## 2 Related Works  
The application of machine-learning models for various parts of OFDM receivers has gained increasing interest in recent years. The authors of [2] proposed a Convolutional Neural Network (CNN) for symbol synchronization, channel estimation, demodulation, and channel decoding in channels utilizing BPSK and QPSK modulation. In [3], the authors proposed the use of a Residual Neural Network (ResNet) with depthwise convolutions [4] for channel estimation and demodulation in OFDM channels operating up to 64-QAM, which showed particular promise for a single model against multiple modulation schemes. However, a drawback of utilizing CNNs for these applications is the computational complexity and large number of parameters.  
Graph Neural Networks (GNNs) have also been proposed for various receiver applications to combat this. The authors of [5] proposed a GNN for symbol detection combined with expectation propagation to improve performance in multi-user Multiple-Input Multiple-Output (MU-MIMO) systems. GNNs have also been proposed for use in channel tracking of MIMO systems; for example, in [6]. However, the trade-off for fewer parameters with these models is the requirement for multiple iterations and message passing within the GNN. Other approaches have also been proposed utilizing Long-Short Term Memory (LSTM) models in [7], which show promising results in channel estimation and demodulation in Rician and 3 GPP models.  
Although each model mentioned has shown promise for different portions of an OFDM receiver, other approaches implement deep learning, utilizing multiple separate models to progress toward a completely ML-based receiver. The authors of [8] propose different time domain and frequency domain models to improve an OFDM receiver's response to increased Error Vector Magnitude while still utilizing traditional receiver components.  
For channel decoding, GNNs were applied to BCH and Low-Density Parity Check (LDPC) codes in [9], which utilized the generation matrix for these codes to ensure valid codewords were generated, rather than focusing solely on BER. The results from this work were promising even for Quasi-Cyclic (QC) LDPC codes and 5G LDPC codes. A shared GNN approach was proposed in [10], where the authors reduced the parameter size of the GNN with limited impact on BER. Other work has focused on augmenting current LDPC decoders with trainable parameters to improve the performance of existing Belief Propagation (BP) algorithms [11; 12]. This approach utilizes fewer parameters than the GNN approach by augmenting the BP algorithm with trainable parameters and improving algorithm performance. CNNs with specially crafted butterfly layers have been shown to perform well in learning linear codes [13] and have also been shown to work well on 1D datasets [14].  
These efforts illustrate the potential for augmenting or replacing traditional wireless receiver functionalities with deep learning implementations. However, we observed a relative lack of consideration for resource-constrained devices and how this device class could benefit from such transformations. Additionally, previously published research provides limited results for DL architectures combining the functionalities of channel estimation, symbol demodulation, and channel decoding. To address this shortcoming, we outline the individual results of our separate functional models along with the results of our combined DL architecture.