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Introduction to Electrical Communication Systems - Lecture 1b Overview

This lecture explores foundational concepts in Electrical Communication Systems, covering both baseband and bandpass signals. Key topics include the importance of high frequencies, international frequency allocations, and an introduction to information theory. The discussion on signals delves into deterministic and stochastic models, noise considerations, and the measures of information such as probability, entropy, and Shannon’s theorem. This session serves as a foundation for understanding communication systems and their applications in engineering.

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Introduction to Electrical Communication Systems - Lecture 1b Overview

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  1. Electrical Communications SystemsECE.09.331Spring 2009 Lecture 1bJanuary 21, 2009 Shreekanth Mandayam ECE Department Rowan University http://users.rowan.edu/~shreek/spring09/ecomms/

  2. ECOMMS: Topics

  3. ECOMMS: Topics

  4. Plan • Baseband and Bandpass Signals • Recall: Comm. Sys. Block diagram • Aside: Why go to higher frequencies? • International & US Frequency Allocations • Intoduction to Information Theory • Recall: List of topics • Probability • Information • Entropy • Signals and Noise

  5. Baseband Signal Baseband Signal Bandpass Signal Demodulation or Detection Modulation Comm. Sys. Bock Diagram Noise Channel Rx m(t) Tx r(t) s(t) • “Low” Frequencies • <20 kHz • Original data rate • “High” Frequencies • >300 kHz • Transmission data rate Formal definitions will be provided later

  6. Aside: Why go to higher frequencies? Half-wave dipole antenna c = f l c = 3E+08 ms-1 Calculate l for f = 5 kHz f = 300 kHz Tx l/2 There are also other reasons for going from baseband to bandpass

  7. Frequency Allocations • International Frequency Allocations: http://www.fcc.gov/oet/spectrum/table/Welcome.html • US Frequency Allocation Chart: http://www.ntia.doc.gov/osmhome/allochrt.html

  8. Info Sink Info Source Comm System Information • Recall: • Information Source: a system that produces messages (waveforms or signals) • Digital/Discrete Information Source: Produces a finite set of possible messages • Digital/Discrete Waveform: A function of time that can only have discrete values • Digital Communication System: Transfers information from a digital source to a digital sink

  9. Deterministic Signals: Can be modeled as a completely specified function of time Random or Stochastic Signals: Cannot be completely specified as a function of time; must be modeled probabilistically What type of signals are information bearing? Another Classification of Signals (Waveforms)

  10. Signals and Noise Lab 1 Comm. Waveform • Strictly, both signals and noise are stochastic and must be modeled as such • We will make these approximations, initially: • Noise is ignored • Signals are deterministic Noise (undesired) Signal (desired)

  11. Measures of Information • Definitions • Probability • Information • Entropy • Source Rate • Recall: Shannon’s Theorem • If R < C = B log2(1 + S/N), then we can have error-free transmission in the presence of noise MATLAB DEMO: entropy.m

  12. Summary

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