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1、Simulation of Communication Systems,Wireless Systems Instructional Design,Simulation “hierarchy”,Networks,Links,DSP,Circuits,RF,Event driven simulations: ns2, Opnet,Time driven simulations: SPW, Cossap, Simulink/Matlab,Algorithm simulations: TI CodeComposer,Packets, messages, flows,Waveforms,Circuit
2、 simulations: NC-VHDL/Verilog, Scirroco,RF simulations: PSpice, ADS,XFDTD,Technology,Waveform Level Simulations,Usually used when analytical evaluation of performance is difficult (nonlinearities, ISI caused by bandlimiting filters) Typically: Generate sampled values of the input waveforms (process)
3、 Process them through system models and generate output Estimate the performance by comparing inputs and outputs,Methodology,Ideally model is a perfect replica of the real system hard to do Instead we introduce approximations to reduce complexity or run-time: Modeling level simplification of the spe
4、cific functions Performance evaluation level estimation of performance measures,Methodology (cont.),Modeling: System Modeling - highest level of description; complexity reduction Device Modeling block or subsystem (e.g. transfer function on every clock cycle: “input-transfer-output”) Random Process
5、Modeling: Source random process (imitated with pseudo random number generator RNG) Time-variant random channel Equivalent random process (ERP),Methodology (cont.),Monte Carlo simulation as the name implies relates to game of chance Input signals are assumed to be random processes Objective is to fin
6、d statistical properties of,Model of Communication System,If we do time evolution of all the waveforms - pure Monte Carlo simulation Generating sampled values of all the input processes,Methodology (cont.),Procedure: Generate sampled values of the inputs (e.g. bit sequence U(k), k=1,2,N and noise V(
7、j), j=1,2,mN) Process samples through the model and genarate Y(k) (received bits): Estimate the performance by counting errors In general find expected value of Eg(Y(t) from the simulation according to:,Methodology (cont.),For our example: where If only some input processes are simulated explicitly
8、partial MC (quasianalytical simulation) Random number generation is essential for MC simulations Requires RNG generation methods from a wide variety of distributions and with arbitrary autocorrelation (PSD).,RNG,Important properties: Algebraic Structure (uncorrelated samples) Period Statistical Dist
9、ribution Uniform RNG Congruental or the power residue method,RNG (cont),where M0 large (prime) integer - modulus 0aM - multiplier c 0,1 increment 0 = X(0) = M-1 seed Generates random sequence of integers 0= X(k) = M-1 Uniform RNG,RNG (cont),“Few good men” (for 32 bit machines) For longer periods Wichman-Hill Algorithm combines 3 RNGs: period,RNG (cont),
