We developed machine learning approaches for data-driven trellis-based soft symbol detection in coded transmission over intersymbol interference (ISI) channels in presence of bursty impulsive noise (IN), for example encountered in wireless digital broadcasting systems and vehicular communications. This enabled us to obtain optimized detectors based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm while circumventing the use of full channel state information (CSI) for computing likelihoods and trellis state transition probabilities. First, we extended the application of the neural network (NN)-aided BCJR, recently proposed for ISI channels with additive white Gaussian noise (AWGN). Although suitable for estimating likelihoods via labeling of transmission sequences, the BCJR-NN method does not provide a framework for learning the trellis state transitions. In addition to detection over the joint ISI and IN states we also focused on another scenario where trellis transitions are not trivial: detection for the ISI channel with AWGN with inaccurate knowledge of the channel memory at the receiver. Without access to the accurate state transition matrix, the BCJR- NN performance significantly degrades in both settings. To this end, we devised an alternative approach for data-driven BCJR detection based on the unsupervised learning of a hidden Markov model (HMM). The BCJR-HMM allowed us to optimize both the likelihood function and the state transition matrix without labeling. Moreover, we demonstrated the viability of a hybrid NN and HMM BCJR detection where NN is used for learning the likelihoods, while the state transitions are optimized via HMM. While reducing the required prior channel knowledge, the examined data-driven detectors with learned trellis state transitions achieve bit error rates close to the optimal full CSI-based BCJR, significantly outperforming detection with inaccurate CSI.

翻译：针对突发脉冲噪声（例如无线数字广播系统与车载通信中遇到的）干扰下的码间串扰信道编码传输，我们开发了基于机器学习的格状数据驱动软符号检测方法。该方法基于Bahl-Cocke-Jelinek-Raviv（BCJR）算法实现检测器优化，同时避免使用完整信道状态信息（CSI）计算似然函数与格状状态转移概率。首先，我们扩展了近期针对加性高斯白噪声（AWGN）ISI信道提出的神经网络（NN）辅助BCJR算法应用。虽然通过传输序列标注可估计似然函数，但BCJR-NN方法未提供学习格状状态转移的框架。除联合ISI与IN状态检测外，我们重点研究了另一种非平凡格状转移场景：接收端信道记忆长度不准确时AWGN ISI信道的检测问题。在无法获取准确状态转移矩阵的情况下，BCJR-NN在这两种场景中的性能均显著下降。为此，我们基于隐马尔可夫模型（HMM）的无监督学习，设计了另一种数据驱动BCJR检测方法。BCJR-HMM无需标注即可同时优化似然函数与状态转移矩阵。此外，我们验证了混合NN与HMM的BCJR检测可行性：利用NN学习似然函数，通过HMM优化状态转移。所研究的数据驱动检测器通过学习格状状态转移，在降低先验信道知识需求的同时，实现了接近最优完整CSI BCJR的误码率，显著优于采用不准确CSI的检测方案。