Adaptive Successive Interference Cancelation Using Fuzzy Derived Weights

In wireless communication interference resulting from spatial or temporal sharing of spectrum places a limit on the performance. This paper studies adaptive cancelation of interference by serial (successive) interference cancelation (SIC) technique that uses fuzzy derived cancelation weights. In the past the fuzzy technique has been applied to parallel interference cancelation but not to SIC. The fuzzy inference system uses SNR to estimate the stage interference cancelation weights in multistage SIC rather than selecting them arbitrarily as has been the case in the past. The performance of the fuzzy weighted SIC system proposed herein is evaluated for different number of stages and shapes of membership functions. The simulation results on the system performance are presented in the presence of channel fading and noise. The results show that for overloaded systems, the fuzzy-based system results in a performance that approaches that of the de-correlator detector. This was not possible for a SIC system with arbitrarily chosen weights. The results also conclude that selection of different shapes of membership functions does not make any noticeable difference to the performance.