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Probabilistic Approaches to System Optimization

The works of Wiener and Shannon were the beginning of modern statistical communication theory. Both these investigators applied probabilistic methods to the problem of extracting information-bearing signals from noisy backgrounds, but they worked from different standpoints.

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Systems-Analysis Techniques

Having identified and discussed the main subsystems in a communication system and certain characteristics of transmission media, let us now look at the techniques at our disposal for systems analysis and design.

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Channel Characteristics

Noise in a communication system can be classified into two broad categories, depending on its source. Noise generated by components within a communication system, such as resistors and solid-state active devices is referred to as internal noise. The second category, external noise, results from sources outside a communication system, including atmospheric, man-made, and extraterrestrial sources.

Atmospheric noise results primarily from spurious radio waves generated by the natural electrical discharges within the atmosphere associated with thunderstorms. It is commonly referred to as static or spherics. Below about 100 MHz, the field strength of such radio waves is inversely proportional to frequency. Atmospheric noise is characterized in the time domain by large-amplitude, short-duration bursts and is one of the prime examples of noise referred to as impulsive. Because of this inverse dependence on frequency, atmospheric noise affects commercial AM broadcast radio, which occupies the frequency range from 540 kHz to 1.6 MHz, more than it affects television and FM radio, which operate in frequency bands above 50 MHz.

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Introduction to Communication Systems

A characteristic of electrical communication systems is the presence of uncertainty. This uncertainty is due in part to the inevitable presence in any system of unwanted signal perturbation, broadly referred to as noise, and in part to the unpredictable nature of information itself. Systems analysis in the presence of such uncertainty requires the use of probabilistic techniques.

Noise has been an ever-present problem since the early days of electrical communication, but it was not until the 1940s that probabilistic systems analysis procedures were used to analyze and optimize communication systems operating in its presence. It is also somewhat surprising that the unpredictable nature of information was not widely recognized until the publication of Claude Shannon's mathematical theory of communications in the late 1940s. This work was the beginning of the science of information theory

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Optical Regenerators

An important application of optical signal processing is for regenerating optical signals degraded during transmission through fibers and amplifiers. An ideal optical regenerator transforms the degraded bitstream into its original form by performing three functions: reamplification, reshaping, and retiming. Such devices are referred to as 3R regenerators to emphasize that they perform all three functions. With this terminology, optical amplifiers can be classified as 1R regenerators because they only reamplify the bitstream. Devices that perform the first two functions are called 2R regenerators. Since 2R and 3R regenerators have to work at time scales shorter than the bit slot in order to carry out pulse reshaping and retiming, they must operate at time scales of 10 ps or less, depending on the bit rate of the optical signal. As nonlinear effects in optical fibers respond at femtosecond time scales, highly nonlinear fibers are often employed for such devices. However, the use of SOAs is also being pursued in view of their low-power requirements.

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