Relevant bibliographies by topics / Dual-Functional Radar Communication

Academic literature on the topic 'Dual-Functional Radar Communication'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Dual-Functional Radar Communication"

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Liu, Fan, Longfei Zhou, Christos Masouros, Ang Li, Wu Luo, and Athina Petropulu. "Toward Dual-functional Radar-Communication Systems: Optimal Waveform Design." IEEE Transactions on Signal Processing 66, no. 16 (August 15, 2018): 4264–79. http://dx.doi.org/10.1109/tsp.2018.2847648.

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Zhang, Zhibo, Qing Chang, Fan Liu, and Shengzhi Yang. "Dual-Functional Radar-Communication Waveform Design: Interference Reduction Versus Exploitation." IEEE Communications Letters 26, no. 1 (January 2022): 148–52. http://dx.doi.org/10.1109/lcomm.2021.3122980.

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Valiulahi, Iman, Christos Masouros, Abdelhamid Salem, and Fan Liu. "Antenna Selection for Energy-Efficient Dual-Functional Radar-Communication Systems." IEEE Wireless Communications Letters 11, no. 4 (April 2022): 741–45. http://dx.doi.org/10.1109/lwc.2022.3142043.

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Liu, Fan, Christos Masouros, Tharmalingam Ratnarajah, and Athina Petropulu. "On Range Sidelobe Reduction for Dual-Functional Radar-Communication Waveforms." IEEE Wireless Communications Letters 9, no. 9 (September 2020): 1572–76. http://dx.doi.org/10.1109/lwc.2020.2997959.

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Shi, Shengnan, Zhaoyi Wang, Zishu He, and Ziyang Cheng. "Constrained waveform design for dual-functional MIMO radar-Communication system." Signal Processing 171 (June 2020): 107530. http://dx.doi.org/10.1016/j.sigpro.2020.107530.

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Li, Ting, Tian Liu, Zhangli Song, Lin Zhang, and Yiming Ma. "Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing." Electronics 13, no. 10 (May 10, 2024): 1872. http://dx.doi.org/10.3390/electronics13101872.

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Recent years witness the rapid development of communication and radar technologies, and many transmitters are equipped with both communication and radar functionalities. To keep track of the working state of a target dual-functional transmitter, it is crucial to sense the modulation mode of the emitted signals. In this paper, we propose a deep learning-based intelligent modulation sensing technique for dual-functional transmitters. Different from existing modulation sensing methods, which usually focus on communication signals, we take both communication and radar signals into consideration. Typically, the dominant features of communication signals lie in the time domain, while those of radar signals lie in both time and frequency domains. To enhance the sensing accuracy, we first exploit real and complex value convolution operations to extract both time-domain and frequency-domain features of emitted signals from the target transmitter. Then, we fuse the extracted features by assigning proper weights with the attention mechanism. Simulation results reveal that the proposed technique can improve the sensing accuracy by up to 4% on average compared with benchmarks.
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Hieu, Nguyen Quang, Dinh Thai Hoang, Nguyen Cong Luong, and Dusit Niyato. "iRDRC: An Intelligent Real-Time Dual-Functional Radar-Communication System for Automotive Vehicles." IEEE Wireless Communications Letters 9, no. 12 (December 2020): 2140–43. http://dx.doi.org/10.1109/lwc.2020.3014972.

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Wang, Xinyi, Zesong Fei, Zhong Zheng, and Jing Guo. "Joint Waveform Design and Passive Beamforming for RIS-Assisted Dual-Functional Radar-Communication System." IEEE Transactions on Vehicular Technology 70, no. 5 (May 2021): 5131–36. http://dx.doi.org/10.1109/tvt.2021.3075497.

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Zhang, Tingxiao, Yongbo Zhao, Donghe Liu, and Jinli Chen. "Interference optimized dual-functional radar-communication waveform design with low PAPR and range sidelobe." Signal Processing 204 (March 2023): 108828. http://dx.doi.org/10.1016/j.sigpro.2022.108828.

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Zhao, Yinan, Zhongqing Zhao, Fangqiu Tong, Ping Sun, Xiang Feng, and Zhanfeng Zhao. "Joint Design of Transmitting Waveform and Receiving Filter via Novel Riemannian Idea for DFRC System." Remote Sensing 15, no. 14 (July 14, 2023): 3548. http://dx.doi.org/10.3390/rs15143548.

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Recently, the problem of target detection in noisy environments for the Dual-Functional Radar Communication (DFRC) integration system has been a hot topic. In this paper, to suppress the noise and further enhance the target detection performance, a novel manifold Riemannian Improved Armijo Search Conjugate Gradient algorithm (RIASCG) framework has been proposed which jointly optimizes the integrated transmitting waveform and receiving filter. Therein, the reference waveform is first designed to achieve excellent pattern matching of radar beamforming. Furthermore, to ensure the quality of system information transmission, the energy of multi-user interference (MUI) of communication signals is incorporated as the constraint. Additionally, the typical similarity constraint is introduced to ensure the transmitting waveform with a good ambiguity function. Finally, simulation results demonstrate that the designed waveform not only enhances the system’s target detection performance in noisy environments but also achieves a relatively good multi-user communication ability when compared with other prevalent waveforms.
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Dissertations / Theses on the topic "Dual-Functional Radar Communication"

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Shahbazi, Arzhang. "Machine Learning Techniques for UAV-assisted Networks." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG076.

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L'objectif principal de cette thèse est la modélisation, l'évaluation des performances et l'optimisation au niveau du système des réseaux cellulaires de prochaine génération équipés de drones en utilisant l'intelligence artificielle. En outre, la technologie émergente de détection et de communication intégrées est étudiée pour être appliquée aux futurs réseaux sans fil des drones. En particulier, en s'appuyant sur la technique d'apprentissage par renforcement pour contrôler les actions des drones, cette thèse développe un ensemble de nouveaux cadres d'apprentissage automatique pour incorporer des mesures de performance importantes dans l'agent, telles que le débit du système de communication et l'erreur de localisation, qui peuvent être utilisées pour l'analyse et l'optimisation au niveau du système. Plus précisément, un nouvel algorithme basé sur l'apprentissage est proposé pour maximiser le débit du système en utilisant une connaissance préalable de la probabilité de présence des utilisateurs dans un réseau. Un cadre d'apprentissage fédéré a été introduit pour trouver une planification optimale de la trajectoire en formant un agent avec un algorithme d'apprentissage profond dans différents environnements afin d'obtenir une généralisation et une convergence plus rapide. Les performances d'un drone équipé d'un système de communication radar à double fonction sont étudiées et les avantages potentiels de ces systèmes sont démontrés en optimisant conjointement le débit du système de communication et l'erreur de localisation
The main focus of this thesis is on modeling, performance evaluation and system-level optimization of next-generation cellular networks empowered by Unmanned Aerial Vehicles (UAVs) by using Machine Learning (ML). In addition, the emerging technology of Integrated Sensing and Communication is investigated for application to future UAV wireless networks. In particular, relying on Reinforcement Learning (RL) technique for controlling UAV actions, this thesis develops a set of new ML frameworks for incorporating important performance metrics in to the RL agent, such as the communication system throughput and localization error, which can be used for system-level analysis and optimization. More specifically, a new learning-based algorithms proposed to maximize the system throughput by utilizing a prior knowledge of users likelihood of presence in a grid. A Federated Learning (FL) framework introduced to find an optimal path planning through training an agent with RL algorithm in different environment settings to achieve generalization and faster convergence. The performance of UAV equipped with Dual-Functional Radar Communication (DFRC) is investigated and the potential benefits of DFRC systems are shown by jointly optimizing communication system throughput and localization error
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Book chapters on the topic "Dual-Functional Radar Communication"

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Wang, Xiangrong, Xianghua Wang, Weitong Zhai, and Kaiquan Cai. "Sparse Sensing for Dual-Functional Radar Communications." In Sparse Sensing and Sparsity Sensed in Multi-sensor Array Applications, 241–90. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9558-5_8.

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Conference papers on the topic "Dual-Functional Radar Communication"

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Yang, Wei-Chih, Hsin-Yuan Chang, Ronald Y. Chang, and Wei-Ho Chung. "Hybrid Beamforming for Dual-Functional Radar-Communication Systems." In 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring). IEEE, 2023. http://dx.doi.org/10.1109/vtc2023-spring57618.2023.10199478.

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Zeng, Junjie, Ping Chu, and Bin Liao. "Hybrid Transmitter and Radar Receiver Design for OFDM Dual-Functional Radar-Communication." In 2021 CIE International Conference on Radar (Radar). IEEE, 2021. http://dx.doi.org/10.1109/radar53847.2021.10027974.

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Xiao, Jun, Jianhua Tang, and Jiao Chen. "Efficient Radar Detection for RIS-Aided Dual-Functional Radar-Communication System." In 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring). IEEE, 2023. http://dx.doi.org/10.1109/vtc2023-spring57618.2023.10200033.

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Cheng, Ziyang, Bin Liao, and Zishu He. "Hybrid Transceiver Design for Dual-Functional Radar-Communication System." In 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2020. http://dx.doi.org/10.1109/sam48682.2020.9104387.

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Su, Nanchi, Fan Liu, Christos Masouros, Tharmalingam Ratnarajah, and Athina Petropulu. "Secure Dual-functional Radar-Communication Transmission: Hardware-Efficient Design." In 2021 55th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2021. http://dx.doi.org/10.1109/ieeeconf53345.2021.9723251.

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Liu, Rang, Ming Li, Yang Liu, and Qian Liu. "Symbol-Level Precoding Design for Dual-Functional Radar-Communication Systems." In ICC 2021 - IEEE International Conference on Communications. IEEE, 2021. http://dx.doi.org/10.1109/icc42927.2021.9500781.

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Zhou, Longfei, Fan Liu, Chang Tian, Christos Masouros, Ang Li, Wei Jiang, and Wu Luo. "Optimal Waveform Design for Dual-functional MIMO Radar-Communication Systems." In 2018 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2018. http://dx.doi.org/10.1109/iccchina.2018.8641142.

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Zhao, Yifei, Zixin Wang, Zhibin Wang, Xu Chen, and Yong Zhou. "Learning to Beamform for Dual-Functional MIMO Radar-Communication Systems." In ICC 2023 - IEEE International Conference on Communications. IEEE, 2023. http://dx.doi.org/10.1109/icc45041.2023.10279159.

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Zhao, Zongyao, Xinke Tang, and Yuhan Dong. "Cognitive Waveform Design for Dual-functional MIMO Radar-Communication Systems." In GLOBECOM 2022 - 2022 IEEE Global Communications Conference. IEEE, 2022. http://dx.doi.org/10.1109/globecom48099.2022.10001112.

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Chu, Jinjin, Rang Liu, Yang Liu, Ming Li, and Qian Liu. "AN-aided Secure Beamforming Design for Dual-Functional Radar-Communication Systems." In 2021 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE, 2021. http://dx.doi.org/10.1109/icccworkshops52231.2021.9538912.

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