Publisher Copyright: © 2026 IEEE. EC/HE/101192113/EU//AMBIENT-6G

Backscatter communication (BC) becomes a promising energy-efficient solution for future wireless sensor networks (WSNs). Unmanned aerial vehicles (UAVs) enable flexible data collection from remote backscatter devices (BDs), yet conventional UAVs rely on omni-directional fixed-position antennas (FPAs), limiting channel gain and prolonging data collection time. To address this issue, we consider equipping a UAV with a directional movable antenna (MA) with high directivity and flexibility. The MA enhances channel gain by precisely aiming its main lobe at each BD, focusing transmission power for efficient communication. Our goal is to minimize the total data collection time by jointly optimizing the UAV's trajectory and the MA's orientation. We develop a deep reinforcement learning (DRL)based strategy using the azimuth angle and distance between the UAV and each BD to simplify the agent's observation space. To ensure stability during training, we adopt Soft Actor-Critic (SAC) algorithm that balances exploration with reward maximization for efficient and reliable learning. Simulation results demonstrate that our proposed MA-equipped UAV with SAC outperforms both FPA-equipped UAVs and other RL methods, achieving significant reductions in both data collection time and energy consumption.

The use of Unmanned Aerial Vehicles (UAVs) as aerial base stations has attracted increasing research interest in recent years. A key challenge in this field is determining how to deploy multiple UAVs in dynamic environments, particularly where mobile user demands fluctuate. To address this challenge, this paper presents an adaptive UAV deployment scheme in a dynamic multi-UAV wireless network, considering the mobility of UAVs and users, state variability, and adjustable UAV transmission power. By jointly optimizing the UAVs’ operational modes, transmission power levels, and movement strategies, our objective is to achieve a trade-off between minimizing power consumption and maximizing ground user coverage. A Deep Reinforcement Learning (DRL) approach is proposed to address these challenges. To capture the dynamic variations of users and UAVs in the environment, a multi-modal feature state space is designed, consisting of both a multi-channel image and vectors. The image component integrates real-time data on user distribution and the UAV coverage area, while the vectors represent UAV operational modes, position data, and system temporal information. These multi-modal features are processed using a combination of Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) for advanced feature extraction. To enhance training stability and efficiency, the proposed approach updates parameters using the Proximal Policy Optimization (PPO) method. Simulation results demonstrate the effectiveness of the proposed scheme in balancing power consumption and coverage while effectively managing system dynamics.

Backscatter communication (BC) enables resource-constrained backscatter devices (BDs) to communicate by reflecting signals from external radio frequency sources (RFSs), thereby avoiding active RF components, making it a cutting-edge technology for the ubiquitous Internet of Things (IoT). However, the open nature of BC makes it vulnerable to passive and active attacks, and existing methods fail to offer robust mutual authentication suitable for mobile BC systems while keeping a low computational overhead. To address this issue, we propose AuthScatter, an accurate, robust, and scalable physical-layer mutual authentication scheme between the RFS and multiple BDs by leveraging channel fading and random numbers as a one-time pad to protect the identity key exchange procedure during the authentication. Specifically, AuthScatter constructs shared identity keys as physical-layer fingerprints for efficient identification and employs a challenge-response authentication mechanism to enable secure key exchange between the RFS and the BD. In the authentication, the one-time pad effectively prevents eavesdropping, spoofing, replay, and counterfeiting attacks, while legitimate devices leverage channel reciprocity and random number knowledge to authenticate efficiently without channel estimation or complex processing. It is tailored for high-mobility scenarios by completing the exchange within the channel coherence time while incorporating a key-update mechanism to ensure sustained security in the long term. Additionally, it includes a re-authentication mechanism to enhance resistance against wireless attacks and a batch authentication framework leveraging time-division duplexing (TDD) to enable scalability in large-scale BC deployments. Comprehensive security analysis demonstrates the resistance of AuthScatter to various threats, including eavesdropping, identity spoofing, replay, and counterfeiting attacks. Extensive simulations further validate its high authentication accuracy across diverse channel conditions, robustness against various attack vectors, and scalability with a large number of BDs, highlighting its superiority over state-of-the-art schemes.

Ambient backscatter communication (AmBC) is a key enabler for green Internet of Things (IoT) employing ambient radio frequency (RF) signals for low-power communication. The AmBC devices discussed in the literature so far harvest light to power their microcontrollers for controlling RF modulation and reflection circuits. The ubiquitous presence of configurable light-emitting diodes (LEDs) based luminaires can also leverage such control functionalities by involving visible light communication (VLC). In this paper, we propose a visible light-enabled AmBC system that integrates VLC and AmBC for future ambient power-enabled IoT. We design LiBD, a backscatter device (BD) that uses visible light signals for both modulation control and energy supply. We demonstrate the real-time end-to-end data transmission with a proof of concept experiment. Evaluation results show that the LiBD supports multiple sub-6 GHz RF bands and can receive modulated light at frequencies up to 250 kHz. The investigations verify the feasibility of integrating the VLC and AmBC for future green IoT systems.

In the ambient backscatter communication (AmBC) system, a backscatter device (BD) transmits its messages to receivers by modulating and reflecting incident radio frequency (RF) signals from ambient RF emitters. Most existing studies investigate single-antenna BDs and fabricate them using the conventional printed circuit board (PCB) method. In this paper, we propose flexible inkjet-printed integrated backscatter devices (IBDs) for indoor backscatter radios. We investigate and fabricate both single-antenna and multi-antenna IBDs using the inkjet printing technique. Since the commonly used substrate polyethy-lene terephthalate (PET) for inkjet-printed electronics has highly lossy properties, the printed feeding lines in the RF become inefficient. We propose to overcome this problem by positioning the backscatter switch next to each antenna and controlling them simultaneously with the baseband backscatter signal. Attenuation of the baseband control signal in printed lines is low leading to increase in overall efficiency of the proposed system. We validate the prototypes with received backscatter signal strength measurements in an anechoic chamber. The result shows that the printed four-antenna IBD can benefit approximately 9 dB diversity gain compared with the single-antenna version.