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.

中文翻译：

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选频衰落信道中短CPM突发的非相干符号检测

我们考虑在频率选择性衰落信道中检测短的连续相位调制 (CPM) 突发。传统的解决方案包括训练辅助信道估计，然后是相干检测。然而，只有当信道完全已知时，相干检测器的性能才是最佳的，这实际上从来都不是这种情况。实际上，性能受到信道估计质量的限制。这给短脉冲串带来了问题，其中训练符号的数量必须保持较低。当信道未知时，最佳接收器使用可用的先验随机信息从似然函数中边缘化信道并确定使其最大化的传输序列。由于缺乏先验信息以及与边缘化多抽头信道相关的高复杂性，我们导出了一个次优检测器，它用每个假设发射序列的条件最大似然 (ML) 估计替换了未知信道。由此，我们推导出非相干软输入软输出 (SISO) 逐个符号检测器。使用蒙特卡罗模拟来估计误码率 (BER)，我们展示了所提出的方法优于传统方法的优越性，特别是对于时变环境中的极短突发。