**5. FIS (Fuzzy Interface system)**

FIS (Fuzzy Interface system) is the decision making system in Channel Estimator (SNR estimator) used in adaptive modulation . It is modeled in Matlab 7.4 Fuzzy Interface editor . It takes instantaneous SNR (Signal to Noise Ratio) and Present modulation order as inputs and controls the modulation order of modulator and demodulator blocks.

Fig. 11. FIS system

FIS (Fuzzy Interface System) consists of two inputs and one output as shown in figure above Input one SNR and input two present mod . The membership function of SNR and present mod is shown in figures 12 and 13. For SNR, three membership functions are taken namely low \_SNR, medium\_SNR and high\_SNR. For present modulation six membership functions are taken namely QPSK, 8QAM, 16QAM, 32QAM, and 64QAM and128QAM.


Fig. 13. Present mod membership functions

Fig. 14. Output membership function

Fig. 15. Rules editor

Rules are edited in rules editor which gives conditions to select modulation order depending on channel estimation (SNR). Example for rules are as follows, if BER is low increase the modulation order by one level compared to present modulation order , if BER is high then decrease the modulation order by one compared to present modulation order. If BER is average then increase the modulation order by one compared to present modulation order.

The above proposed system was simulated in Matlab7.4., Using fuzzy logic in decision making is a good choice because ordinary (non fuzzy) system is controlled by plain if and else statements, 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. So using FIS (Fuzzy interface system) increases the performance adaptive modulation system.

Fig 16 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 17.

Fig. 16. Simulation result

Fig 17 shows comparison bit error rates of adaptive modulated OFDM system and fixed modulated OFDM system. Fig 18 gives plot showing the comparison of performances of adaptive modulated OFDM system using fuzzy logic and adaptive modulated OFDM system using ordinary control logic. It was shown that OFDM system using Fuzzy logic performs better than OFDM system using ordinary control logic. Using FIS (Fuzzy interface system) in implementing adaptive modulation for OFDM system increases performance of system since it responds to channel condition and maintains good performance (Bit Error Rate) and capacity (spectral efficiency) efficiently than system using ordinary control logic.

Fig. 17. BER comparison of proposed scheme and fixed modulation schemes.

Fig. 18. Comparing Adaptive modulation schemes using FIS and using ordinary control logic
