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Outline. Transmitters (Chapters 3 and 4, Source Coding and Modulation) (week 1 and 2) Receivers (Chapter 5) (week 3 and 4) Received Signal Synchronization (Chapter 6) (week 5) Channel Capacity (Chapter 7) (week 6) Error Correction Codes (Chapter 8) (week 7 and 8)

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Presentation Transcript
outline
Outline
  • Transmitters (Chapters 3 and 4, Source Coding and Modulation) (week 1 and 2)
  • Receivers (Chapter 5) (week 3 and 4)
  • Received Signal Synchronization (Chapter 6) (week 5)
  • Channel Capacity (Chapter 7) (week 6)
  • Error Correction Codes (Chapter 8) (week 7 and 8)
  • Equalization (Bandwidth Constrained Channels) (Chapter 10) (week 9)
  • Adaptive Equalization (Chapter 11) (week 10 and 11)
  • Spread Spectrum (Chapter 13) (week 12)
  • Fading and multi path (Chapter 14) (week 12)
digital communication system
Digital Communication System:

Information per bit increases

Bandwidth efficiency increases

noise immunity increases

Transmitter

Receiver

increasing information per bit
Increasing Information per Bit
  • Information in a source
    • Mathematical Models of Sources
    • Information Measures
  • Compressing information
    • Huffman encoding
      • Optimal Compression?
    • Lempel-Ziv-Welch Algorithm
      • Practical Compression
  • Quantization of analog data
    • Scalar Quantization
    • Vector Quantization
    • Model Based Coding
    • Practical Quantization
      • m-law encoding
      • Delta Modulation
      • Linear Predictor Coding (LPC)
scalar quantization
Scalar Quantization
  • Optimum quantization based on random variable assumption for signal is possible through nonuniform quantization
  • Does not buy much, few dB
  • Arbitrary non uniform quantization, such as -law, works well for speech (>20 dB) better)
vector quantization
Vector Quantization
  • Sort of the equivalent of block coding
  • Better rates obtained for groups of analog inputs coded as vectors
  • Works great on statistically dependant analog samples like severely band limited signals or coded analog like speech
vector quantization6
Vector Quantization

distortion

e.g., l2 norm

Average distortion

vector quantization7
Vector Quantization
  • K-Means Algorithm
    • Guess
    • Classify the vectors by
    • Compute new
    • Iterate till D does not change
    • Finds local minimum based on

into

Centroid of

vector quantization8
Vector Quantization
  • Optimal Coding for lots of dimensions
    • If the number of dimensions is increased
    • Then D approaches optimal value
practical coding of analog
Practical Coding of Analog
  • m-law encoding
  • Delta Modulation
  • Linear Predictor Coding (LPC)
m law encoding
m-law encoding
  • =255 reduces noise power in speech ~20dB
delta modulation
Delta Modulation
  • Sends quantized error between input and code

1

0

1

0

1

1

1

1

1

delta modulation12
Delta modulation
  • Need only 1-bit quantizer and adder (integrator)
linear predictor coding
Linear Predictor Coding
  • Learn parameters of filter to fit input speech
  • Can solve for ai if we have a training sample
  • This is feasible and is one of the better speech codes
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