2026· IEEE Transactions on Green Communications and Networking· Vol 10, pp. 3582-3595· 0 citations· 39 references
Computer Science
Abstract
To meet the collaborative requirements of high-precision communication, sensing, and navigation for unmanned aerial vehicles (UAVs) and other aerial mobile terminals operating in urban low-altitude airspace, this paper proposes an integrated communication-sensing-navigation (ICSN) beamforming design tailored to low-altitude scenarios. In the developed ICSN system model, communication performance is evaluated by the total communication rate of users, while sensing performance is quantified by the Cramér–Rao Bound (CRB) of target angle estimation, enabling accurate positioning and continuous perception for aerial users. A two-stage beamforming strategy is adopted: in the first stage, a wide beam is constructed under integrated sidelobe to mainlobe ratio (ISMR) constraints to sense potential target directions; in the second stage, a weighted optimization problem is formulated to jointly enhance communication rate and angle estimation accuracy, subject to user signal-to-interference-plus-noise ratio (SINR) requirements and mainlobe/sidelobe constraints. To efficiently solve the resulting non-convex problem, this paper proposes an algorithmic framework that combines fractional programming (FP) and alternating optimization (AO), fully utilizing the perception results from the previous round to perform feedback updates for the next round of beam design. Simulation results verify the effectiveness of the proposed method in multi-user and multi-target urban low-altitude scenarios, demonstrating the role of the first-stage directional beam design in system robustness and accuracy. Moreover, the proposed framework facilitates spectrum and hardware reuse across functionalities, thereby contributing to the design of sustainable and energy-efficient aerial communication systems aligned with the principles of green communication.
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
Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.
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.
To address the challenges of inter-cell interference/reflection and static clutter, a clutter-aware waveform design for a multi-cell multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system is proposed. Various levels of coordination among base stations (BSs) are investigated to enhance target detectability in cluttered environments while maintaining the quality of service (QoS) for communication users. Specifically, two coordination schemes are investigated: 1) coordinated beamforming (CBF), where only channel state information (CSI) is shared, and 2) coordinated multipoint (CoMP), where both CSI and user data are exchanged among BSs. The waveform design problem is formulated as a non-convex optimization that maximizes the radar output signal-to-clutter-plus-interference-plus-noise ratio (SCINR), subject to robust symbol-level QoS and constant-modulus power constraints to ensure uncertainty in shared and estimated CSI. To tackle this problem, the single-ratio SCINR objective is decoupled via Dinkelbach’s transform (or a quadratic transform for multiple-ratio objectives) and reformulated on a Riemannian manifold to accommodate the constant-modulus constraint guarantees a practical peak-to-average-power ratio (PAPR). The resulting problem is further converted into an unconstrained form using the augmented Lagrangian method (ALM) and solved through a Riemannian conjugate gradient (RCG) algorithm. Simulation results demonstrate that the proposed designs achieve superior radar and communication performance compared to baseline schemes that underestimate the effects of multi-cell deployment.
Yves Fidele Aikoun, Gordon Owusu Boateng, Zhao-Jie Wang et al.· IEEE Transactions on Wireles...· 0 citations
In integrated sensing and communication (ISAC) systems, stringent sensing performance constraints can severely limit the power available for communication. Hybrid reconfigurable intelligent surfaces (HRISs) with capabilities of both passive reflection and active signal amplification can significantly improve communication performance in the power-limited regime. This motivates us to analyze and optimize the performance of an HRIS-aided multiple-input-multiple-output (mMIMO) ISAC system. We first estimate the effective uplink/downlink channels using the minimum mean square error method. We then derive closed-form expressions for the communication sum-rate and sensing Cram\'er-Rao lower bound (CRLB). It is shown that under the equal power allocation strategy, the CRLB remains independent of the HRIS coefficients. Then, we formulate a joint optimization problem of power allocation and HRIS beamforming to maximize the communication sum-rate while ensuring specified sensing CRLB constraints. To solve the formulated non-convex problem, we propose an alternating optimization algorithm based on fractional programming and successive convex approximation. Extensive simulations validate our analysis and proposed algorithm, showing significant improvements in both communication and sensing performances enabled by the HRIS. For example, an HRIS with only $4$ active elements offers $97.30\%$ improvement in the communication sum-rate, while ensuring a sensing CRLB constraint of $-30$ dB.
Smriti Uniyal, Tianyu Fang, Marco Di Renzo et al.· 0 citations
The safety development requirements of low-altitude economy (LAE) renders the robust low-altitude airspace monitoring critical important than ever before. Integrated sensing and communication (ISAC) as one of the key development trends of 6G provides potential solutions for the LAE. However, conventional antenna arrays suffer from limited three-dimensional (3D) sensing coverage and degraded angular resolution at high elevation angles. To address these challenges, this paper investigates low-altitude ISAC systems enabled by the recently proposed spherical directly-connected antenna array (DCAA). By carefully deploying multiple simple uniform planar arrays (sUPAs) over a spherical surface, without relying on any phase shifter, spherical DCAA enjoys advantages of full 3D coverage, superior and uniform angular resolution, enhanced energy-focusing and low hardware cost. In this paper, we first characterizes the sensing performance of the spherical DCAA, in terms of the sensing signal-to-noise ratio (SNR), area average probability of detection, and Cram\'er-Rao lower bounds (CRLBs) for both elevation and azimuth angle estimation. Then, a low-altitude ISAC optimization problem is formulated to maximize the worst-case sensing SNR over a prescribed aerial region while satisfying the communication quality-of-service requirements of ground users. To effective solve this mixed-integer non-convex problem, we develop a novel greed-based joint array selection and beamforming optimization framework. Simulation results demonstrate that spherical DCAA significantly outperforms conventional UPA in terms of sensing coverage, angle estimation accuracy, and communication-sensing SNR tradeoff, highlighting its potential for future low-altitude ISAC systems.
Zhiqiang Xiao, Tao Zhang, Zhenjun Dong et al.· 0 citations