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.

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.

Orthogonal time frequency space (OTFS) modulation has been considered as one of the most promising candidates to support reliable data transmission especially in high-mobility networks, wherein the performance of communications strongly relies on timely and accurate tracking of the relevant user state parameters. In this context, the problem of integrated sensing and communication (ISAC) assisted user state refinement is addressed in the framework of OTFS systems. In particular, by exploiting the initial yet coarse angle estimate provided by the typical codebook-based user state sensing algorithm, we judiciously design a hybrid digital-analog architecture to output the nested array structured low dimensional observations. In this way, the corresponding nested array based technique is employed to perform angle refinement by fully utilizing the degrees of freedom provided by the measurements. Next, based on the refined angle estimate, we develop a two-stage joint delay and Doppler shifts estimation scheme to update the corresponding coarse estimates. Numerical results validate the effectiveness of the proposed algorithm in various scenarios, showing that our well designed user state refinement scheme is able to improve the performance of the considered ISAC-assisted OTFS systems in term of both radar and communication metrics.

This letter addresses the energy storage maximization issue in the typical wireless networks aided by reconfigurable intelligent surface (RIS), where the joint simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) are further exploited in a collaborative manner. In particular, by taking energy harvesting and task offloading into account simultaneously, the RIS reflection coefficients and task allocation strategy are optimized jointly subject to quality of service (QoS) constraints. To tackle the non-convex problem, a practical block coordinate descent (BCD) based alternating optimization algorithm is proposed to obtain a near-optimal solution. Numerical results verify that the proposed RIS-aided joint-SWIPT-MEC scheme outperforms other algorithms.

Nowadays, unmanned aerial vehicles (UAVs), empowered with the capability of high-definition image transmission, are used to capture the rapidly changing physical environment by leveraging its high flexibility to reconstruct an immersive realistic virtual environment for metaverse users. In this paper, we consider a novel UAV-assisted image acquisition system where a UAV is dispatched to take off from an initial location to capture real-time images of multiple ground targets and then transfer the captured images back to the ground user for virtual environment reconstruction. We aim to minimize the time for the UAV to complete the image acquisition task by optimizing the three-dimensional UAV trajectory under the constraints of image quality, information causality and energy consumption. To this end, we first formulate the investigated scenario into a mixed integer optimization problem, which, however, is difficult to solve due to the infinite time-varying variables closely coupled with each other. Then, a three-stage progressive algorithm is proposed to obtain an efficient solution to the formulated mixed integer optimization problem, where the constraints of image quality, information causality and energy consumption can be sequentially satisfied. Finally, comprehensive performance evaluation is conducted to verify the effectiveness of the proposed three-stage progressive trajectory design algorithm, and the results show that the proposed algorithm significantly outperforms the benchmark schemes.