This correspondence paper studies a network integrated sensing and communication (ISAC) system that unifies the interference channel for communication and distributed radar sensing. In this system, a set of distributed ISAC transmitters send individual messages to their respective communication users (CUs), and at the same time cooperate with multiple sensing receivers to estimate the location of one target. We exploit the coordinated power control among ISAC transmitters to minimize their total transmit power while ensuring the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs and the maximum Cramér-Rao lower bound (CRLB) requirement for target location estimation. Although the formulated coordinated power control problem is non-convex and difficult to solve in general, we propose two efficient algorithms to obtain high-quality solutions based on the semi-definite relaxation (SDR) and CRLB approximation, respectively. Numerical results show that the proposed designs achieve substantial performance gains in terms of power reduction, as compared to the benchmark with a heuristic separate communication-sensing design.

This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help the base station (BS) provide ISAC services at separate line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive uniform linear array (ULA) IRSs for sensing, in which each IRS is integrated with dedicated sensors for processing echo signals, and each IRS simultaneously serves one sensing target and multiple communication users (CUs) in its coverage area. We consider two cases with point and extended targets, in which each IRS aims to estimate the target direction-of-arrival (DoA) and the complete target response matrix, respectively. Under this setup, we first derive the closed-form Cramér-Rao bounds (CRBs) for parameter estimation under the two target models. Then, we assume that the BS sends combined information and dedicated sensing signals for ISAC, and accordingly consider two different types of CU receivers that can and cannot cancel the interference from dedicated sensing signals. Under this setup, we minimize the maximum CRB at all IRSs, via jointly optimizing the transmit beamformers at the BS and the reflective beamformers at the multiple IRSs, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs, the maximum transmit power constraint at the BS, and the unit-modulus constraints at the multiple IRSs. To tackle the highly non-convex SINR-constrained max-CRB minimization problems, we propose efficient algorithms based on alternating optimization and semi-definite relaxation, to obtain converged solutions. Finally, numerical results are provided to verify the benefits of our proposed designs over various benchmark schemes based on separate or heuristic beamforming designs.

Object detection in unmanned aerial vehicle (UAV) images has become a popular research topic in recent years. However, UAV images are captured from high altitudes with a large proportion of small objects and dense object regions, posing a significant challenge to small object detection. To solve this issue, we propose an efficient YOLOv7-UAV algorithm in which a low-level prediction head (P2) is added to detect small objects from the shallow feature map, and a deep-level prediction head (P5) is removed to reduce the effect of excessive down-sampling. Furthermore, we modify the bidirectional feature pyramid network (BiFPN) structure with a weighted cross-level connection to enhance the fusion effectiveness of multi-scale feature maps in UAV images. To mitigate the mismatch between the prediction box and ground-truth box, the SCYLLA-IoU (SIoU) function is employed in the regression loss to accelerate the training convergence process. Moreover, the proposed YOLOv7-UAV algorithm has been quantified and compiled in the Vitis-AI development environment and validated in terms of power consumption and hardware resources on the FPGA platform. The experiments show that the resource consumption of YOLOv7-UAV is reduced by 28%, the mAP is improved by 3.9% compared to YOLOv7, and the FPGA implementation improves the energy efficiency by 12 times compared to the GPU.

This letter studies an unmanned aerial vehicle (UAV)-enabled two-user interference channel, in which two ground users send individual messages to their respective UAVs in the sky. By considering probabilistic line-of-sight (LoS) channel models, we jointly optimize the two UAVs' three-dimensional (3D) positioning locations and the two users' power control, to maximize their sum-rate throughput. Although this problem is non-convex and difficult to solve in general, we obtain the optimal solution by decoupling the positioning optimization and power control. The optimal solution shows that if the signal-to-noise-ratio (SNR) ρ is high, then the two users should take turns to transmit at the maximum power in an on-off manner and each UAV is positioned exactly above the associated user. By contrast, if ρ is low and/or the two users' distance $D$ is large, then the two users should transmit simultaneously at the maximum power, and each UAV should be positioned away from the two users for interference mitigation.

This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully- and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze both the sensing signal-to-noise ratio (SNR) and the Cramér-Rao bound (CRB) for estimating the target’s direction-of-arrival (DoA) with joint transmit and reflective beamforming optimization. First, we characterize the maximum sensing SNR when the BS-IRS channel follows line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elements N equipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally to N² and N⁴ for the semi- and fully-passive IRSs, respectively. Then, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. It is shown that when N grows, the minimum CRB decreases inversely proportionally to N⁴ and N⁶ for the semi- and fully-passive IRS, respectively. Finally, numerical results are presented to corroborate our analysis across general channel conditions. It is shown that the fully-passive IRS outperforms the semi-passive counterpart when N exceeds a certain threshold due to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss.

Dynamic load identification plays an important role in the field of fault diagnosis and structural modification design for aircraft. In conventional dynamic load identification approaches, accurate structural modeling is usually needed, which is difficult to obtain for highly nonlinear or unknown structures. In this paper, a one-dimensional convolution neural network with multiple modules is proposed for random dynamic load identification of aircraft. Firstly, the convolution module is designed for temporal feature extraction. Secondly, the extracted features are linearly weighted based on the contributions to the final output. The contributions are learned in a data driven manner via the designed attention module. Lastly, the dynamic load of a certain time stamp is predicted from the learned and weighted features. The proposed model is trained and tested using the real data from a GARTEUR aircraft model. Extensive experimental results with qualitative and quantitative evaluations have demonstrated the identification performance with satisfactory accuracy of the proposed approach under different strengths of load noises.