lack of duality between the two domains. In contrast, the continuous-time Fourier transform has a strong duality between the time and frequency domains and in fact the Fourier transform of the Fourier transform gets us back to the original signal, time-reversed. In discrete time the situation is the opposite. The Fourier series represents a periodic time-domain sequence by a periodic sequence of Fourier series coefficients. On the other hand, the discrete-time Fourier transform is a representation of a discrete-time aperiodic sequence by a continuous periodic function, its Fourier transform. Also, as we discuss, a strong duality exists between the continuous-time Fourier series and the discrete-time Fourier transform. Suggested Reading Section 5.5, Properties of the Discrete-Time Fourier Transform, pages 321-327 Section 5.6, The Convolution Property, pages 327-333 Section 5.7, The Modulation Property, pages 333-335 Section 5.8, Tables of Fourier Properties and of Basic Fourier Transform and Fourier Series Pairs, pages 335-336 Section 5.9, Duality, pages 336-343 Section 5.10, The Polar Representation of Discrete-Time Fourier Transforms, pages 343-345 Section 5.11.1, Calculations of Frequency and Impulse Responses for LTI Systems Characterized by Difference Equations, pages 345-347 Discrete-Time Fourier Transform 11-3 DISCRETE-TIME FOURIER TRANSFORM x [n] - 1fX(2) 27r 2 ejn dU synthesis +00 x[n] X(92) analysis e- jn TRANSPARENCY 11.1 The discrete-time Fourier transform. [As corrected here, x[n], not x(t), has Fourier transform X(fl).J n=-w0 x[n] x(-i2) Periodicity and symmetry properties of the discrete-time Fourier transform. +27r m) Symmetry: x[n] real RejX(w)t even IX(SiZ)I Imlx() 4 X(W) odd X*(92) Signals and Systems 11-4 = x[n] O 'I rn3 T~~QoIZ~OkCWL) r1V - 8t 4070()& 00 -16) I~t e %t )e0 I esASo2M~r