1. Electrical Communications Systems ECE.09.433 Spring 2019
Lecture 1b January 24, 2019
Shreekanth Mandayam
ECE Department
Rowan University

2. ECOMMS: Topics

3. Plan Baseband and Bandpass Signals Intoduction to Information Theory
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

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

5. 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

6. Frequency Allocations
International Frequency Allocations:
US Frequency Allocation Chart:

7. 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
Info
Source
Sink
Comm
System

8. Another Classification of Signals (Waveforms)
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?

9. Signals and Noise
Lab 1
Comm. Waveform
Signal
(desired)
Noise
(undesired)
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

10. 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

11. Summary