Dynamic Joint PHY-MAC Waveform Design for IoT Connectivity

We envision dense network deployments of Internet-of-Things (IoT) connected devices that report data to a common base station (BS). The devices utilize repeats of a basic shaping pulse occupying the entire continuum of the device-accessible spectrum. We propose an optimal algorithm to adaptively design sparse waveforms with well-placed energy that maximize the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR linear filter at the BS. Additionally, we propose a computationally efficient suboptimal waveform design algorithm for the same problem. Simulation studies show that the proposed waveform designs attain superior pre-detection SINR performance than conventional binary, quaternary, and sparse-binary/quaternary waveform designs, thus offering a promising PHY-MAC approach to maintain wireless connectivity in overloaded network setups.