08-04-2014, 04:25 PM
Digital Communications and Signal Processing – with Matlab Examples
Introduction
Digital communications and signal processing refers to the field of study concerned with the trans-
mission and processing of digital data. This is in contrast with analog communications. While
analog communications use a continuously varying signal, a digital transmission can be broken
down into discrete messages. Transmitting data in discrete messages allows for greater signal pro-
cessing capability. The ability to process a communications signal means that errors caused by
random processes can be detected and corrected. Digital signals can also be sampled instead of
continuously monitored and multiple signals can be multiplexed together to form one signal.
Because of all these advantages, and because recent advances in wideband communication
channels and solid-state electronics have allowed scientists to fully realize these advantages, dig-
ital communications has grown quickly. Digital communications is quickly edging out analog
communication because of the vast demand to transmit computer data and the ability of digital
communications to do so.
Examples
Telecommunications traffic is characterised by great diversity. A non-exclusive list is the follow-
ing:
1. Audio signals. An audio signal is an example of an analogue signal. It occupies a frequency
range from about 200 Hz to about 15KHz. Speech signals occupy a smaller range of fre-
quencies, and telephone speech typically occupies the range 300 Hz to 3300 Hz. The range
of frequencies occupied by the signal is called its bandwidth (see Fig. 2).
2. Television. A television signal is an analogue signal created by linearly scanning a two
dimensional image. Typically the signal occupies a bandwidth of about 6 MHz.
The conversion of analogue and digital signals
In order to send analogue signals over a digital communication system, or process them on a digital
computer, we need to convert analogue signals to digital ones. This process is performed by an
analogue-to-digital converter (ADC). The analogue signal is sampled (i.e. measured at regularly
spaced instant) (Fig 3) and then quantised (Fig. 3, bottom panel) i.e. converted to discrete numeric
values. The converse operation to the ADC is performed by a digital-to-analogue converter (DAC).
The ADC process is governed by an important law. The Nyquist-Shannon Theorem (which
will be discussed in Chapter 3) states that an analogue signal of bandwidth B can be completely
recreated from its sampled form provided it is sampled at a rate equal to at least twice its bandwidth.
The relationship between information, bandwidth and noise
The most important question associated with a communication channel is the maximum rate at
which it can transfer information. Analogue signals passing through physical channels may not
achieve arbitrarily fast changes. The rate at which a signal may change is determined by the band-
width. Namely, a signal of bandwidth B may change at a maximum rate of 2B, so the maximum
information rate is 2B. If changes of differing magnitude are each associated with a separate bit,
the information rate may be increased.
Digital demodulation
From the discussion above it might appear that QPSK offers advantages over both ASK, FSK and
PSK. However, the demodulation of these signals requires various degrees of difficulty and hence
expense. The method of demodulation is an important factor in determining the selection of a
modulation scheme. There are two types of demodulation which are distinguished by the need
to provide knowledge of the phase of the carrier. Demodulation schemes requiring the carrier
phase are termed coherent. Those that do not need knowledge of the carrier phase are termed
incoherent. Incoherent demodulation can be applied to ASK and wide-band FSK. It describes
demodulation schemes that are sensitive only to the power in the signal. With ASK, the power is
either present, or it is not. With wide-band FSK, the power is either present at one frequency, or the
other. Incoherent modulation is inexpensive but has poorer performance. Coherent demodulation
requires more complex circuity, but has better performance.