Compressed Time-Frequency Channel Beamforming Using Empirical MIMO-UWB RFs for Indoor Jobshop

Abstract

Most collaborative analog-digital beamforming precoder architectures are commonly deployed for ground-to-underground (G2U) layer system sensing. The architectures are designed based on the target system's signal channel links in order to leverage its array and multiplexing millimeter wave (mmWave) gains. Such gains includes link quality, energy consumption, delay and packet accuracy. But recent designs have mostly targeted narrowband-based channels and fewer wideband mmWave domains. In this paper, novel sparse-formulated in time-frequency compressed process resource block (PRB) beamforming, to decode-and-forward (DF) packets by joint-relaying it over radio frequency (RF) mmWave is proposed. The model is deployed into an indoor jobshop with remote sensing capability. The routing path's transmission energy is minimized using an optimized three-way next-hop node selection over empirically characterized MIMO-ultrawideband RFs of dissimilar soil's volumetric water content (VWC) and burial depths. The novel collaborative time-frequency PRB-based approach is further exploited for estimating the wideband mmWave channels. Results are corroborated by calibrating the system's minimum transmission energy (MTE), packet-reception-ratio (PRR), link quality indicator (LQI), and end-to-end delay profiles using the latest vector network analyzer (VNA) 8722ES device.