Orthogonal chirp division multiplexing (OCDM) has emerged as an attractive modulation scheme due to its double-spreading property in both the time and frequency domains. Meanwhile, it is well known that multiple-input multiple-output (MIMO) techniques enhance spatial diversity to combat channel fading. Thus, MIMO-OCDM is promising for reliable high rate wireless communications. When deploying MIMO-OCDM systems, most studies assume perfect channel knowledge at the receivers. However, channel estimation is crucial and should be considered in the system design. In this paper, we apply Alamouti code for MIMO-OCDM over frequency-selective fading channels. In order to facilitate the channel estimation, three pilot-aided transmission (PAT) schemes are proposed, where the pilots are inserted in the frequency, time, and Fresnel domains, respectively. The first two schemes enjoy higher bandwidth efficiency, while the third one is more resistant to burst interference. The channel estimation methods and the optimal PAT designs are developed accordingly for these PAT schemes. Simulation results corroborate the superior performance of the proposed MIMO-OCDM system and channel estimation methods.

Orthogonal chirp division multiplexing (OCDM) is a novel modulation scheme with increased spectral efficiency compared with conventional chirp spectrum spread systems. Meanwhile, it is well known that multiple-input multiple-output (MIMO) techniques can enhance diversity to combat channel fading. Thus, MIMO-OCDM is promising for reliable high-rate wireless communications. In MIMO-OCDM systems, channel estimation is crucial and challenging. In this paper, we apply the Alamouti technique to develop a MIMO-OCDM system and enable its spatial diversity under frequency-selective channels. In order to facilitate the channel estimation, a pilot-aided transmission (PAT) scheme is proposed. The pilots are designed in the Fresnel domain to inherit the double-spreading property of OCDM and be robust to burst interference. The corresponding channel estimator is provided to achieve the lower bound of the mean square error (MSE) of channel estimation. Simulation results verify the superior performance of the proposed channel estimation method for the pilot-aided MIMO-OCDM system.

Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we trained graph neural fin-gerprint docking models for high-throughput virtual COVID-19 drug screening. The graph neural fingerprint models yield high prediction accuracy on docking scores with the mean squared error lower than 0.21 kcal/mol for most of the docking targets, showing significant improvement over conventional circular fin-gerprint methods. To make the neural fingerprints transferable for unknown targets, we also propose a transferable graph neural fingerprint method trained on multiple targets. With comparable accuracy to target-specific graph neural fingerprint models, the training and data efficiency of the transferable model is several times higher. We highlight that the impact of this study extends beyond COVID-19 dataset, as our approach for fast virtual ligand screening can be easily adapted and integrated into a general machine learning-accelerated pipeline to battle future bio-threats.