The traditional denoising algorithm can reduce the noise from environment, yet an evident downside of this is the sharp decrease of efficacy under non-stationary noise with low signal to noise ratio (SNR). In this cas...
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In this paper, an algorithm based on quality of service (QoS) framework is proposed to address the multitask scheduling problem in complex collaborative multi-radar scenarios. The task scheduling problem is described ...
In this paper, an algorithm based on quality of service (QoS) framework is proposed to address the multitask scheduling problem in complex collaborative multi-radar scenarios. The task scheduling problem is described as an NP-hard mixed integer optimization problem in terms of task quality and task efficiency. To efficiently tackle the characteristic of the optimization problem, we proposed a two-step decoupling algorithm based on convex relaxation focusing on optimizing overall performance. Then, two comparison algorithms are proposed in this paper to benchmark against the proposed task scheduling algorithm. By contrasting these algorithms with the proposed approach, the study assesses the effectiveness and superiority of the new method in optimizing task utility and addressing the challenges of multi-task scheduling in multi-radar cooperative surveillance scenarios.
Completing networks is often a necessary step when dealing with problems arising from applications in incomplete network data mining. This paper investigates the network completion problem with node attributes. We pro...
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To reduce the burden of transmission delay on the fronthaul link, this paper investigates an energy-efficient content caching, access point (AP) clustering and digital-to-analog converter resolution selection strategy...
To reduce the burden of transmission delay on the fronthaul link, this paper investigates an energy-efficient content caching, access point (AP) clustering and digital-to-analog converter resolution selection strategy in cell-free massive multiple-input multiple-output (CF-mMIMO) network with cache enabled are investigated. Specifically, we first give the signaling transmission model, cache model and power model. Furthermore, we formulated an optimization problem for maximizing the energy-efficiency of the considered system by optimizing the content caching, AP clustering and ADC/DAC resolution. Finally, a reinforcement learning method-a deep deterministic policy gradient algorithm is adopted to acquire the optimal solution.
The Fast-Imaging Formula (FIF) enables rapid radar image simulation through One-Shot ray-tracing. However, the classical Imaging Formula is approximate results under the conditions of small angle and bandwidth. The ap...
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
The Fast-Imaging Formula (FIF) enables rapid radar image simulation through One-Shot ray-tracing. However, the classical Imaging Formula is approximate results under the conditions of small angle and bandwidth. The application of FIF to wideband high-resolution radar images is subject to discussion. This study constructs simulations by employing the '6+6' Degrees of Freedom (DoF) motion model, determining the imaging plane. Utilizing the Discrete Fourier Transform (DFT), the range profile formula for large bandwidth is obtained, and the result is frequency dependent. According to the principle of spectrum correction, the integration is modified to obtain the image primitive suitable for high-resolution image simulation. The simulation results of the ship target model and the range profile analysis of the ray tube verify the effectiveness of the proposed method.
The bottom bounce mode plays a crucial role in underwater acoustic detection in the shadow zone of deep water. When both the source and hydrophone are situated at depths of 300 meters or shallower, the main contributi...
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ISBN:
(数字)9798350362077
ISBN:
(纸本)9798350362084
The bottom bounce mode plays a crucial role in underwater acoustic detection in the shadow zone of deep water. When both the source and hydrophone are situated at depths of 300 meters or shallower, the main contributions to the sound field come from four multipath rays that reflected by the bottom only once. The sound field in the shadow zone mostly consists of larger- angle rays. Under such conditions, when the bottom bounce sound is received by a vertical line array, the lack of azimuthal resolution prevents obtaining source azimuth information. When the bottom bounce sound is received by a horizontal line array, an azimuth deviation appears because of the coupling effect between elevation angle and azimuth angle. This paper explains the reasons for the deviation. Then based on the trigonometric relation between the elevation angle and the azimuth angle, the deviation could be modified by the tilted array. The method is demonstrated with the simulated result.
Low illumination and various colour casts are present in images taken in sand-dust weather. A deep learning-based or prior-based desanding approach may not produce the best results. This work proposes an efficient joi...
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Target depth estimation in deep sea is an important topic in underwater acoustic signalprocessing. In recent years, researchers [1]–[3] proposed some methods of estimating depth of underwater sources for passive aco...
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ISBN:
(数字)9798350362077
ISBN:
(纸本)9798350362084
Target depth estimation in deep sea is an important topic in underwater acoustic signalprocessing. In recent years, researchers [1]–[3] proposed some methods of estimating depth of underwater sources for passive acoustic systems with vertical line arrays. These vertical arrays are located at great depth at the point where the sound speed equals the maximum speed near the surface, which makes use of the deep-sea propagation characteristics of reliable acoustic path. However, using active sonars located at the great depth to estimate near-surface target depth has rarely been investigated. This paper focuses on target depth estimation of active sonars with a single hydrophone or horizontal line array at the great depth.
In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks under multipath propagation. To fully utilize the received signal, we decom...
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Combining cell-free massive multiple-input multiple-output (CF-mMIMO) and mobile edge computing (MEC) facilitates the processing of compute-intensive and latency-sensitive tasks in the distributed IoT. For MEC-enabled...
Combining cell-free massive multiple-input multiple-output (CF-mMIMO) and mobile edge computing (MEC) facilitates the processing of compute-intensive and latency-sensitive tasks in the distributed IoT. For MEC-enabled CF-mMIMO system, this paper designs a task offloading strategy for local computing and multi-access points (APs) collaboration. Under energy constraints, we aim to minimize the latency of computing offloading. According to the different data size of each user and the service of APs, the graph neural network method is adopted to deal with the link prediction between the user and APs.
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