# A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV

arXiv: https://arxiv.org/abs/2405.17015 · HTML: https://mlsanity.com/paper/2405.17015/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2024.

## Built on by (most cited first, 2 of 2)

- [Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2412.04074/) (2024)
- [A Cognitive Framework for Autonomous Agents: Toward Human-Inspired Design](https://mlsanity.com/paper/2601.16648/) (2026)

## Abstract (arXiv)

In this paper, we consider a scenario with one UAV equipped with a ULA, which sends combined information and sensing signals to communicate with multiple GBS and, at the same time, senses potential targets placed within an interested area on the ground. We aim to jointly design the transmit beamforming with the GBS association to optimize communication performance while ensuring high sensing accuracy. We propose a predictive beamforming framework based on a dual DNN solution to solve the formulated nonconvex optimization problem. A first DNN is trained to produce the required beamforming matrix for any point of the UAV flying area in a reduced time compared to state-of-the-art beamforming optimizers. A second DNN is trained to learn the optimal mapping from the input features, power, and EIRP constraints to the GBS association decision. Finally, we provide an extensive simulation analysis to corroborate the proposed approach and show the benefits of EIRP, SINR performance and computational speed.