UCLA

Multi-UAV deployments create new opportunities for wireless communications. By coordinating the UAVs, they can act as a virtual-antenna-array and use multiantenna communication schemes like distributed MIMO and distributed beamforming (BF).

Distributed MIMO enables a swarm of UAVs to transmit multiple data streams simultaneously to a multiantenna ground station (GS), thus improving the spectral efficiency. Due to the line-of-sight propagation between the swarm and the GS, the MIMO channel is highly correlated, leading to limited multiplexing gains. By optimizing the UAV positions, the swarm can attain the maximum capacity given by the single-user-bound. To achieve this capacity, we propose a centralized approach using block coordinate descent and distributed iterative approach using linear controllers.

Distributed BF can extend the communication range of a remotely deployed swarm, avoiding energy waste in travel towards the destination radio. In order to beamform, the UAVs typically rely on the destination feedback, however, noisy feedback degrades the BF gains. To limit the degradation, we developed an analytical framework to predict the BF gains at a given SNR and used it to optimize the signaling with the destination. The proposed framework was verified experimentally in the lab and using UAV-mounted software-defined-radios (SDR). We also developed a feedback-free BF approach that eliminates the need for destination feedback entirely in a LOS channel. In this approach, one BF radio acts as a guide and moves to point the beam of the remaining radios towards the destination. This approach tolerates localization error and was demonstrated using SDRs.

As for the security considerations, they apply beyond UAVs to any wireless device. Security considerations include radio authentication and interpreting unauthorized signals. For device authentication, we leveraged the radios' RF fingerprint extracted using deep learning and formulated an open set classification problem to reject signals from unauthorized transmitters. We compared several approaches and studied the training dataset impact on performance. To blindly decode unauthorized signals, we proposed the dual path network (DPN) combining digital signal processing and deep learning for modulation classification and blind symbol decoding. DPN design yields interpretable outputs and by jointly estimating the unknown parameters, it improves the modulation classification accuracy.