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EE 3220: Digital Communication

EE 3220: Digital Communication. Lec-13: Shannon and modulation schemes. Dr. Hassan Yousif Ahmed Department of Electrical Engineering College of Engineering at Wadi Aldwasser Slman bin Abdulaziz University. Goals in designing a DCS. Goals: Maximizing the transmission bit rate

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EE 3220: Digital Communication

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  1. EE 3220: Digital Communication Lec-13: Shannon and modulation schemes Dr. Hassan Yousif Ahmed Department of Electrical Engineering College of Engineering at Wadi Aldwasser Slman bin Abdulaziz University Dr Hassan Yousif

  2. Goals in designing a DCS • Goals: • Maximizing the transmission bit rate • Minimizing probability of bit error • Minimizing the required power • Minimizing required system bandwidth • Maximizing system utilization • Minimize system complexity Dr Hassan Yousif

  3. Error probability plane(example for coherent MPSK and MFSK)‏ M-FSK M-PSK bandwidth-efficient power-efficient k=5 k=4 k=1 k=2 Bit error probability k=4 k=3 k=5 k=1,2 Dr Hassan Yousif

  4. Limitations in designing a DCS • Limitations: • The Nyquist theoretical minimum bandwidth requirement • The Shannon-Hartley capacity theorem (and the Shannon limit)‏ • Government regulations • Technological limitations • Other system requirements (e.g satellite orbits)‏ Dr Hassan Yousif

  5. Nyquist minimum bandwidth requirement • The theoretical minimum bandwidth needed for baseband transmission of Rs symbols per second is Rs/2 hertz. Dr Hassan Yousif

  6. Shannon limit • Channel capacity: The maximum data rate at which error-free communication over the channel is performed. • Channel capacity of AWGV channel (Shannon-Hartley capacity theorem): Dr Hassan Yousif

  7. Shannon limit … • The Shannon theorem puts a limit on the transmission data rate, not on the error probability: • Theoretically possible to transmit information at any rate , with an arbitrary small error probability by using a sufficiently complicated coding scheme • For an information rate , it is not possible to find a code that can achieve an arbitrary small error probability. Dr Hassan Yousif

  8. Shannon limit … C/W [bits/s/Hz] Unattainable region Practical region SNR [bits/s/Hz] Dr Hassan Yousif

  9. Shannon limit … • There exists a limiting value of below which there can be no error-free communication at any information rate. • By increasing the bandwidth alone, the capacity can not be increased to any desired value. Shannon limit Dr Hassan Yousif

  10. Shannon limit … W/C [Hz/bits/s] Practical region Unattainable region -1.6 [dB] Dr Hassan Yousif

  11. Bandwidth efficiency plane R/W [bits/s/Hz] R=C R>C Unattainable region M=256 M=64 Bandwidth limited M=16 M=8 M=4 R<C Practical region M=2 M=4 M=2 M=8 M=16 MPSK MQAM MFSK Shannon limit Power limited Dr Hassan Yousif

  12. Power and bandwidth limited systems • Two major communication resources: • Transmit power and channel bandwidth • In many communication systems, one of these resources is more precious than the other. Hence, systems can be classified as: • Power-limited systems: • save power at the expense of bandwidth (for example by using coding schemes)‏ • Bandwidth-limited systems: • save bandwidth at the expense of power (for example by using spectrally efficient modulation schemes) Dr Hassan Yousif

  13. M-ary signaling • Bandwidth efficiency: • Assuming Nyquist (ideal rectangular) filtering at baseband, the required passband bandwidth is: • M-PSK and M-QAM (bandwidth-limited systems)‏ • Bandwidth efficiency increases as M increases. • MFSK (power-limited systems)‏ • Bandwidth efficiency decreases as M increases. Dr Hassan Yousif

  14. Design example of uncoded systems • Design goals: • The bit error probability at the modulator output must meet the system error requirement. • The transmission bandwidth must not exceed the available channel bandwidth. Input M-ary modulator Output M-ary demodulator Dr Hassan Yousif

  15. Design example of uncoded systems … • Choose a modulation scheme that meets the following system requirements: Dr Hassan Yousif

  16. Design example of uncoded systems … • Choose a modulation scheme that meets the following system requirements: Dr Hassan Yousif

  17. Design example of coded systems • Design goals: • The bit error probability at the decoder output must meet the system error requirement. • The rate of the code must not expand the required transmission bandwidth beyond the available channel bandwidth. • The code should be as simple as possible. Generally, the shorter the code, the simpler will be its implementation. Input Encoder M-ary modulator Output Decoder M-ary demodulator Dr Hassan Yousif

  18. Design example of coded systems … • Choose a modulation/coding scheme that meets the following system requirements: • The requirements are similar to the bandwidth-limited uncoded system, except that the target bit error probability is much lower. Dr Hassan Yousif

  19. Design example of coded systems • Using 8-PSK, satisfies the bandwidth constraint, but not the bit error probability constraint. Much higher power is required for uncoded 8-PSK. • The solution is to use channel coding (block codes or convolutional codes) to save the power at the expense of bandwidth while meeting the target bit error probability. Dr Hassan Yousif

  20. Design example of coded systems • For simplicity, we use BCH codes. • The required coding gain is: • The maximum allowed bandwidth expansion due to coding is: • The current bandwidth of uncoded 8-PSK can be expanded by still 25% to remain below the channel bandwidth. • Among the BCH codes, we choose the one which provides the required coding gain and bandwidth expansion with minimum amount of redundancy. Dr Hassan Yousif

  21. Design example of coded systems … • Bandwidth compatible BCHcodes Coding gain in dB with MPSK Dr Hassan Yousif

  22. Design example of coded systems … • Examine that the combination of 8-PSK and (63,51) BCH codes meets the requirements: Dr Hassan Yousif

  23. Effects of error-correcting codes on error performance • Error-correcting codes at fixed SNR influence the error performance in two ways: • Improving effect: • The larger the redundancy, the greater the error-correction capability • Degrading effect: • Energy reduction per channel symbol or coded bits for real-time applications due to faster signaling. • The degrading effect vanishes for non-real time applications when delay is tolerable, since the channel symbol energy is not reduced. Dr Hassan Yousif

  24. Bandwidth efficient modulation schemes • Offset QPSK (OQPSK) and Minimum shift keying • Bandwidth efficient and constant envelope modulations, suitable for non-linear amplifier • M-QAM • Bandwidth efficient modulation • Trellis coded modulation (TCM)‏ • Bandwidth efficient modulation which improves the performance without bandwidth expansion Dr Hassan Yousif

  25. Course summary • In a big picture, we studied: • Fundamentals issues in designing a digital communication system (DSC)‏ • Basic techniques: formatting, coding, modulation • Design goals: • Probability of error and delay constraints • Trade-off between parameters: • Bandwidth and power limited systems • Trading power with bandwidth and vise versa Dr Hassan Yousif

  26. Block diagram of a DCS Source encode Channel encode Pulse modulate Bandpass modulate Format Digital modulation Channel Digital demodulation Source decode Demod. Sample Channel decode Format Detect Dr Hassan Yousif

  27. Course summary – cont’d • In details, we studies: • Basic definitions and concepts • Signals classification and linear systems • Random processes and their statistics • WSS, cyclostationary and ergodic processes • Autocorrelation and power spectral density • Power and energy spectral density • Noise in communication systems (AWGN)‏ • Bandwidth of signal • Formatting • Continuous sources • Nyquist sampling theorem and aliasing • Uniform and non-uniform quantization Dr Hassan Yousif

  28. Course summary – cont’d • Channel coding • Linear block codes (cyclic codes and Hamming codes)‏ • Encoding and decoding structure • Generator and parity-check matrices (or polynomials), syndrome, standard array • Codes properties: • Linear property of the code, Hamming distance, minimum distance, error-correction capability, coding gain, bandwidth expansion due to redundant bits, systematic codes Dr Hassan Yousif

  29. Course summary – cont’d • Convolutional codes • Encoder and decoder structure • Encoder as a finite state machine, state diagram, trellis, transfer function • Minimum free distance, catastrophic codes, systematic codes • Maximum likelihood decoding: • Viterbi decoding algorithm with soft and hard decisions • Coding gain, Hamming distance, Euclidean distance, affects of free distance, code rate and encoder memory on the performance (probability of error and bandwidth)‏ Dr Hassan Yousif

  30. Course summary – cont’d • Modulation • Baseband modulation • Signal space, Euclidean distance • Orthogonal basic function • Matched filter to reduce ISI • Equalization to reduce channel induced ISI • Pulse shaping to reduce ISI due to filtering at the transmitter and receiver • Minimum Nyquist bandwidth, ideal Nyquist pulse shapes, raise cosine pulse shape Dr Hassan Yousif

  31. Course summary – cont’d • Baseband detection • Structure of optimum receiver • Optimum receiver structure • Optimum detection (MAP)‏ • Maximum likelihood detection for equally likely symbols • Average bit error probability • Union bound on error probability • Upper bound on error probability based on minimum distance Dr Hassan Yousif

  32. Course summary – cont’d • Passband modulation • Modulation schemes • One dimensional waveforms (ASK, M-PAM)‏ • Two dimensional waveforms (M-PSK, M-QAM)‏ • Multidimensional waveforms (M-FSK)‏ • Coherent and non-coherent detection • Average symbol and bit error probabilities • Average symbol energy, symbol rate, bandwidth • Comparison of modulation schemes in terms of error performance and bandwidth occupation (power and bandwidth)‏ Dr Hassan Yousif

  33. Course summary – cont’d • Trade-off between modulation and coding • Channel models • Discrete inputs, discrete outputs • Memoryless channels : BSC • Channels with memory • Discrete input, continuous output • AWGN channels • Shannon limits for information transmission rate • Comparison between different modulation and coding schemes • Probability of error, required bandwidth, delay • Trade-offs between power and bandwidth • Uncoded and coded systems Dr Hassan Yousif

  34. Information about the exam: • Exam date: • 8th of March 2008 (Saturday)‏ • Allowed material: • Any calculator (no computers)‏ • Mathematics handbook • Swedish-English dictionary • A list of formulae that will be available with the exam. Dr Hassan Yousif

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