TWIRLD: Transformer Generated Terahertz Waveform for Improved Radio Link Distance

Abstract

terahertz (THz) band communication is envisioned as one of the leading technologies to meet the exponentially growing data rate requirements of emerging and future wireless communication networks. Utilizing the contiguous bandwidth available at THz frequencies requires a transceiver design tailored to tackle issues existing at these frequencies such as strong propagation and absorption loss, small scale fading (e.g. scattering, reflection, refraction), hardware non-linearity, etc. In prior works, multicarrier waveforms (e.g., Orthogonal Frequency Division Multiplexing (OFDM)) are shown to be efficient in tackling some of these issues. However, OFDM introduces a drawback in the form of peak-to-average power ratio (PAPR) which, compounded with strong propagation and absorption loss and high noise power due to large bandwidth at THz and sub-THz frequencies, severely limits link distances and, in turn, capacity, preventing efficient bandwidth usage. In this work, we propose TWIRLD - a deep learning (DL)-based joint optimization method, modeled and implemented as components of end-to-end transceiver chain. TWIRLD performs a symbol remapping at baseband of OFDM signals, which increases average transmit power while also optimizing the bit error rate (BER). We provide theoretical analysis, statistical equivalence of TWIRLD to the ideal receiver, and comprehensive complexity and footprint estimates. We validate TWIRLD in simulation showing link distance improvement of up to 91% and compare the results with legacy and state of the art methods and their enhanced versions. Finally, TWIRLD is validated with over the air (OTA) communication using a state-of-the-art testbed at 140 GHz up to a bandwidth of 5 GHz where we observe improvement of up to 79% in link distance accommodating for practical channel and other transmission losses.