  Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used
for communication links in many current and emerging Internet of Things (IoT)
applications, including the latest WiFi standards. For such OFDM-based
transceivers, many core physical layer functions related to channel estimation,
demapping, and decoding are implemented for specific choices of channel types
and modulation schemes, among others. To decouple hard-wired choices from the
receiver chain and thereby enhance the flexibility of IoT deployment in many
novel scenarios without changing the underlying hardware, we explore a novel,
modular Machine Learning (ML)-based receiver chain design. Here, ML blocks
replace the individual processing blocks of an OFDM receiver, and we
specifically describe this swapping for the legacy channel estimation, symbol
demapping, and decoding blocks with Neural Networks (NNs). A unique aspect of
this modular design is providing flexible allocation of processing functions to
the legacy or ML blocks, allowing them to interchangeably coexist. Furthermore,
we study the implementation cost-benefits of the proposed NNs in
resource-constrained IoT devices through pruning and quantization, as well as
emulation of these compressed NNs within Field Programmable Gate Arrays
(FPGAs). Our evaluations demonstrate that the proposed modular NN-based
receiver improves bit error rate of the traditional non-ML receiver by
averagely 61% and 10% for the simulated and over-the-air datasets,
respectively. We further show complexity-performance tradeoffs by presenting
computational complexity comparisons between the traditional algorithms and the
proposed compressed NNs.
