[理学]北大数字通信课件：a-- 3-2 Simulation of 16QAM systems.ppt

Lecture 2: Simulation of 16QAM systems March 28 – April 19 2008revised 5 Oct 2008 Yuping Zhao (Doctor of Science in technology) Professor, Peking University Beijing, China Yuping.zhao@pku.edu.cn What we learn here? How to simulate the continues signals using discrete samples of the signals How to design the Matched filters that fulfill Nyquist conditions How to add the noise signals for given SNR, sampling rate, modulation level ? How to simulation Eye diagram How to evaluate the BER, Symbol error rate (theory and simulations) Transmitter Eye diagram without noise * Block diagram of the communication system Generate binary data x=randint(1,N), randomly generate binary data of length N As the starting point, we suggest N=2000 Each symbol contains 4 bits, the values in real and imaginary are 3，-1，1，3. The bits 1,3 corresponds to real part, the bits 2,4 corresponds to the imaginary part. 16QAM Modulation module You may modulate real and imaginary parts separately The real part corresponds to bits 1,3, using Gray mapping, the constellation is as follows 00 01 11 10 -3 -1 1 3 Binary data Modulated symbol The imaginary part do the same way. The modulated symbols are x = a + jb, or x = I + jQ Transmitted signal constellation 4. Up-sampling module The reason for up sampling: The samples are impulse signals, its spectrum is extremely wide. The signals should pass the lowpass filters before transmission. In the hardware implementation, the continues signals is transmitted Hardware process for the transmitted signals Modulated samples Up sampling Digital Filter Analog Filter Carrier Modulation Digital process Analog process Anatenna Implementation Assume the up-sampling rate is Then we place zeros after each sample -1 In the simulation, you can chose any up-sampling values, for example: 8 In the hardware implementation, the typical value is 4 For real and imaginary parts, do the up-sampling separately The values can be +3,+1,-1,-3,0 Low-pass filtering To limit the