# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 2 Related Work  
### AE for Communication  
The approach of training an end-to-end communication system was first proposed by [PERSON] et al. in 2017 [PERSON] et al. (2017). Transmitter and receiver are designed as feedforward ANN with multiple dense layers and a normalization layer to ensure power constraints, whereas the channel is represented as an additive noise layer. The complete system is referred to as AE which is a long-known concept in the field of deep learning (DL) (([PERSON] et al., 2018), Chapter 14). In [PERSON] et al. (2017), the system is trained to reduce the block error rate (BLER) and compared to a baseline that uses a Hamming (7,4) code and binary phase shift keying (BPSK) modulation. As a result, the end-to-end system achieves a gain of up to 1 dB due to the joint coding and modulation scheme.  
The previously mentioned AEs are optimized based on the symbol-wise categorical cross-entropy loss and therefore minimizing the BLER of the receiver. As described in [PERSON] et al. (2019), practical systems can benefit from maximizing the bitwise mutual information that is proven to be an achievable rate for bit-metric decoding (BMD) and, thus, bit interleaved coded modulation (BICM) can be applied. Additionally, when trained to minimize the BLER, the learned constellation points must be labeled by bit vectors, which can be a highly sophisticated task. To overcome this limitation, the AE can be trained to directly optimize the bit-metrics, referred to as bitwise AE in the following. This approach was first described by [PERSON] et al. in 2019 [PERSON] et al. (2019). They applied the AE concept to a channel modeling additive radar inference in which standard constellations are not optimal and showed that the AE is able to yield solutions that outperform the standard configurations.  
The idea of a bitwise AE is further evaluated and extended in [PERSON] et al. (2019). On the one hand, the approach is applied to the BICM scenario, and on the other hand, the universality of the method is practically demonstrated and implemented on software-defined radios. Additionally, it is investigated how low-density parity-check (LDPC) codes can be optimized after the system is trained to achieve further gains.  
This concept of an AE-based communication system was first demonstrated via over-the-air measurements in [21]. After the initial model-based training, the encoder and decoder parts were separated and deployed as transmitter and receiver, respectively, where the transmission and recording of the AE-generated signals were realized using software-defined radios (SDRs). As the channel model does not cover all impairments that are later present via over-the-air transmission, a second training step to adapt the decoder part at the receiver to the actual channel and hardware impairments is proposed. Therefore, a large dataset of known AE messages was transmitted and recorded at receiver side over the real channel and then used for supervised re-training of the decoder ANN. This approach of re-training, or _fine-tuning_, showed improved performance. Therefore, in this work, using the bitwise AE structure presented in [10], we will focus on the trainable demapper that enables online fine-tuning to latest noise conditions in wireless channels.