# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 2 Related Work  
In this section, we first give a general introduction to the application of deep learning technology in modulation and demodulation and then introduce the development and application of LSTM-AE and codebook structure. The autoencoder network is an unsupervised learning technique that applies backpropagation. Generally speaking, the autoencoder is divided into at least two parts: the encoder and the decoder. One part is responsible for compressing the input data into a low-dimensional latent space, and the other part restores the low-dimensional representation to an output vector close to the input data by decompression. Similar to other networks, the encoder and decoder each have nonlinear activation functions and multiple hidden layers to obtain sufficient mapping capabilities. [PERSON] [10] first considered the feasibility of autoencoder networks in deep learning for end-to-end communication systems, discussed the impact of loss functions and different channel regularization layers, and used an attention model specific to the field of wireless communications to deal with channel effects such as phase offset and frequency offset. Autoencoders' excellent adaptability enables them to have the ability to generate modulation and coding schemes close to the Shannon limit. Two years later, the team introduced adversarial networks into the model to synthesize new physical layer modulation and coding schemes for communication systems without the need for analytical models of channel impairments [11]. This work extended previous research on channel autoencoders to consider cases where the random channel response is unknown or difficult to model with closed-form analytical expressions. However, none of these frameworks have demonstrated the ability to uniformly implement multiple modulation schemes in a single framework. [PERSON] et al. [12] discussed in detail the application of autoencoders in spatial modulation. To balance energy efficiency and detection performance, three spatial modulation frameworks based on autoencoders were proposed for high antenna correlation scenarios. [PERSON] et al. [8] combined the characteristics of Sparse Autoencoders (SAE) and Denoising Autoencoders (DAE) and proposed a stacked autoencoder to implement M-ary phase position shift keying (MPSK) modulation and demodulation. Experiments have shown that the 2 PSK and 4 PSK schemes of the stacked autoencoder model can reach or exceed those of traditional methods. [PERSON] et al. [9] implemented a modulation method for two to one hundred twenty-eight orders using a unified architecture that is close to the performance of traditional modulation schemes in most cases and even exceeds the traditional methods in some cases. However, these studies often require more complex models, which require software and hardware to provide precious resources for storage and use. In the literature cited above, at least eight hidden layers are required to build a complete autoencoder, and complex structures such as stacking or special functional layers are often used to pursue better performance, which makes the number of hidden neurons considerable and becomes a source of resource burden. In addition, the autoencoder models mentioned in the references above also involve the mixed use of multiple different types of neural networks, and there is no simple and efficient unified network architecture to achieve unified modulation of multiple waveforms. Since its introduction, the LSTM structure [13] has had a profound impact on the field of deep learning and sequence modeling and has become one of the most popular models in processing sequence data, time series predictions, natural language processing, and other tasks. [PERSON] [14] combined it with an autoencoder network for multi-sensor anomaly detection. This method learns to reconstruct the behavior of normal time series and uses the reconstruction error to detect anomalies, which is very similar to compression transmission and recovery of signals in communication. [PERSON] et al.