When trying to estimate time-varying multipath channels by applying a basis expansion model (BEM) in orthogonal frequency division multiplexing (OFDM) systems, pilot clusters are contaminated by inter-carrier interference (ICI). The pilot cluster ICI (PC-ICI) degrades the estimation accuracy of BEM coefficients, which degrades system performance. In this paper, a PC-ICI suppression scheme is proposed, in which two coded symbols defined as weighted sums of data symbols are inserted on both sides of each pilot cluster. Under the assumption that the channel has Flat Doppler spectrum, the optimized weight coefficients are obtained by an alternating iterative optimization algorithm, so that the sum of the PC-ICI generated by the encoded symbols and the data symbols is minimized. By approximating the optimized weight coefficients, they are independent of the channel tap power. Furthermore, it is verified that the proposed scheme is robust to the estimation error of the normalized Doppler frequency offset and can be applied to channels with other types of Doppler spectra. Numerical simulation results show that, compared with the conventional schemes, the proposed scheme achieves significant improvements in the performance of PC-ICI suppression, channel estimation and system bit-error-ratio (BER).

Traditional pipeline methods for task-oriented dialogue systems are designed individually and expensively. Existing memory augmented end-to-end methods directly map the inputs to outputs and achieve promising results. However, the most existing end-to-end solutions store the dialogue history and knowledge base (KB) information in the same memory and represent KB information in the form of KB triples, making the memory reader's reasoning on the memory more difficult, which makes the system difficult to retrieve the correct information from the memory to generate a response. Some methods introduce many manual annotations to strengthen reasoning. To reduce the use of manual annotations, while strengthening reasoning, we propose a hierarchical memory model (HM2Seq) for task-oriented systems. HM2Seq uses a hierarchical memory to separate the dialogue history and KB information into two memories and stores KB in KB rows, then we use memory rows pointer combined with an entity decoder to perform hierarchical reasoning over memory. The experimental results on two publicly available task-oriented dialogue datasets confirm our hypothesis and show the outstanding performance of our HM2Seq by outperforming the baselines.