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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) 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)

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Channel Capacity (Chapter 7) (week 6) Discrete Memoryless Channels Random Codes Block Codes Trellis Codes

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Channel Models Discrete Memoryless Channel –Discrete-discrete Binary channel, M-ary channel –Discrete-continuous M-ary channel with soft-decision (analog) –Continuous-continuous Modulated waveform channels (QAM)

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Discrete Memoryless Channel Discrete-discrete –Binary channel, M-ary channel Probability transition matrix

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Discrete Memoryless Channel Discrete-continuous –M-ary channel with soft-decision (analog) output x 0 x 1 x 2. x q-1 y AWGN

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Discrete Memoryless Channel Continuous-continuous –Modulated waveform channels (QAM) –Assume Band limited waveforms, bandwidth = W Sampling at Nyquist = 2W sample/s –Then over interval of N = 2WT samples use an orthogonal function expansion:

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Discrete Memoryless Channel Continuous-continuous –Using orthogonal function expansion:

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Discrete Memoryless Channel Continuous-continuous –Using orthogonal function expansion get an equivalent discrete time channel: Gaussian noise

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Capacity of binary symmetric channel BSC

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Capacity of binary symmetric channel Average Mutual Information

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Capacity of binary symmetric channel Channel Capacity is Maximum Information – earlier showed:

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Capacity of binary symmetric channel Channel Capacity –When p=1 bits are inverted but information is perfect if invert them back!

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Capacity of binary symmetric channel Effect of SNR on Capacity –Binary PAM signal (digital signal amplitude 2A) AGWN

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Capacity of binary symmetric channel Effect of SNR –Binary PAM signal (digital signal amplitude 2A)

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Capacity of binary symmetric channel Effect of SNR –Binary PAM signal (digital signal amplitude 2A)

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Capacity of binary symmetric channel Effect of SNR –Binary PAM signal (digital signal amplitude 2A) Not sure about this Does it depend on bandwidth?

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Capacity of binary symmetric channel Effect of SNR –Binary PAM signal (digital signal amplitude 2A)

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Capacity of binary symmetric channel Effect of SNR –Binary PAM signal (digital signal amplitude 2A)

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Capacity of binary symmetric channel Effect of SNR –Binary PAM signal (digital signal amplitude 2A) At capacity SNR = 7, so waste lots of SNR to get low BER!!!

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Capacity of binary symmetric channel Effect of SNR b –Binary PAM signal (digital signal amplitude 2A)

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Channel Capacity of Discrete Memoryless Channel Discrete-discrete –Binary channel, M-ary channel Probability transition matrix

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Channel Capacity of Discrete Memoryless Channel Average Mutual Information

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Channel Capacity of Discrete Memoryless Channel Channel Capacity is Maximum Information Occurs for only if Otherwise must work out max

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Channel Capacity Discrete Memoryless Channel Discrete-continuous Channel Capacity x 0 x 1 x 2. x q-1 y

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Channel Capacity Discrete Memoryless Channel Discrete-continuous Channel Capacity with AWGN x 0 x 1 x 2. x q-1 y

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Channel Capacity Discrete Memoryless Channel Binary Symmetric PAM-continuous Maximum Information when: x 0 x 1 x 2. x q-1 y

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Channel Capacity Discrete Memoryless Channel Binary Symmetric PAM-continuous Maximum Information when:

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Channel Capacity Discrete Memoryless Channel Binary Symmetric PAM-continuous Versus Binary Symmetric discrete

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Discrete Memoryless Channel Continuous-continuous –Modulated waveform channels (QAM) –Assume Band limited waveforms, bandwidth = W Sampling at Nyquist = 2W sample/s –Then over interval of N = 2WT samples use an orthogonal function expansion:

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Discrete Memoryless Channel Continuous-continuous –Using orthogonal function expansion get an equivalent discrete time channel: Gaussian noise

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Discrete Memoryless Channel Continuous-continuous Capacity is (Shannon)

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Discrete Memoryless Channel Continuous-continuous Maximum Information when: Statistically independent zero mean Gaussian inputs then

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Discrete Memoryless Channel Continuous-continuous Constrain average power in x(t):

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Discrete Memoryless Channel Continuous-continuous Thus Capacity is:

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Discrete Memoryless Channel Continuous-continuous Thus Normalized Capacity is:

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