ECE 283 Digital Communication All FacultyECE 283数字通信所有的教员.pptVIP

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: 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 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 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 Quantization Vector Quantization K-Means Algorithm Guess Classify the vectors by Compute new Iterate till D does not change Finds local minimum based on Vector Quantization Optimal Coding for lots of dimensions If the number of dimensions is increased Then D approaches optimal value Practical Coding of Analog m-law encoding Delta Modulation Linear Predictor Coding (LPC) m-law encoding ?=255 reduces noise power in speech ~20dB Delta Modulation Sends quantized error between input and code Delta modulation Need only 1-bit quantizer and adder (integrator) Linear Predictor Coding Learn parame