Equal Gain Combining-Based Maximum Likelihood Detector for FFH/MFSK Systems

In this letter, a new multi-user detector based on equal gain combining (EGC) and maximum likelihood (ML) principle is proposed, which is referred to as an EGC-aided ML (EGC-ML) detector. The detection performance of fast frequency-hopping M-ary frequency-shift keying (FFH/MFSK) systems with a novel EGC-ML receiver is investigated in the presence of multi-user interference and additive white Gaussian noise over Rayleigh fading channels. After envelop detection and frequency de-hopping operations, the proposed algorithm picks up several most possible estimations based on the EGC principle and then make final decision via an ML detector within the possible searching range. Our simulation results show that this novel EGC-ML detector may noticeably reduce the complexity of the ML detector with an acceptable bit error rate performance decrement, when a reasonable searching range is chosen by the EGC principle.