## 1 Introduction  
With the development of mobile multimedia services such as high-definition video and XR, mobile communication systems have put forward higher and higher requirements for transmission rates. In the current situation of increasingly tight spectrum resources, improving the spectrum efficiency of the transmission system has increasingly become a necessary way to improve the throughput of the communication system [1]. As a non-orthogonal transmission technology that can achieve higher spectral efficiency, FTN has attracted widespread attention in academia and industry worldwide. FTN realizes the compression of the transmitted signal in the time domain and the frequency domain by introducing a certain amount of ISI at the transmitting end in advance [2, 3, 4, 5], so as to obtain higher spectral efficiency than traditional orthogonal transmission technology. As a non-orthogonal transmission technology, FTN breaks the traditional Nyquist criterion. In the case of artificially introducing inevitable ISI, Mazo's research shows that the data rate can be increased by about 25% without performances. Although the existence of Mazo Limit enables FTN transmission to improve the spectrum efficiency of the system while ensuring the reliability of the transmission, because the FTN system will inevitably introduce ISI, it is necessary for the receiving end to use detection techniques to eliminate ISI. However, compared to the orthogonal system, the receiver only needs to go through simple matched filtering to achieve the best detection symbol-by-symbol, a non-orthogonal FTN system requires more complex receiver processing algorithms to eliminate interference caused by ISI in order to obtain real gains. Therefore, it can be considered that designing a detection algorithm with good performance and acceptable computational complexity is the key to FTN research. In recent years, research on FTN has mainly focused on model-driven detection algorithms, but for FTN signaling with higher compression rates, whether it isinear detection algorithms based on MMSE or ZF, or non-linear detection algorithms such as MAP or BCJR, the performance is not ideal, and theatre's implementation complexity is even very high. In order to overcome the above-mentioned problems, the FTN signal detection algorithm based on data-driven has gradually attracted the attention of scholars. Preliminary research results show that under the condition of a high compression rate, the performance of the FTN signal detection algorithm based on deepearning is better than the traditional model-driven algorithm. However, due to the huge differences between training data and test data in time-varying multipath channels in terms of channel characteristics such as multipath delay expansion and moving speed, the FTN signal detection algorithm based on deepearning has a serious problem of insufficient generalization. Therefore, the current related research mainly focuses on FTN signal detection in the AWGN channel or the microwave communication environment that can be similar to AWGN. In this article, we propose a DMD-JD detection algorithm to solve the problem of FTN signal detection in time-varying multipath channels. The model-driven part adoptslinear equalization technology to eliminate the influence of channel multipath fading to improve the generalization ability of the model. The data-driven part adopts the Deep-Bi-LSTM deepearning network model based on Long Short-Term Memory (LSTM) and uses the two-way network structure to obtain the ISI information caused by the adjacent symbols before and after the symbol of each FTN signaling to eliminate the influence of ISI caused by FTN signaling compression. The channel equalization adopts theinear equalization algorithm, and the deepearning network can complete the training offline. Therefore, the overall implementation complexity of the proposed DMD-JD detection algorithm is stillower than that of the traditional nonlinear detection algorithm, which improves the engineering practicability of the algorithm.