Noncoherent Symbol Detection of Short CPM Bursts in Frequency-Selective Fading Channels

Abstract

We consider the detection of short continuous phase modulation (CPM) bursts in a frequency-selective fading channel. The conventional solution comprises training-aided channel estimation followed by coherent detection. However, the performance of the coherent detector is optimal only when the channel is perfectly known, which is practically never the case. In practice, the performance is limited by the quality of the channel estimate. This poses a problem for short bursts, where the number of training symbols must be kept low. When the channel is unknown, the optimal receiver uses available a priori stochastic information to marginalize the channel out of the likelihood function and determine the transmit sequence that maximizes it. Due to the lack of a priori information and the high complexity associated with marginalizing out a multi-tap channel, we derive a suboptimal detector, which replaces the unknown channel by the conditional maximum likelihood (ML) estimate for each hypothesis transmit sequence. From that, we derive a noncoherent soft-input soft-output (SISO) symbol-by-symbol detector. Using Monte-Carlo simulations to estimate the bit error rate (BER), we show the superiority of the proposed approach over the conventional one, especially for extremely short bursts in a time-variant environment.