Connexions

You are here: Home » Content » Channel Models
Content Actions
Lenses

What is a lens?

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...
Affiliated with (?)
This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.

  • This module is included inLens: Rice University Disability Support Services's Lens
    By: Rice University Disability Support ServicesAs a part of collection:"Fundamentals of Electrical Engineering I"

    Comments:

    "Electrical Engineering Digital Processing Systems in Braille."

    Click the "Rice DSS - Braille" link to see all content affiliated with them.

    Rice DSS - Braille

  • This module is included inLens: Rice University OpenCourseWare
    By: OpenCourseWare ConsortiumAs a part of collection:"Fundamentals of Electrical Engineering I"

    Click the "Rice University OCW" link to see all content affiliated with them.

    Rice University OCW
Also in these lenses

  • This module is included inLens: Connexions Books Available for Print on Demand
    By: ConnexionsAs a part of collection:"Fundamentals of Electrical Engineering I"

    Comments:

    "This book was assembled for print in July 07. A braille version of this book is being produced also."

    Click the "Printable Books" link to see all content selected in this lens.

    Printable Books
Tags

(?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

Channel Models

Module by: Don Johnson

Summary: Both wireline and wireless channels share characteristics, allowing us to use a common model for how the channel affects transmitted signals.

Both wireline and wireless channels share characteristics, allowing us to use a common model for how the channel affects transmitted signals.
  • The transmitted signal is usually not filtered by the channel.
  • The signal can be attenuated.
  • The signal propagates through the channel at a speed equal to or less than the speed of light, which means that the channel delays the transmission.
  • The channel may introduce additive interference and/or noise.
Letting αα represent the attenuation introduced by the channel, the receiver's input signal is related to the transmitted one by
rt=αxt-τ+it+nt rt α x t τ it nt (1)
This expression corresponds to the system model for the channel shown in Figure 1. In this book, we shall assume that the noise is white.
sys16.png
Figure 1: The channel component of the fundamental model of communication has the depicted form. The attenuation is due to propagation loss. Adding the interference and noise is justified by the linearity property of Maxwell's equations.
Problem 1
Is this model for the channel linear?
[ Click for Solution 1 ]
Solution 1
The additive-noise channel is not linear because it does not have the zero-input-zero-output property (even though we might transmit nothing, the receiver's input consists of noise).
[ Hide Solution 1 ]
As expected, the signal that emerges from the channel is corrupted, but does contain the transmitted signal. Communication system design begins with detailing the channel model, then developing the transmitter and receiver that best compensate for the channel's corrupting behavior. We characterize the channel's quality by the signal-to-interference ratio (SIR) and the signal-to-noise ratio (SNR). The ratios are computed according to the relative power of each within the transmitted signal's bandwidth. Assuming the signal xt x t 's spectrum spans the frequency interval f l f u f l f u , these ratios can be expressed in terms of power spectra.
SIR=2α2∫0∞Pxfdf2∫ f l f u Pifdf SIR 2 α 2 f 0 Px f 2 f f l f u Pi f (2)
SNR=2α2∫0∞PxfdfN0fu-fl SNR 2 α 2 f 0 Px f N0 fu fl (3)
In most cases, the interference and noise powers do not vary for a given receiver. Variations in signal-to-interference and signal-to-noise ratios arise from the attenuation because of transmitter-to-receiver distance variations.

Comments, questions, feedback, criticisms?

Send feedback