Скачать или смотреть Communication Block Diagram II Transmitter II Receiver II Channel Modeling II PMF, CDF, PDFII ADC

------------------- Credit : Muhammad Tilal -------------------------
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Classification of Signals
• Signals can e classified on the
basis of different properties.
Most common of these are
– Analog & Digital Signals
– Periodic & Non-Periodic Signals
– Energy and Power Signals
– Deterministic and Random Signals
• Deterministic signals are ones with no uncertainty in their value with respect to time. They have explicit mathematical expression.
• Examples of deterministic signals
are
– Sinusoids
– Square/Rectangular/Triangular signals etc
• Random signals are the signals in which there is some degree of uncertainty before they actually occur.
• How to express a random signal mathematically?
Random Signals
• Random signals are represented
in terms of “Probabilistic Models”.
• Real life examples of random signals?
• Deterministic signals are ones with no uncertainty in their value with respect to time. They have explicit mathematical expression.
• Random signals can be discrete as well as Continuous.
• Some discrete random variables are
– Bernoulli Random Variable.
– Binomial Random Variable.
– Poisson Random Variable.
– Geometric Random Variable.

Tools & Parameters of Random
• Probability Mass Function (PMF)
• Cumulative Density Function (CDF)
• Probability Density Function (PDF)
• Work out the use and scope of each of these parameters.Variable

Channel Models

• Channel model is the interpretation of the physical/statistical processes that a signal experiences when transmitted into certain transmission channel.
• The core objective behind channel modeling is to incorporate all the signal modifications done by the transmission channel.
• The physical and statistical approaches are combined to include all the channel effects.
• The channel models can be analog as well as digital.

Channel Models
Analog
• Noise Channel Model.
• Interference Channel Model.
• Distortion Channel Model.
Digital

• Binary Symmetric Channel (BSC)
• Binary Bursty Bit Error Channel.
• Binary Erasure Channel (BEC).
• Packet Erasure Channel (PEC).
• Arbitrary Varying Channel (AVC).
• Frequency Response Model.
• Group Delay Model.
• Radio Frequency Propagation Model.
Noise Channel Model
• Noise is the random fluctuations of the electrical signal which causes the original signal to be corrupted.
• Noise can be categorize into
– Thermal Noise.
– Shot Noise.
– Flicker Noise.
– Intermediation Noise.
– Cross Talk.
– Impulse Noise.
– Interference.


• Most important of these noise types is ‘Thermal Noise’ and it is present in all the communication systems regardless of their type and parameters.
• Thermal noise is generated by the random thermal motion of charge carriers.
• Thermal noise is modeled as Additive White Gaussian Noise.
Additive White Gaussian Noise
• Thermal noise is
– Additive.
– White.
– Gaussian.
• ‘Additive’ refers to the fact that the noise signal values are added to the transmitted signal.
• ‘White’ is a property that implies the uniformity of the noise power.
• ‘Gaussian’ implies that the random amplitude values of thermal noise in time domain follow the Gaussian probability distribution function.
• Gaussian probability distribution is also known as ‘Normal Distribution’.
• AWGN Channel is good for satellite and deep space communications but it is not a good channel for RF communications. Why?
[1] Bernard Sklar, Digital Communications- Fundamentals and Applications, 2
nd Edition.
[2] Scott Miller & Donald Childers, Probability & Random Processes: With Applications to
Signal Processing and Communications, 2

nd Edition, Pearson Education Limited, England,

ISBN-13: 978-0123869814, ISBN-10: 0123869811.
[3] S.Miller & D. Childers, Probability and Random Processes: With Applications to Signal
Processing and Communications, 2

nd Edition, Elsevier Inc, ISBN: 13-978-0121726515.
[4] Charan Langton, Inter-symbol Interference (ISI) and Root Raised Cosine Filtering (RRC),
2002.
URL to full Text:
http://complextoreal.com/wp-content/u...

[5] Andrea Goldsmith, Wireless Communications, 2005, Cambridge University Press, ISBN-
13: 978-0521837163.