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Electrical Communication Systems ECE.09.331 Spring 2010

Electrical Communication Systems ECE.09.331 Spring 2010. Lecture 2a January 27, 2010. Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/spring10/ecomms/. Plan. Digital and Analog Communications Systems Properties of Signals and Noise Terminology

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Electrical Communication Systems ECE.09.331 Spring 2010

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  1. Electrical Communication SystemsECE.09.331Spring 2010 Lecture 2aJanuary 27, 2010 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/spring10/ecomms/

  2. Plan • Digital and Analog Communications Systems • Properties of Signals and Noise • Terminology • Power and Energy Signals • Recall: Fourier Analysis • Fourier Series of Periodic Signals • Continuous Fourier Transform (CFT) and Inverse Fourier Transform (IFT) • Amplitude and Phase Spectrum • Properties of Fourier Transforms

  3. ECOMMS: Topics

  4. Digital Finite set of messages (signals) inexpensive/expensive privacy & security data fusion error detection and correction More bandwidth More overhead (hw/sw) Analog Continuous set of messages (signals) Legacy Predominant Inexpensive Communications Systems

  5. Signal Properties: Terminology • Waveform • Time-average operator • Periodicity • DC value • Power • RMS Value • Normalized Power • Normalized Energy

  6. Power Signal Infinite duration Normalized power is finite and non-zero Normalized energy averaged over infinite time is infinite Mathematically tractable Energy Signal Finite duration Normalized energy is finite and non-zero Normalized power averaged over infinite time is zero Physically realizable Power and Energy Signals • Although “real” signals are energy signals, we analyze them pretending they are power signals!

  7. The Decibel (dB) • Measure of power transfer • 1 dB = 10 log10 (Pout / Pin) • 1 dBm = 10 log10 (P / 10-3) where P is in Watts • 1 dBmV = 20 log10 (V / 10-3) where V is in Volts

  8. ECOMMS: Topics

  9. Fourier Series Infinite sum of sines and cosines at different frequencies Any periodic power signal Fourier Series Fourier Series Applet: http://www.gac.edu/~huber/fourier/

  10. |W(n)| -3f0 -2f0 -f0 f0 2f0 3f0 f Fourier Series Exponential Representation Periodic Waveform w(t) t T0 2-Sided Amplitude Spectrum f0 = 1/T0; T0 = period

  11. Fourier Transform • Fourier Series of periodic signals • finite amplitudes • spectral components separated by discrete frequency intervals of f0 = 1/T0 • We want a spectral representation for aperiodic signals • Model an aperiodic signal as a periodic signal with T0 ----> infinity Then, f0 -----> 0 The spectrum is continuous!

  12. Continuous Fourier Transform Aperiodic Waveform • We want a spectral representation for aperiodic signals • Model an aperiodic signal as a periodic signal with T0 ----> infinity Then, f0 -----> 0 The spectrum is continuous! w(t) t T0 Infinity |W(f)| f f0 0

  13. Continuous Fourier Transform (CFT) Frequency, [Hz] Phase Spectrum Amplitude Spectrum Inverse Fourier Transform (IFT) Definitions See p. 45 Dirichlet Conditions

  14. Properties of FT’s • If w(t) is real, then W(f) = W*(f) • If W(f) is real, then w(t) is even • If W(f) is imaginary, then w(t) is odd • Linearity • Time delay • Scaling • Duality See p. 50 FT Theorems

  15. Summary

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