# Fuzzy logic based Adaptive Modulation Using Non Data Aided SNR Estimation for OFDM system

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4 Memory block provides inputs to FIS block and compare block. In the proposed system SNR is calculated by amount of deviation. Fig2 shows amount of deviation and corresponding SNR value for Phase component and Fig 3 shows amount of deviation and corresponding SNR value for Quadrature component The deviation caused by the real and imaginary components from the actual values decides the SNR(Signal to Noise ratio). The information of actual values (quadrature and phase components of Modulator and demodulator) and SNR to corresponding deviation is provided by memory block. Fig 2 Deviation real values and corresponding SNR Fig 3 Deviation Imaginary values and corresponding SNR For a system having variable modulation (adaptive modulation system) modulation order will change (constellation will change with modulation order). When modulation order changes, the demodulator sends a control signal to memory to change the information of constellation 2.3 Fuzzy Logic Interface Fuzzy Logic Interface block is an important component in Adaptive modulation system. In the proposed system it is used to calculate instantaneous SNR and to control modulation order. The overall performance of the system depends on Fuzzy logic Interface block. It takes two inputs from Non data Aided SNR estimation block, two inputs from memory. Working of Fuzzy logic interface block can be explained as follows, according to the inputs received from Non data Aided SNR estimation block and present ISSN:

5 modulation scheme (input from memory) fuzzy logic interface block controls the modulation order.. Fuzzy logic interface block is modeled in Matlab 7.4 Fuzzy Interface editor. Fig 5 Fuzzy logic Interface system Fuzzy interface System was shown in Fig 5.. It consists of three input variables namely imaginary, real and present modulation. It consists of one output variable namely modulation order which controls the modulation order of adaptive modulation and demodulation blocks Fig 6 Input function Real part The membership functions of input variables are shown in Fig 6,Fig 7 and the membership functions of output variable is shown in Fig 8. Input variable real part consists of seven membership functions which gives the deviation from original value ISSN:

6 Fig 7 Membership function of Input variable Imaginary part Input variable imaginary part also consists of seven membership functions and out put variable modulation order consists of seven membership functions named 4 QAM,8QAM, 16QAM, 32QAM, 64QAM, 128QAM and 256QAM as shown in Fig 8. Fig 8 Output function modulation order Rules are edited in rules editor which was shown in Fig 9. Final output is shown and explained in section 3 ISSN:

7 Fig 9 Rules Editor 3.Results The above proposed system was simulated in Matlab7.4. Using fuzzy logic in decision making and in SNR estimation is a good choice because ordinary (non fuzzy) system is controlled by plain if and else, for example if for poor SNR(Signal To Noise Ratio) range is declared as 0 to 4, if input is 4.1 then the input is not considered as poor SNR (But it is poor). If we use fuzzy logic in above case 4.1 is also considered as poor SNR (Signal To Noise Ratio). So using FIS (Fuzzy interface system) increases the performance adaptive modulation system. Fig 9 simulation result Fig 9 shows the output of FIS (fuzzy interface system) for given set of inputs, output is selected based on given rules. Bit Error Rate performance of the simulated system is shown in Fig 10. ISSN:

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