Search Results (59)

The identification of mixed monopole and dipole sound sources under highly randomized acoustic environments is of interest in many industrial applications. The DAMAS–MS method is one of the few methods that has been explicitly developed to address this problem. However, it suffers from a critical constraint in that it consistently exhibits limited accuracy in identifying monopole sources, which leads to their underestimation in the final results. To overcome this constraint, this paper proposed a novel identification framework that integrates vision transformer (ViT) with beamforming techniques. The framework leverages preliminary beamforming results to construct input features by extracting the real and imaginary components of the cross-spectral matrix at target frequencies and incorporating spatial position encodings derived from estimated source locations. To ensure adaptability to varying source densities, multiple ViT sub-models are trained on representative scenarios. This strategy enables effective generalization across the target range and supports multi-label identification of monopole and dipole sources with varied configurations. Furthermore, anechoic chamber experiments with synthesized monopole and dipole emitters validate the method’s stability under single-frequency excitation. Compared to the DAMAS–MS method, the proposed method achieves improved identification accuracy for monopole sources, while maintaining comparable performance in dipole source identification, underscoring its potential for practical applications. Full article

In a cellular maritime communication system, ocean buoys are essential to enable environmental monitoring, offshore platform management, and disaster response. Therefore, energy-efficient transmission from the buoys is a key requirement to prolong their operational time. A fixed uplink beamforming can be considered to save energy by leveraging its beam gain while managing the target link reliability. However, the dynamic condition of ocean waves causes buoys’ random orientation, leading to frequent misalignment of their predefined beam direction aimed at the base station, which degrades both the link reliability and energy efficiency. To address this challenge, we propose a wave-adaptive beamforming framework to satisfy data-rate demands within limited power budgets. This strategy targets scenarios where sea state information is unavailable, such as in network-assisted systems. We propose a Two-Dimensional Thompson Sampling (2DTS) scheme that jointly selects beamwidth and transmit power to satisfy the target-rate constraint with minimal power consumption and thus achieve maximal energy efficiency. This adaptive learning approach effectively balances exploration and exploitation, enabling efficient operation in uncertain and changing sea conditions. In simulation, under a moderate sea state, 2DTS achieves an energy efficiency of $1.26 \times {10}^4$ bps/Hz/J at round 600, which is $73.7\%$ of the ideal ($1.71 \times {10}^4$), and yield gains of $96.9\%$ and $447.8\%$ over exploration-based TS and conventional TS, respectively. Under a harsh sea state, 2DTS attains $3.09 \times {10}^4$ bps/Hz/J ($85.6\%$ of the ideal $3.61 \times {10}^4$), outperforming the exploration-based and conventional TS by $83.9\%$ and $113.1\%$, respectively. The simulation results demonstrate that the strategy enhances energy efficiency, confirming its practicality for maritime communication systems constrained by limited power budgets. Full article

This article presents a novel strategy for the design of planar phased arrays using Fibonacci-based partitioning combined with a random multi-objective search. This approach intends to minimize the number of phase shifters used by the system while maintaining the radiation characteristics required for Ku-band user terminals in Low Earth Orbit (LEO) satellite communications. This methodology efficiently tessellates a $16\times 16$ antenna array, reducing the solution search space size and improving algorithmic computational time. From a total of 409,600 possible configurations, an optimal candidate solution was obtained in 2 h. This configuration achieves a balanced trade-off between radiation performance metrics, including side lobe level (SLL), first null beamwidth (FNBW), and the number of phase shifters. This optimal design maintains a value of SLL below $-15$ dB across all the azimuth scanning angles, with a beam steering capability of $\theta =-{40}^{\circ }$ and $0^{\circ }\le \varphi \le {360}^{\circ }$. These results demonstrate the suitability of this novel approach regarding Ku-band satellite communications, providing efficient and practical solutions for high-demand internet services via LEO satellite systems. Full article

Integrated sensing and communication (ISAC) has emerged as a transformative technology for intelligent transportation systems. Index modulation (IM), recognized for its high robustness and energy efficiency (EE), has been successfully incorporated into ISAC systems. However, most existing IM-based ISAC schemes overlook the spatial multiplexing potential of millimeter-wave channels and remain confined to single-hop vehicle-to-vehicle (V2V) setups, failing to address the challenges of energy consumption and noise accumulation in real-world multi-hop V2V networks with complex road topologies. To bridge this gap, we propose a spatial scattering modulation-based ISAC (ISAC-SSM) scheme and introduce it to multi-hop V2V networks. The proposed scheme leverages the sensed positioning information to select maximum signal-to-noise ratio relay vehicles and employs a detect-amplify-and-forward (DAF) protocol to mitigate noise propagation, while utilizing sensed angle data for Doppler compensation to enhance communication reliability. At each hop, the transmitter modulates index bits on the angular-domain spatial directions of scattering clusters, achieving higher EE. We initially derive a closed-form bit error rate expression and Chernoff upper bound for the proposed DAF ISAC-SSM under multi-hop V2V networks. Both theoretical analyses and Monte Carlo simulations have been made and demonstrate the superiority of DAF ISAC-SSM over existing alternatives in terms of EE and error performance. Specifically, in a two-hop network with 12 scattering clusters, compared with DAF ISAC-conventional spatial multiplexing, DAF ISAC-maximum beamforming, and DAF ISAC-random beamforming, the proposed DAF ISAC-SSM scheme can achieve a coding gain of 1.5 dB, 2 dB, and 4 dB, respectively. Moreover, it shows robust performance with less than a 1.5 dB error degradation under 0.018 Doppler shifts, thereby verifying its superiority in practical vehicular environments. Full article

Digital Beamforming SAR (DBF-SAR) provides high-resolution wide-swath imaging capability, yet it is affected by inter-channel amplitude, phase and time-delay errors induced by temperature variations and random error factors. Since all elevation channel data are weighted and summed by the DBF module in real time, conventional record-then-compensate approaches cannot meet real-time processing requirements. To resolve the problem, a real-time calibration architecture for Intermediate Frequency DBF (IFDBF) is presented in this paper. The Field-Programmable Gate Array (FPGA) implementation estimates amplitude errors through simple summation, time-delay errors via a simple counter, and phase errors via single-bin Discrete-Time Fourier Transform (DTFT). The time-delay and phase error information are converted into single-tone frequency components through Dechirp processing. The proposed method deliberately employs a reduced-length DTFT implementation to achieve enhanced delay estimation range adaptability. The method completes calibration within tens of PRIs (under 1 s). The proposed method is analyzed and validated through a spaceborne simulation and X-band 16-channel DBF-SAR experiments. Full article

In complex urban wireless environments, unmanned aerial vehicle–mobile edge computing (UAV-MEC) systems face challenges like link blockage and single-antenna eavesdropping threats. The traditional single reconfigurable intelligent surface (RIS), limited in collaboration, struggles to address these issues. This paper proposes a double-RIS cooperative UAV-MEC optimization scheme, leveraging their joint reflection to build multi-dimensional signal paths, boosting legitimate link gains while suppressing eavesdropping channels. It considers double-RIS phase shifts, ground user (GU) transmission power, UAV trajectories, resource allocation, and receiving beamforming, aiming to maximize secure energy efficiency (EE) while ensuring long-term stability of GU and UAV task queues. Given random task arrivals and high-dimensional variable coupling, a dynamic model integrating queue stability and secure transmission constraints is built using Lyapunov optimization, transforming long-term stochastic optimization into slot-by-slot deterministic decisions via the drift-plus-penalty method. To handle high-dimensional continuous spaces, an end-to-end proximal policy optimization (PPO) framework is designed for online learning of multi-dimensional resource allocation and direct acquisition of joint optimization strategies. Simulation results show that compared with benchmark schemes (e.g., single RIS, non-cooperative double RIS) and reinforcement learning algorithms (e.g., advantage actor–critic (A2C), deep deterministic policy gradient (DDPG), deep Q-network (DQN)), the proposed scheme achieves significant improvements in secure EE and queue stability, with faster convergence and better optimization effects, fully verifying its superiority and robustness in complex scenarios. Full article

A comprehensive framework for ultrasound imaging simulations is presented. Solutions to an inhomogeneous wave equation are provided, yielding a linear model for characterizing ultrasound propagation and scattering in soft tissue. This simulation approach, which is based upon the fast nearfield method, is implemented in the Fast Object-oriented C++ Ultrasound Simulator (FOCUS) and is extended to a range of imaging modalities, including synthetic aperture, B-mode, plane wave, and color Doppler imaging. The generation of radiofrequency (RF) data and the receive beamforming techniques employed for each imaging modality, along with background on color Doppler imaging, are described. Simulation results demonstrate rapid convergence and lower error rates compared to conventional spatial impulse response methods and Field II, resulting in substantial reductions in computation time. Notably, the framework effectively simulates hundreds of thousands of scatterers without the need for a full three-dimensional (3D) grid, and the inherent randomness in the scatterer distributions produces realistic speckle patterns. A plane wave imaging example, for instance, achieves high fidelity using 100,000 scatterers with five steering angles, and the simulation is completed on a personal computer in a few minutes. Furthermore, by modeling scatterers as moving particles, the simulation framework captures dynamic flow conditions in vascular phantoms for color Doppler imaging. These advances establish FOCUS as a robust, versatile tool for the rapid prototyping, validation, and optimization of both established and novel ultrasound imaging techniques. Full article

Satellite Internet of Things (IoT) terminals face design constraints regarding low power consumption and light control. These constraints pose a significant collision risk when utilizing traditional random-access protocols, making it challenging to meet the system throughput requirements. Auxiliary beam schemes based on conventional beam formation suffer from the problem of the auxiliary beam shape being limited by the fixed directional map. This leads to the problem of limited throughput enhancement. In this paper, an auxiliary beam weight optimization method for satellite IoT capacity enhancement is proposed. By increasing the number of main flap roll-off bands, the success rate of collision signal separation is increased. It is possible to improve the system access performance. The simulation results indicate that the proposed method can significantly improve the system throughput performance. Furthermore, it can withstand some direction of arrival (DOA) estimation errors and amplitude–phase errors. Robustness is possessed. Full article

Ultra-wide swath synthetic aperture radar (SAR) systems are of great significance for applications such as terrain measurement and ocean monitoring. In conventional SAR systems, targets echo from the near-range and far-range of an observed swath mutually overlap, and the blind ranges are caused by those that the radar cannot receive while it is transmitting. Therefore, the swath of conventional SAR systems is limited due to their range ambiguity as well as the transmitted pulse blockage. With the development of waveform diversity, range ambiguity can be suppressed by radar waveform design with a low-range sidelobe without increasing the system’s complexity when compared to the scan-on-receive (SCORE) based on digital beamforming (DBF) technique. Additionally, by optimizing the pulse repetition interval (PRI) variation strategy, the negative impact of blind range on imaging can be reduced. Based on these technologies, this paper proposes a theoretical architecture to achieve an ultra-wide swath SAR imaging system via chaotic frequency modulation (FM) signals and a random pulse repetition interval variation strategy without increasing the antenna area. By transmitting time-variant chaotic-FM signals, the interference between targets in the near range and far range can be reduced by the corresponding match filters. Furthermore, random pulse repetition intervals increase the irregularity and aperiodicity of the blind ranges to further improve the imaging quality. Simulation results demonstrate that the proposed wide-swath SAR system has better performance compared to classical SAR systems. Full article

In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. A neural network based on minimum variance distortionless response (MVDR) beamformer is considered as the beamformer of choice, where a residual dense convolutional graph-U-Net is applied in a generative adversarial network (GAN) setting to model the beamformer for target speech enhancement under reverberant conditions involving multiple moving speech sources. The input dataset for this neural architecture is constructed by applying multi-source tracking using multi-sensor generalized labeled multi-Bernoulli (MS-GLMB) filtering, which belongs to the labeled RFS framework, to obtain estimations of the sources’ positions and the associated labels (corresponding to each source) at each time frame with high accuracy under the effect of undesirable factors like reverberation and background noise. The tracked sources’ positions and associated labels help to correctly discriminate the target source from the interferers across all time frames and generate time–frequency (T-F) masks corresponding to the target source from the output of a time-varying, minimum variance distortionless response (MVDR) beamformer. These T-F masks constitute the target label set used to train the proposed deep neural architecture to perform target speech enhancement. The exploitation of MS-GLMB filtering and a time-varying MVDR beamformer help in providing the spatial information of the sources, in addition to the spectral information, within the neural speech enhancement framework during the training phase. Moreover, the application of the GAN framework takes advantage of adversarial optimization as an alternative to maximum likelihood (ML)-based frameworks, which further boosts the performance of target speech enhancement under reverberant conditions. The computer simulations demonstrate that the proposed approach leads to better target speech enhancement performance compared with existing state-of-the-art DL-based methodologies which do not incorporate the labeled RFS-based approach, something which is evident from the 75% ESTOI and PESQ of 2.70 achieved by the proposed approach as compared with the 46.74% ESTOI and PESQ of 1.84 achieved by Mask-MVDR with self-attention mechanism at a reverberation time (RT60) of 550 ms. Full article

The emergence of 5G technology promises remarkable advancements in wireless communication, particularly in the realm of mmWave (millimeter-wave) massive multiple input multiple output (m-MIMO) systems. However, the realization of its full potential is hindered by the challenge of pilot overhead, which compromises system efficiency. The efficient usage of pilot signals is crucial for precise channel estimation and interference reduction to maintain data integrity. Nevertheless, this requirement brings up the challenge of pilot overhead, which utilizes precious spectrum space, thus reducing spectral efficiency (SE). To address this obstacle, researchers have progressively turned to artificial intelligence (AI) and machine learning (ML) methods to design hybrid beam-forming systems that enhance SE while reducing changes to the bit error rate (BER). This study addresses the challenge of pilot overhead in hybrid beamforming for 5G mmWave m-MIMO systems by leveraging advanced artificial intelligence (AI) techniques. We propose a framework integrating k-clustering, linear regression, random forest regression, and neural networks with singular value decomposition (NN-SVD) to optimize pilot placement and hybrid beamforming strategies. The results demonstrate an 82% reduction in pilot overhead, a 250% improvement in spectral efficiency, and a tenfold enhancement in bit error rate at low SNR conditions, surpassing state-of-the-art methods. These findings validate the efficacy of the proposed system in advancing next-generation wireless networks. Full article

Passive radar has drawn a lot of attention due to its applications across military and civilian sectors. Under this working paradigm, the utilization of antenna arrays is instrumental, as it increases the signal quality and enables precise target positioning. These promising features rely, however, on the precise calibration of the antenna array, as the different hardware components introduce impairments that compromise the beamforming capabilities of the system. We propose a technique that employs a low-power external beacon signal to produce precise information about the target location, avoiding the angular ambiguities present in other solutions in the literature. The experimental results demonstrate the method’s ability to effectively correct the amplitude and phase inconsistencies while compensating for frequency drifts, enabling beamforming capabilities and direction-of-arrival estimation. Among the tested beacon waveforms, the pseudo-random noise-based signals proved the most robust, especially in low-power scenarios. Additionally, the method was validated in a passive radar setup, where it successfully detected a vessel using opportunistic signals. These findings highlight the method’s potential to enhance passive radar performance while maintaining a low probability of detection, a key aspect in military applications, as well as its applicability to civilian purposes, such as infrastructure monitoring, environmental observation, and traffic management. Full article

This paper investigates the design of active and passive beamforming in a reconfigurable intelligent surface (RIS)-aided multi-user multiple-input single-output (MU-MISO) system with the objective of maximizing the sum rate. We propose a deep evolution policy (DEP)-based algorithm to derive the optimal beamforming strategy by generating multiple agents, each utilizing distinct deep neural networks (DNNs). Additionally, a random subspace selection (RSS) strategy is incorporated to effectively balance exploitation and exploration. The proposed DEP-based algorithm operates without the need for alternating iterations, gradient descent, or backpropagation, enabling simultaneous optimization of both active and passive beamforming. Simulation results indicate that the proposed algorithm can bring significant performance enhancements. Full article

Millimeter-wave (mmWave) antenna arrays are pivotal components in modern wireless communication systems, offering high data rates and improved spectrum efficiency. However, the performance of mmWave antenna arrays can be significantly affected by structural distortions, such as mechanical deformations and environmental conditions, which may lead to deviations in beamforming characteristics and radiation patterns. In this paper, we present a comprehensive sensitivity study of mmWave antenna arrays to structural distortion, employing a coupled structural–electromagnetic statistical concept. The proposed model integrates structural analysis techniques with electromagnetic simulations to assess the impact of structural distortions on the performance of mmWave antenna arrays. In addition, the model incorporates random element positioning, making it easy to analyze radiation pattern sensitivity to structural deformation. Demonstrating the applicability of the model, a 10 × 10 microstrip patch antenna array is designed to assess the performance of the model with a random position error and saddle shape distortion. The results of the model are then compared against the acceptable results from the HFSS software (version 13.0), where a good agreement is observed between the two results. The results show the gain variation and sidelobe level under various degrees of distortion and random errors, respectively. These results provide a guide for design, deployment, and optimization of mmWave communication networks in real-world environments. In addition, the model provides valuable insights into the trade-offs between antenna performance, structural integrity, and system reliability, paving the way for more efficient and dependable mmWave communication systems in the era of 5G and beyond. Full article

Reducing the number of phase shifters by grouping antenna elements into subarrays has been extensively studied for decades. The number of phase shifters directly affects the cost, complexity, and power consumption of the system. A novel method for the design of phased planar antenna arrays is presented in this work in order to perform a reduction of up to $70\%$ in the number of phase shifters used by the array, while maintaining the desired radiation characteristics. This method consists of creating fusions of subarrays to generate random sequences that form the best feeding network configuration for planar phased arrays. The obtained solution allows scanning the mainlobe at $\theta =-{40}^{\circ }$ elevation with a range of scanning of $[-{75}^{\circ }<\varphi <{75}^{\circ }]$ in the azimuth plane, while maintaining a side lobe level below $-10$ dB and achieving a reduction of $62\%$ in the number of phase shifters. It is shown that each solution is created based on search criteria, which influence the morphology of the array in terms of subarray size and orientation. The proposed methodology shows great flexibility for creating new phased antenna array designs that meet the requirements of specific applications in a short period of time. Full article