Falls remain a major cause of injury and death among older adults, which shows the need for reliable and non-intrusive monitoring solutions in healthcare environments. In this paper, we propose a novel Ambient Internet of Things (IoT) backscatter sensing system that utilizes a dense array of passive tags and a minimal number of reader antennas for cost-effective fall detection and localization. To fully exploit the spatial and temporal characteristics of ambient backscatter sensing data, we design a hierarchical multi-task spatio-temporal graph attention network (HM-STGAT), which jointly models the spatial relationships among tags and antennas as well as the temporal dynamics of human activities. The proposed unified framework simultaneously detects fall events and accurately estimates fall locations. We validate the proposed approach through a real-world experiment to collect a diverse dataset of fall and non-fall scenarios. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in both fall detection accuracy and localization precision, highlighting its potential for practical deployment in healthcare monitoring applications.

In this paper, we propose a radio frequency identification (RFID)–based integrated sensing and communication (ISAC) system that uses the variations in received signal strength (RSS) from a passive tag grid for both robust communication and healthcare monitoring. Our approach utilizes the same RSS fluctuations for dual purposes: human activity triggered key generation and fall detection. Specifically, we conducted a hardware experiment to collect real-time RSS data from the tag grid and proposed a dynamic threshold adjustment-based physical layer key generation algorithm that guarantees robust and secure communication activated by human motion. For the healthcare sensing, we developed a non-wearable-based fall detection system by detecting the sudden RSS variations. We also generated heatmaps to visualize real-time alerts and fall location. By unifying these two functions, our system not only demonstrates the practicality and efficiency of using RFID as an ISAC platform, but also overcomes the traditional trade-off between communication and sensing.

This paper proposes a spectrum-efficient non-orthogonal affine frequency division multiplexing (AFDM) waveform for reliable high-mobility communications in the upcoming sixth-generation (6G) mobile systems. Our core idea is to introduce a compression factor to enable controllable subcarrier overlapping in chirp-based AFDM modulation. To mitigate inter-carrier interference (ICI), we introduce linear precoding at the transmitter and an iterative detection scheme at the receiver. Simulation results demonstrate that these techniques can effectively reduce interference and maintain robust bit error rate (BER) performance even under aggressive compression factors and high-mobility channel conditions. The proposed non-orthogonal AFDM waveform offers a promising solution for next-generation wireless networks, balancing spectrum efficiency and Doppler resilience in highly dynamic environments.

The unmanned aerial vehicle assisted multi-access edge computing (UAV-MEC) technology has been widely applied in the sixth-generation era. However, due to the limitations of energy and computing resources in disaster areas, how to efficiently offload the tasks of damaged user equipments (UEs) to UAVs is a key issue. In this work, we consider a multiple UAVMECs assisted task offloading scenario, which is deployed inside the three-dimensional corridors and provide computation services for UEs. In detail, a ground UAV controller acts as the central decision-making unit for deploying the UAV-MECs and allocates the computational resources. Then, we model the relationship between the UAV controller and UEs based on the Stackelberg game. The problem is formulated to maximize the utility of both the UAV controller and UEs. To tackle the problem, we design a K-means based UAV localization and availability response mechanism to pre-deploy the UAV-MECs. Then, a chess-like particle swarm optimization probability based strategy selection learning optimization algorithm is proposed to deal with the resource allocation. Finally, extensive simulation results verify that the proposed scheme can significantly improve the utility of the UAV controller and UEs in various scenarios compared with baseline schemes.

The unmanned aerial vehicle (UAV) network has gained significant attentions in recent years due to its various applications. However, the traffic security becomes the key threatening public safety issue in an emergency rescue system due to the increasing vulnerability of UAVs to cyber attacks in environments with high heterogeneities. Hence, in this paper, we propose a novel anomaly traffic detection architecture for UAV networks based on the software-defined networking (SDN) framework and blockchain technology. Specifically, SDN separates the control and data plane to enhance the network manageability and security. Meanwhile, the blockchain provides decentralized identity authentication and data security records. Beisdes, a complete security architecture requires an effective mechanism to detect the time-series based abnormal traffic. Thus, an integrated algorithm combining convolutional neural networks (CNNs) and Transformer (CNN+Transformer) for anomaly traffic detection is developed, which is called CTranATD. Finally, the simulation results show that the proposed CTranATD algorithm is effective and outperforms the individual CNN, Transformer, and LSTM algorithms for detecting anomaly traffic.

Future wireless networks targeted for improving spectral and energy efficiency, are expected to simultaneously provide sensing functionality and support low-power communications. This paper proposes a novel net-zero integrated sensing and communication (ISAC) model for backscatter systems, including an access point (AP), a net-zero device, and a user receiver. We fully utilize the backscatter mechanism for sensing and communication without additional power consumption and signal processing in the hardware device, which reduces the system complexity and makes it feasible for practical applications. To further optimize the system performance, we design a novel signal frame structure for the ISAC model that effectively mitigates communication interference at the transmitter, tag, and receiver. Additionally, we employ distributed antennas for sensing which can be placed flexibly to capture a wider range of signals from diverse angles and distances, thereby improving the accuracy of sensing. We derive theoretical expressions for the symbol error rate (SER) and tag’s location detection probability, and provide a detailed analysis of how the system parameters, such as transmit power and tag’s reflection coefficient, affect the system performance.

In this paper, we study the fast coverage problem of 3D irregular terrain surfaces with a hierarchical UAV swarm. We first build a 3D model of a random irregular terrain and project the 3D terrain surface into many weighted 2D patches. Then we develop a two-level hierarchical UAV swarm architecture, including the low-level follower UAVs (FUAVs) and the high-level leader UAVs (LUAVs). For FUAVs, we adopt the traditional coverage trajectory algorithm to carry out specific coverage tasks within patches based on the star communication topology. For LUAVs, we propose a swarm deep Q-learning (SDQN) reinforcement learning algorithm to select patches. The numerical results show that the total coverage time of the SDQN is less than that of existing methods, which demonstrates the effectiveness of the proposed algorithm.

The emerging backscatter communication technology is recognized as a promising solution to the battery problem of Internet of Things (IoT) devices. For example, the wireless sensor network with backscatter communication technology can monitor the environment in remote areas without battery maintenance or replacement. Unfortunately, the transmission range of backscatter communication is limited. To tackle this challenge, we propose a multi-UAV-aided data collection scenario where the unmanned aerial vehicle (UAV) can fly close to the backscatter sensor node (BSN) to activate it and then collects the data. We aim to minimize the total flight time of the rechargeable UAVs when the collection mission is finished. During the data collection process, the UAVs can return to the charging station to recharge itself when the energy of UAV is not sufficient to complete the mission. To reduce the complexity of the task, we first use the Gaussian mixture model clustering method to divide the BSNs into multiple clusters. Then we consider the deterministic boundary and ambiguous boundary for the UAV flying regions, respectively. For the deterministic boundary scenario, we propose a single-agent deep option learning (SADOL) algorithm, where each UAV cannot fly beyond the deterministic boundary. For the ambiguous boundary scenario, we propose a multiagent deep option learning (MADOL) algorithm to enable the UAVs to cooperatively learn the ambiguous BSNs assignment. In the simulation, we compare the proposed algorithms with multiagent deep deterministic policy gradient (MADDPG), deep deterministic policy gradient (DDPG), and deep Q-network (DQN) algorithms, which proves the proposed algorithms can achieve better performance.

Unmanned aerial vehicle (UAV) technology is recognized as a promising solution to area coverage problems (ACPs) and has been extensively studied recently. In this paper, we study the 3D irregular terrain surface coverage problem with a hierarchical UAV swarm. We first build the 3D model of a random irregular terrain and propose a geometric way to project the 3D terrain surface into many weighted 2D patches. Then we develop a two-level hierarchical UAV swarm architecture, including the low-level follower UAVs (FUAVs) and the high-level leader UAVs (LUAVs). For FUAVs, we design a coverage trajectory algorithm to carry out specific coverage tasks within patches based on the star communication topology. For LUAVs, we propose a swarm deep Q-learning (SDQN) reinforcement learning algorithm to select patches. Moreover, an observation history model based on convolutional neural networks (CNNs) and the mean embedding method is integrated into SDQN to address the communication limitation problems of LUAVs. The numerical results show that FUAVs can cover the entire area of each patch with little redundancies, and the total coverage time of the SDQN is less than that of existing methods, which demonstrates the effectiveness of the proposed algorithms.

Implementing wireless body area networks (WBANs) is very challenging, due to limited power supply, inadequate computation capability, and imperfect channel state information (CSI). In this paper, we propose a hybrid offloading scheme with backscatter communication (BackCom) under imperfect CSI, where each sensor firstly receives radio frequency (RF) energy and then offloads body data task via low-power BackCom to the access point (AP) for edge computing. Aiming to minimize the end-to-end system latency, we jointly optimize the computation speed of AP for processing computation tasks, the power of the signal transmitted by the AP, and the power reflection coefficient under energy and data rate chance constraints. To solve the proposed distributionally robust chance-constrained optimization problem, we approximate chance constraints by the Bernstein-type-inequality (BTI) method and Conditional value-at-risk (CVaR) method in the Gaussian distribution and arbitrary distribution of channel estimation errors, respectively. To tackle the NP-hard problem efficiently, the original problem can be decomposed into two subproblems, which are solved by successive linear programming and iterative algorithm, respectively. Simulation results show that the CVaR method outperforms the other methods for the non-Gaussian CSI mismatch, and the Bernstein method is more suitable for the Gaussian distribution of CSI errors.

In this paper, we propose a distributionally robust chance-constrained design for the backscatter communication-aided computation offloading scheme in wireless body area networks (WBANs), where each sensor firstly receives radio frequency (RF) energy and then offloads body physiological computation tasks via low-power BackCom to the access point (AP) for edge computing. Specifically, only rough first and second-order moment statistics are obtained for the estimation errors of CSI. Based on all the possible distributions of CSI errors, we aim to minimize the end-to-end system latency by jointly optimizing the power of the signal transmitted by the AP and the power reflection coefficient with energy chance restrictions and throughput requirement constraints. In order to solve the proposed non-convex chance-constrained optimization problem, we approximate chance constraints by the conditional value-at-risk (CVaR), and apply an efficient block coordinate descent (BCD) algorithm to solve it. Simulation results are provided to corroborate that the proposed method outperforms other methods for the non-Gaussian mismatch.

In this paper, we investigate a multi-unmanned aerial vehicle (UAV) cooperation mechanism for secure communications, where the UAV transmitter moves around to serve the multiple ground users (GUs) while the UAV jammers send the 3D jamming signals to the ground eavesdroppers (GEs) to protect the UAV transmitter from being wiretapped. The 3D jamming guarantees the GEs not being interfered by the jamming signals. It is challenging to make a joint trajectory design and power control for a UAV team without central control. To this end, we propose a multi-agent deep reinforcement learning approach to achieve the maximum sum secure rate by designing the dynamic trajectory of each UAV. The proposed multi-agent deep deterministic policy gradient (MADDPG) technique is centralized training at high altitude platforms (HAPs) and distributed execution at each UAV, which enables the fully distributed cooperation among UAVs. Finally, the simulation results show the proposed method can efficiently solve the multi-UAV cooperation trajectory design problem in secure communication scenarios.

Backscatter communications have been considered as one of the key technologies in the Internet of Things (IoT) applications. In this paper, we consider a multitag ambient backscatter system, where the multiple tags can harvest radio frequency (RF) energy from the power station and backscatter the RF signals to the reader for data transmission. In order to guarantee the throughput requirements, we aim to maximize the minimum user rate among all the tags by jointly optimizing the backscatter time allocation and power reflection coefficient. Channel state information (CSI) mismatch is taken into account in our proposed optimization problem, which leads to a robust chance-constrained optimization problem. To deal with the nonconvex chance constraints, we propose two safe approximation methods: 1) Bernstein-type-in-equality and 2) conditional value-at-risk (CVaR), applying to the Gaussian distribution and arbitrary distribution of channel estimation errors, respectively. In addition, we develop an alternating optimization algorithm to obtain the optimal value of minimum throughput. Finally, simulation results reveal that the CVaR-based method outperforms the Bernstein-type-inequality-based method for the non-Gaussian channel estimation error.

In this paper, we consider a radio-frequency identification (RFID) backscatter system with two scenarios: single input multiple output (SIMO) and multiple input single output (MISO). We investigate the impact of channel correlation between the forward and backscatter links on the symbol error rate (SER), and we compare the performance of SIMO and MISO RFID systems with maximum ratio combining reception over arbitrarily correlated Nakagami-m fading channels. In order to gain an insight for the system design, closed-form expressions for the asymptotic SER and an upper bound are derived for M-ary phase-shift keying and quadrature amplitude modulation. The diversity order in the fully correlated cases, revealed from these expressions, is half of that in the partially correlated and uncorrelated cases. Finally, simulations are performed to validate the theoretical analysis.

Radio frequency identification (RFID) backscatter communication systems play an important part in the future Internet of Things (IoT) applications. In this paper, we consider a multiple input single output (MISO) backscatter system, where the reader is equipped with multiple antennas and the tag is equipped with single antenna. The impact of the channel correlation between the forward and backscatter links on the symbol error rate (SER) is investigated. Then, we derive expressions for asymptotic SER and an upper bound, assuming M-ary phase-shift keying (M-PSK) and quadrature amplitude modulation (M-QAM) with maximum ratio combining (MRC) reception, over arbitrarily correlated Nakagami-m fading channels. From these expressions, we can directly obtain the diversity order. Finally, numerical results and simulations are provided to demonstrate the accuracy of the theoretical expressions.

In this paper, a multi-tag selection combining (SC) scheme is proposed in a radio frequency identification (RFID) backscatter communication system which contains a reader and L tags. The proposed scheme could efficiently combat the double-fading channel in RFID system since the diversity of multiple tags is utilized. Different from the conventional one-way communication system, the forward link channel and backscatter link channel could be correlated in backscatter communication system. Hence, we investigate the system performance under fully correlated and partially correlated Nakagami-m fading channels. The closed-form analytical outage probabilities for the two correlation circumstances are derived. Furthermore, the asymptotic outage probability is derived in a high signal-to- noise ratio (SNR) range, from which the insight on how the channel and system parameters affect the outage performance is gained. Finally, the simulation results are presented to verify the theoretical analysis.

This paper studies the energy efficient optimization for full duplex (FD) assisted closed-loop downlink transmission, where a FD base station (BS) serves multiple half duplex (HD) users in a time division multiple access manner. We optimize the durations of uplink training and downlink transmission, aimed at maximizing the energy efficiency (EE) of the system. We first show that the EE-optimal transmission scheme must use one of the two strategies: transmitting in HD mode or transmitting downlink data over all time slots so that the BS operates in FD mode during the whole uplink training phase. We then derive an approximate average net data rate of the system considering the bidirectional interference between uplink and downlink users in FD mode. Based on the result, a closed-form expression of the EE is obtained. We prove that the EE in FD mode is quasi-concave with respect to the duration of uplink training, with which the optimal durations of uplink training and downlink transmission are obtained. Simulation results show the evident gain of the proposed scheme over existing FD and HD schemes.