Fundamentals of Communication Systems 2nd Edition Question 2.1

Digital Communication System

In digital communications systems a modulator is a device which maps bits of information to elementary signals, symbols, which can transmitted in the channel.

From: Polymer Optical Fibres , 2017

Introducing Telecommunications

Carl Nassar , in Telecommunications Demystified, 2001

1.3.3 And Digital Became the Favorite

Digital communication systems are becoming, and in many ways have already become, the communication system of choice among us telecommunication folks. Certainly, one of the reasons for this is the rapid availability and low cost of digital components. But this reason is far from the full story. To explain the full benefits of a digital communication system, we'll use Figures 1.7 and 1.8 to help.

Figure 1.7. (a) Transmitted analog signal; (b) Received analog signal

Figure 1.8. (a) Transmitted digital signal; (b) Received digital signal

Let's first consider an analog communication system, using Figure 1.7. Let's pretend the transmitter sends out the analog signal of Figure 1.7(a) from point A to point B. This signal travels across the channel, which adds some noise (an unwanted signal). The signal that arrives at the receiver now looks like Figure 1.7(b). Let's now consider a digital communication system with the help of Figure 1.8. Let's imagine that the transmitter sends out the signal of Figure 1.8(a). This signal travels across the channel, which adds a noise. The signal that arrives at the receiver is found in Figure 1.8 (b).

Here's the key idea. In the digital communication system, even after noise is added, a 1 (sent as +5 V) still looks like a 1 (+5 V), and a 0 (−5 V) still looks like a 0 (−5 V). So, the receiver can determine that the information transmitted was a 1 0 1. Since it can decide this, it's as if the channel added no noise. In the analog communication system, the receiver is stuck with the noisy signal and there is no way it can recover exactly what was sent. (If you can think of a way, please do let me know.) So, in a digital communication system, the effects of channel noise can be much, much less than in an analog communication system.

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Precision, matched, baseband filter ICs outperform discrete implementations

Philip Karantzalis , in Analog Circuit Design, Volume Three, 2015

Introduction

In digital communication systems, baseband signals must be band-limited in the transmitter or the receiver. Although the bulk of baseband signal shaping and analysis is accomplished using digital signal processing (DSP), analog filtering is used in a number of places along the signal chain. For instance analog filters reduce the imaging of a digital-to-analog converter (DAC), filter out the high frequency noise of an RF demodulator or reduce the aliasing inputs of an analog-to-digital converter (ADC).

Typically, 3G communication systems (CDMA, GSM, UMTS or WiMax) feature a baseband channel bandwidth of 1.25MHz to over 20MHz. In this frequency range, discrete analog filters—those constructed with high speed op amps, resistors and capacitors—are sensitive to PCB layout parasitics, component tolerances and mismatches. The pitfalls of using discrete components can be avoided by using integrated, pin-configurable, precision analog filter ICs, such as the LTC6601-1/-2 and the LTC6605-7/-10/-14. The LTC6601-x is a single 2nd order lowpass filter and the LTC6605-x is a dual, matched filter.

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Random Fractal Signals

Jonathan M. Blackledget , in Digital Signal Processing (Second Edition), 2006

17.9.1 Secure Digital Communications

A digital communications systems is one that is based on transmitting and receiving bit streams. The basic processes involved are as follows: (i) a digital signal is obtained from sampling an analogue signal derived from some speech and/or video system; (ii) this signal (floating point stream) is converted into a binary signal consisting of 0   s and 1   s (bit stream); (iii) the bit stream is then modulated and transmitted; (iv) at reception, the transmitted signal is demodulated to recover the transmitted bit stream; (v) the (floating point) digital signal is reconstructed. Digital to analogue conversion may then be required depending on the type of technology being used.

In the case of sensitive information, an additional step is required between stages (ii) and (iii) above where the bit stream is coded according to some classified algorithm. Appropriate decoding is then introduced between stages (iv) and (v) with suitable pre-processing to reduce the effects of transmission noise for example which introduces bit errors. The bit stream coding algorithm is typically based on a pseudo random number generator or nonlinear maps in chaotic regions of their phase spaces (chaotic number generation). The modulation technique is typically either Frequency Modulation or Phase Modulation. Frequency modulation involves assigning a specific frequency to each 0 in the bit stream and another higher (or lower) frequency to each 1 in the stream. The difference between the two frequencies is minimized to provide space for other channels within the available bandwidth. Phase modulation involves assigning a phase value (0, π/2, π, 3π/2) to one of four possible combinations that occur in a bit stream (i.e. 00, 11, 01 or 10).

Scrambling methods can be introduced before binarization. A conventional approach to this is to distort the digital signal by adding random numbers to the out-of-band components of its spectrum. The original signal is then recovered by lowpass filtering. This approach requires an enhanced bandwidth but is effective in the sense that the signal can be recovered from data with a relatively low signai-to-noise ratio. 'Spread spectrum' or 'frequency hopping' is used to spread the transmitted (e.g. frequency modulated) information over many different frequencies. Although spread spectrum communications use more bandwidth than necessary, by doing so, each communications system avoids interference with another because the transmissions are at such minimal power, with only spurts of data at any one frequency. The emitted signals are so weak that they are almost imperceptible above background noise. This feature results in an added benefit of spread spectrum which is that eaves-dropping on a transmission is very difficult and in general, only the intended receiver may ever known that a transmission is taking place, the frequency hopping sequence being known only to the intended party. Direct sequencing, in which the transmitted information is mixed with a coded signal, is based on transmitting each bit of data at several different frequencies simultaneously, with both the transmitter and receiver synchronized to the same coded sequence. More sophisticated spread spectrum techniques include hybrid ones that leverage the best features of frequency hopping and direct sequencing as well as other ways to code data. These methods are particularly resistant to jamming, noise and multipath anomalies, a frequency dependent effect in which the signal is reflected from objects in urban and/or rural environments and from different atmospheric layers, introducing delays in the transmission that can confuse any unauthorized reception of the transmission.

The purpose of Fractal Modulation is to try and make a bit stream 'look like' transmission noise (assumed to be fractal). The technique considered here focuses on the design of algorithms which encode a bit stream in terms of two fractal dimensions that can be combined to produce a fractal signal characteristic of transmission noise. Ultimately, fractal modulation can be considered to be an alternative to frequency modulation although requiring a significantly greater bandwidth for its operation. However, fractal modulation could relatively easily be used as an additional preprocessing security measure before transmission. The fractal modulated signal would then be binarized and the new bit stream fed into a conventional frequency modulated digital communications system albeit with a considerably reduced information throughput for a given bit rate. The problem is as follows: given an arbitrary binary code, convert it into a non-stationary fractal signal by modulating the fractal dimension in such a way that the original binary code can be recovered in the presence of additive noise with minimal bit errors.

In terms of the theory discussed earlier, we consider a model of the type

$\left[\frac{{\partial }^2}{\partial x^2}-{\tau }^{q\left(t\right)}\frac{{\partial }^{q\left(t\right)}}{\partial t^{q\left(t\right)}}\right]u\left(x,t\right)=-\delta \left(x\right)n\left(t\right),q>0,x\to 0$

where q(t) is assigned two states, namely q ₁ and q ₂ (which correspond to 0 and 1 in a bit stream respectively) for a fixed period of time. The forward problem (fractal modulation) is then defined as: given q(t) compute u(t) ≡ u(0, t). The inverse problem (fractal demodulation) is defined as: given u(t) compute q(t).

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Synchronization

Ali Grami , in Introduction to Digital Communications, 2016

Abstract

In every digital communication system, some level of synchronization is required, without which a reliable transmission of information is not possible. Of various synchronization levels, the focus of this chapter is on symbol synchronization and carrier recovery. The role of the former is to provide the receiver with an accurate estimate of the beginning and ending times of the symbols and the latter aims to replicate a sinusoidal carrier at the receiver whose phase is the same as that sent by the transmitter. Due to the fact that carrier frequency is much higher than the symbol rate, these two types of synchronization are done with different circuitry.

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Introduction

Vikram Arkalgud Chandrasetty , Syed Mahfuzul Aziz , in Resource Efficient LDPC Decoders, 2018

1.1 Error Correction in Digital Communication System

In a Digital Communication System, the messages generated by the source which are generally in analog form are converted to digital format and then transmitted. At the receiver end, the received digital data is converted back to analog form, which is an approximation of the original message [1]. A simple block diagram of a digital communication system is shown in Fig. 1.1.

Figure 1.1. Block diagram of a simple Digital Communication System.

A digital communication system consists of six basic blocks. The functional blocks at the transmitter are responsible for processing the input message, encoding, modulating, and transmitting over the communication channel. The functional blocks at the receiver perform the reverse process to retrieve the original message [2].

The aim of a digital communication system is to transmit the message efficiently over the communication channel by incorporating various data compressions (e.g., DCT, JPEG, MPEG) [3], encoding and modulation techniques, in order to reproduce the message in the receiver with the least errors. The information input, which is generally in analog form, is digitized into a binary sequence, also known as an information sequence. The source encoder is responsible for compressing the input information sequence to represent it with less redundancy. The compressed data is passed to the channel encoder. The channel encoder introduces some redundancy in the binary information sequence that can be used by the channel decoder at the receiver to overcome the effects of noise and interference encountered by the signal while in transit through the communication channel [4]. Hence, the redundancy added in the information message helps in increasing the reliability of the data received and also improves the fidelity of the received signal. Thus, the channel encoder aids the receiver in decoding the desired information sequence. Some of the popular channel encoders are Low Density Parity Check (LDPC) codes, Turbo codes, Convolution codes, and Reed-Solomon codes. The channel encoded data is passed to the channel modulator, which serves as the interface to the communication channel. The encoded sequence is modulated using suitable digital modulation techniques, i.e., Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK) and transmitted over the communication channel [1].

The communication channel is the physical medium used to transfer signals carrying the encoded information from the transmitter to the receiver. A range of noise and interferences can affect the information signal during transmission depending on the type of the channel medium, e.g., thermal noise, atmospheric noise, man-made noise. The communication channel can be air, wire, or optical cable [2].

At the receiver, the received modulated signal, probably incorporating some noise introduced by the channel, is demodulated by channel demodulator to obtain a sequence of channel encoded data in digital format. The channel decoder processes the received encoded sequence and decodes the message bits with the help of the redundant data inserted by the channel encoder in the transmitter. Finally, the source decoder reconstructs the original information message. The reconstructed information message at the receiver is probably an approximation of the original message because of errors involved in channel decoding and the distortion introduced by the source encoder and decoder [4].

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Coding

Dr M D Macleod MA PhD MIEEE , in Telecommunications Engineer's Reference Book, 1993

Publisher Summary

In a digital communication system, information is sent as a sequence of digits that are first converted to an analog form by modulation at the transmitter and then converted back into digits by de-modulation at the receiver. An ideal communication channel would transmit information without any form of corruption or distortion. Error control coding is the controlled addition of redundancy to the transmitted digit stream in such a way that errors introduced in the channel can be detected, and in certain circumstances corrected, in the receiver. It is, therefore, a form of channel coding, so called because it compensates for imperfections in the channel; the other form of channel coding is transmission (or line coding), which has different objectives such as spectrum shaping of the transmitted signal. There are two main types of error-correcting code: block coding and convolutional coding. In block coding, the input is divided into blocks of k digits. The coder then produces a block of n digits for transmission and the code is described as an (n, k) code. In convolutional coding, the coder input and output are continuous streams of digits. The coder outputs n output digits for every k digits input, and the code is described as a rate k/n code. A more sophisticated decoder may use the parity digits for error detection and a full decoder for error correction. Unsystematic codes also exist but are less commonly used.

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Submarine fibers

Scott R. Bickham , ... Snigdharaj Mishra , in Undersea Fiber Communication Systems (Second Edition), 2016

11.3.3 Dispersion

Chromatic dispersion in digital communication systems induces distortion because the various spectral components of the pulse propagate through the fiber at different speeds, thereby arriving at the receiver with phase variations. The phase variations accumulate as the signal propagates, leading to temporal broadening of the pulse. In the context of waveguide theory, dispersion is defined as the change in the specific group delay of the signal with wavelength. It is related to the propagation constant by:

(11.5) $D=\frac{d}{d\lambda }\left(\frac{1}{c}\frac{d\beta }{dk}\right),$

which can be directly calculated from the solution of the scalar wave equation (Eq. 11.3) for a given refractive index profile. Chromatic dispersion slope is then obtained by differentiating Eq. 11.5 with respect to wavelength:

(11.6) $S=\frac{dD}{d\lambda }=\frac{d^2}{d{\lambda }^2}\left(\frac{1}{c}\frac{d\beta }{dk}\right).$

In the absence of polarization mode dispersion (PMD) and intermodal dispersion, the total dispersion is the sum of material and waveguide dispersion. The material dispersion is a consequence of the wavelength dependence of the silica refractive index and is well-described over a broad wavelength range by a three-term Sellmeier equation:

(11.7) $n^2(\lambda )-1=\sum _{i=1}^3\frac{a_i{\lambda }^2}{{\lambda }^2-b_i},$

where {a _i} and {b _i} are material parameters. A table of the material parameters for various glass dopants in silica is given by [13].

The dispersion curves of several fibers are shown in Figure 11.3. The wavelength dependence of the standard (unshifted) single-mode fiber is very close to pure silica, which has a zero dispersion wavelength near 1270   nm and dispersion and dispersion slope values of about 17   ps/nm/km and 0.06   ps/nm²/km, respectively, at 1550   nm. The zero dispersion wavelengths of the NZ-DSF fibers are typically shifted to approximately 1500 and 1590   nm.

Figure 11.3. Dispersion curves of non-dispersion-shifted and non-zero-dispersion-shifted fibers.

Historically, NZ-DSFs evolved from dispersion-shifted (DS) fibers that had zero-dispersion wavelengths near 1550   nm [14]. Although these DS fibers accomplished the goal of making the wavelengths of minimum dispersion and attenuation coincident, nonlinearities in the fiber were found to be a limiting factor for DWDM transmission. Mitigation of nonlinear processes such as four-wave mixing (FWM) and cross-phase modulation (XPM) requires operation away from the zero-dispersion wavelength, which led to the generation of two families of NZ-DSFs with zero dispersion wavelengths either above or below the 1550   nm window [15]. The former are primarily used in submarine systems, which are designed to operate in the negative dispersion regime in order to minimize modulational instability. On the other hand, historical terrestrial systems utilized either standard single-mode fiber or positive dispersion NZ-DSFs in conjunction with dispersion compensation modules based on dispersion compensation fibers (DCF) with negative dispersion.

Most of the undersea networks deployed prior to the late 1990s primarily used standard single-mode fiber, which was adequate for line rates of 2.5   Gb/s or less. However, as the bandwidth demand grew, many system houses converted to NZ-DSF, which has low negative dispersion in the 1550   nm window and requires less frequent dispersion compensation. These fibers typically have dispersion values at 1550   nm in the range of about −1 to −4   ps/nm/km and dispersion slopes between 0.04 and 0.12   ps/nm²/km. As an example of a system utilizing these fibers, consider a block of ten 50   km optical fiber spans comprised of NZ-DSF with a dispersion of −3.4   ps/nm/km. These concatenated fibers have an accumulated dispersion of −1700   ps/nm that can be perfectly balanced (or compensated) by two 50   km spans of standard single-mode fiber with a dispersion of +17   ps/nm/km. This block of ten NZ-SDF spans plus the two spans of single-mode fibers has a total length of 600   km and would be repeated as necessary with appropriate amplification to form a repeatered submarine system. This dispersion management scenario is sometimes referred to as a dispersion map.

One issue with the dispersion map based on NZ-DSF and a positive dispersion fiber (PDF) is that the slopes of both fibers are positive, making dispersion compensation at other wavelengths impossible unless the WDM signals channels are demultiplexed and compensated individually through different lengths of DCF. This can be done at the terminals, but is impractical to do inline in a repeatered submarine system due to space and power constraints in the repeaters. To address this dispersion management challenge, some fiber manufacturers developed dispersion compensating fibers that have negative dispersion as well as negative dispersion slope. These dispersion characteristics are achieved through the addition of an annular segment with a depressed refractive index on the periphery of the central core of the fiber, similar to the example shown schematically in Figure 11.1. The depressed moat tends to increase the bend sensitivity, but this can be mitigated through the addition of an appropriate annular ring with a raised refractive index on the outside of the moat [16]. The moat and ring parameters can be optimized to provide dispersion characteristics that mirror the dispersion curve of the PDF.

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Fundamental Aspects of Digital Communications

Ali Grami , in Introduction to Digital Communications, 2016

2.1.2 Disadvantages of Digital

Signal-processing intensive: Digital communication systems require a very high degree of signal processing, where every one of the three major functions of source coding, channel coding, and modulation in the transceiver—each requiring an array of sub-functions (especially in the receiver)—warrants high computational load and thus complexity. Due to major advances in digital signal processing (DSP) technologies in the past two decades, this is no longer a major disadvantage.

Additional bandwidth: Digital communication systems generally require more bandwidth than analog systems, unless digital signal compression (source coding) and M-ary (vis-à-vis binary) signaling techniques are heavily employed. Due to major advances in compression techniques and bandwidth-efficient modulation schemes, the bit rate requirement and thus the corresponding bandwidth requirement can be considerably reduced by a couple of orders of magnitude. As such, additional bandwidth is no longer a critical issue.

Synchronization: Digital communication systems always require a significant share of resources allocated to synchronization, including carrier phase and frequency recovery, timing (bit or symbol) recovery, and frame and network synchronization. This inherent drawback of digital transmission cannot be circumvented. However, synchronization in a digital communication system can be accomplished to the extent required, but at the expense of a high degree of complexity.

Non-graceful performance degradation: Digital communication systems yield non-graceful performance degradation when the SNR drops below a certain threshold. A modest reduction in SNR can give rise to a considerable increase in bit error rate (BER), thus resulting in a significant degradation in performance.

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Information Theory and Coding

Richard E. Blahut , in Reference Data for Engineers (Ninth Edition), 2002

Bit Energy and Bit Error Rate

The performance of a digital communication system is measured by the probability of bit error, also called the bit error rate (BER). On an additive Gaussian noise channel, the bit error rate can always be reduced by increasing transmitted power, but it is by the performance at low transmitted power that one judges the quality of a digital communication system. The better of two systems, otherwise the same, is the one that can achieve a desired bit error rate with the lower transmitted power.

Given a message, s(t), of duration T containing K information bits, the bit energy, E_b , is given by

$E_b=E_m/K$

where

$E_m={\int }_0^{T}s{\left(t\right)}^{2}dt$

is the message energy.

Bit energy E_b is calculated from the message energy and the number of information bits at the input to the encoder/modulator. At the input to the channel, one may find a message structure in which he perceives a larger number of bits. The extra symbols may be parity symbols for error control, or symbols for frame synchronization or channel protocol. These symbols do not represent transmitted information, and their energy must be amortized over information bits. Only information bits are used in calculating E_b .

For an infinite-length message of rate R information bits/second, E_b is defined by

$E_b=S/R$

where S is the message average power.

In addition to the message energy, the receiver also sees a white noise signal of one-sided spectral density N_o watts/hertz. Only the ratio E_m /N_o or E_b /N_o affects the bit error rate because the reception of the signal cannot be affected if both the signal and the noise are doubled. Signaling schemes are compared by comparing their respective graphs of BER versus required E_b /N_o .

It is possible to make precise statements about values of E_b /N_o for which good waveforms exist; these are a consequence of the channel capacity formula for the ideal rectangular bandpass channel in additive Gaussian noise. Let the signal power be S = E_bR and the noise power be N = N_oW. Then

$C/W={\log }_2\left(1+R{E}_b/N_{o}W\right)$

Define the spectral bit rate, r (measured in bits per second per hertz), by

$r=R/W$

The spectral bit rate, r, and E_b /N_o are the two most important figures of merit of a digital communication system.

Since the rate, R, is less than but can be made arbitrarily close to the capacity, C, the capacity formula becomes

$E_b/N_o>\left(2\prime -1\right)/r$

but E_b /N_o can be arbitrarily close to the bound by designing a sufficiently sophisticated digital communication system. This inequality, shown in Fig. 20, tells us that increasing the bit rate per unit bandwidth increases the required energy per bit. This is the basis of the energy/bandwidth trade of digital communication theory where increasing bandwidth at a fixed information rate can reduce power requirements.

Fig. 20. Capacity of baseband Gaussian noise channel.

Every communication system can be described by a point lying below the curve of Fig. 20. Any communication system that attempts to operate above the curve will lose enough data through errors so that its actual data rate will lie below the curve. By the fundamental theorem of information theory, for any point below the curve one can design a communication system that has as small a bit error rate as one desires. The history of digital communications can be described in part as a series of attempts to move ever closer to this limiting curve with systems that have very low bit error rate. Such systems employ both modem techniques and error-control techniques.

If bandwidth W is a plentiful resource but energy is scarce, then one should let W go to infinity, or r to zero. Then we have

$E_b/N_o\ge {\log }_{e}2=0.69$

This is a fundamental limit. Ratio E_b /N_o is never less than −1.6 dB, and by a sufficiently expensive system one can communicate with any E_b /N_o larger than −1.6 dB.

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Intelligent Control System

Swapan Basu , Ajay Kumar Debnath , in Power Plant Instrumentation and Control Handbook (Second Edition), 2019

1.1.5.1 Fieldbus Requirements

The fieldbus is a digital communication system for various field devices such as sensors, actuators, and field control systems. The fieldbus cable SHOULD NOT BE LOOKED AT AS "JUST WIRE." In an integrated system, it can be compared with one of the main arteries in the human body. There are a few advantages of a fieldbus that shall include but are not limited to the following:

•

Huge reductions in cable, wiring, cable tray, marshalling cabinet, and junction boxes, along with a reduction in installation labor (hence, cost and complications).

•

The fieldbus is reliable, cost effective, and deterministic. It offers greater flexibility in system design and layout for easier use, expansion, and modification.

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More extensive parameters and data exchange (calibration, service history configuration data, diagnostics, test information, device documentation, etc.) to and from the field devices at a much faster rate to facilitate speedier and easier commissioning, maintenance, and servicing.

•

Openness and interface capability make it possible to integrate multiple products from different vendors in a system.

The IEC 61158-2 standard has been developed for the interconnection of various automation system components. Some of the salient features of IEC 1158-2 related to transmission technology are presented in Table 7.7.

Table 7.7. IEC 1158-2 Features

Features	Requirements	Features	Requirements
1. Data transmission	Digital Manchester Bit synchronized	5. Data security	Preamble, error free start and end delimiter
2. Speed	31.25 kbps voltage	6. Cable	Two-wire shielded twisted pair
3. Explosion protection	IS EExiaand ib EEx d/m/p/q	7. Number of stations	32/segment (Max. could be 126 address available)
4. Topology	Line/tree or combination	8. Repeaters	4

The following are the basic transmission features noted from this standard combining with the field intrinsically safe concept (FISCO):

•

Various topologies such as linear, tree, and star topology are allowed with each segment with one power supply. No power feeding to a bus when a node is sending.

•

Field devices are passive current sinks. The passive line terminates at both ends of the main bus line, and in a steady state, all field devices consume constant current.

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Fundamentals of Communication Systems 2nd Edition Question 2.1

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