A sensing information-assisted superimposed pilot channel estimation method is proposed in UAV orthogonal frequency division multiplexing systems that achieves rapid convergence and optimal symbol detection performance at a low pilot power ratio, effectively improving spectral efficiency in dynamic UAV communication scenarios.
Abstract
The pervasive integration of unmanned aerial vehicles (UAVs) into demanding industrial scenarios, including power line inspection and mine hoisting, poses critical challenges for next-generation wireless networks in ensuring robust connectivity and high spectral efficiency under rapidly time-varying channel conditions. Although superimposed pilot schemes offer a promising solution to improve spectral efficiency by sharing time-frequency resources, these methods inevitably introduce severe pilot-data mutual interference. This interference degrades channel estimation accuracy and symbol detection reliability, thereby threatening mission-critical UAV operations. To tackle this issue, a sensing information-assisted superimposed pilot channel estimation method is proposed in UAV orthogonal frequency division multiplexing systems. In the proposed method, high-precision kinematic parameters, including position and velocity, acquired from onboard UAV sensing receivers are exploited to derive deterministic, enhanced prior bounds in the delay and Doppler domains. Based on the sensed prior information, a two-stage delay-Doppler denoising scheme is designed to truncate data symbol interference via adaptive thresholding. Subsequently, a sensing-assisted iterative decision-directed mechanism is employed to refine the estimation accuracy. Simulation results demonstrate that the proposed method eliminates the severe error floors of conventional superimposed pilot-based channel estimation methods. Furthermore, it achieves rapid convergence and optimal symbol detection performance at a low pilot power ratio, effectively improving spectral efficiency in dynamic UAV communication scenarios.
Integrated sensing and communications (ISAC) is a key enabler for uncrewed aerial vehicles (UAVs) in the low-altitude economy. This paper proposes an ISAC waveform that embeds a unique word (UW) into orthogonal chirp division multiplexing (OCDM), termed UW-OCDM, together with corresponding communication reception and cooperative sensing schemes for high-mobility UAV scenarios. For communication, the embedded UW enables timing synchronization and Doppler estimation and compensation without requiring a separate synchronization sequence. A sparse spatio-temporal channel estimation method exploits the common channel support across multiple receive antennas and consecutive UW observations to support reliable data demodulation. For sensing, the deterministic UW serves as a shared prior that allows distributed base stations to construct sensing dictionaries locally without exchanging random payload symbols in real time. A hierarchical multi-target detection and tracking algorithm integrates direct-path interference suppression, kinematic prediction, multi-candidate screening, off-grid refinement, residual verification, and successive interference cancellation for robust localization with reduced search complexity. Simulation results demonstrate reliable communication and localization in highly dynamic UAV scenarios, while the proposed framework retains low-complexity frequency-domain equalization and reduces transmit-reference sharing overhead and multi-static localization complexity.
An unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and a joint beamforming and trajectory optimization framework is investigated and results demonstrate that the proposed algorithm significantly improves the achievable system downlink rate.
Shunxuan Wang, Qi Zhu· Italian National Conference...· 0 citations
Integrated sensing and communication (ISAC) technology, when deployed on unmanned aerial vehicles (UAVs), enables aerial base stations to simultaneously provide wireless connectivity to ground users and perform environmental sensing through echo signal analysis. However, the broadcast nature of wireless transmission, combined with the line-of-sight (LoS) propagation characteristics of UAVs, increases the risk of passive eavesdropping on transmitted signals during ISAC missions. This paper investigates the joint trajectory design and power allocation (JTDPA) problem for UAV-enabled ISAC systems in environments with multiple mobile ground users and potential eavesdroppers. The proposed approach formulates the optimization problem as a constrained Markov decision process (CMDP), aiming to balance communication rate, secrecy rate, and energy consumption. To address the limitations of existing secure trajectory designs, such as unnecessary energy expenditure and overly conservative avoidance actions, we propose a two-stage (TS) strategy that incorporates the safe twin delayed deep deterministic policy gradient (Safe-TD3) algorithm, referred to as TS-SafeTD3. In the first stage (sensing stage), the UAV navigates toward a user-centric location without communication to enhance initial coverage efficiency, while satisfying the minimum-distance safety constraints with respect to potential eavesdroppers.In the second stage (ISAC stage), Safe-TD3 is employed to jointly optimize both trajectory and power allocation under the same safety constraints to maximize the weighted secrecy rate. Simulation results indicate that the proposed algorithm improves the weighted secrecy rate and energy efficiency under various operational conditions, while maintaining a low violation probability of the safety constraints.
Yu-Jia Chen, Hai-Yan Huang, Ting-Wei Chen et al.· IEEE Transactions on Network...· 0 citations
—Unmanned aerial vehicle (UAV) swarm-assisted integrated sensing and communication (ISAC) networks are a crucial technology for providing communication and sensing services in emergency rescue scenarios without base station support. However, the strong coupling between communication and sensing resources in such networks fundamentally limits the communication and sensing performance of ISAC systems. This paper jointly optimizes spectrum allocation, UAV association and deployment to maximize average system throughput while ensuring localization accuracy in such networks, where sensing is realized through localization. We begin by deriving an analytical expression for localization accuracy, which explicitly captures the joint effects of link quality and anchor geometry under shared communication-localization spectrum resources. We then formulate average system throughput maximization as a mixed-integer nonlinear and non-convex optimization problem with the constraints of localization accuracy, sub-channels, UAV association, UAV deployment and signal-to-interference-plus-noise ratio. We further develop an alternating iterative optimization method to solve this complex optimization problem. Within this method, a particle swarm optimization-based method is developed to jointly optimize spectrum allocation and UAV association, and a dueling double deep Q-network-based method is further employed for UAV deployment optimization. Finally, extensive simulation results are presented to validate the efficiency of our optimization method, and also to illustrate how key parameters influence average system throughput and localization accuracy.
Zhuo-Jia Yang, Wei Su, Bin Yang et al.· IEEE Transactions on Mobile...· 0 citations
This paper investigates the outage performance of unmanned aerial vehicle (UAV)-assisted free-space optical (FSO) communication systems operating in foggy channels. The analysis jointly accounts for atmospheric turbulence, fog-induced attenuation, generalized Beckmann-distributed pointing errors (PE), and angle-of-arrival (AoA) fluctuations caused by UAV orientation jitter. A comprehensive statistical channel model is developed, from which closed-form expressions for outage probability and an analytical characterization of outage capacity are derived. The effects of receiver field-of-view (FoV) limitations and multi-UAV amplify-and-forward relaying on link reliability are also investigated. Numerical results validate the proposed analysis and reveal that AoA fluctuations impose a fundamental performance limit, resulting in an outage floor, particularly under dense fog and narrow-FoV conditions. These findings provide practical design insights for improving the reliability of UAV-assisted FSO communication systems in adverse weather environments.
Mahdi Ataee, S. Sadough, Khadijeh Ali Mahmoodi· EAI Endorsed Transactions on...· 0 citations
—This paper studies covert communications in the presence of an unmanned aerial vehicle (UAV) acting as an aerial warden, where a terrestrial transmitter communicates with a legitimate terrestrial receiver under ground interference. To enhance transmission reliability, the legitimate receiver employs a fluid antenna (FA) with port selection based on the maximum signal-to-interference-plus-noise ratio (SINR). The UAV adopts an energy-detection-based hypothesis test to detect the covert transmission. Closed-form expressions for the false alarm and missed detection probabilities are derived, based on which the covertness outage probability (COP) is characterized. To tackle the analytical challenges introduced by spatially correlated FA channels, an eigenvalue-based weighted approximation is adopted to evaluate the outage probability at the legitimate receiver. Based on the derived outage and missed detection probability expressions, the success probability of covert communications is further characterized. Numerical results demonstrate that the proposed FA-assisted scheme significantly improves covert communications, and the eigenvalue-based weighted approximation remains highly accurate under various system parameter settings.
Jing-Wei Yao, Hui Zhao, Rui Zhang et al.· IEEE Transactions on Vehicul...· 0 citations
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