{"title":"下一代解决方案:针对嘈杂信道环境的深度学习增强型联合认知雷达和通信系统设计","authors":"","doi":"10.1016/j.compeleceng.2024.109663","DOIUrl":null,"url":null,"abstract":"<div><p>In recent years, the dual-function radar and communication (DFRC) paradigm has emerged as a focal point in addressing spectrum congestion challenges. However, prevailing research heavily relies on computationally complex likelihood-based approaches for communication signals with an added Gaussian noise based single waveform. Note that, a single waveform for diverse scenarios e.g., presence of a communication receiver in the radar main lobe, side lobe, etc., may lead to a deteriorated detection performance in a DFRC design. Therefore, in this paper, we present a cognitive DFRC architecture that utilizes a diverse set of orthogonal waveforms at the transmitter. Specifically, based on a perception-action cycle, a QAM-based waveform is employed for communication when both the radar target and communication receiver are within the main lobe, while a PSK-based waveform is used when the radar target is in the main lobe and the communication receiver is in the side lobes. Furthermore, to enhance the feature-based estimation, the communication receiver integrates a Convolutional Neural Network (CNN) architecture designed to autonomously learn and extract features from received signals with different Signal-to-Noise ratio (SNR). Next, the adaptive nature of the system enables proficient discernment of the received signal type and its corresponding SNR value. Moreover, deep learning techniques are applied in realistic scenarios with various channel impairments to extract features from received signals, departing significantly from likelihood-based methods and reducing computational complexity. The proposed methodology’s effectiveness is validated through Monte Carlo simulations, underscoring its potential to address challenges associated with DFRC under real-world conditions.</p></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0045790624005901/pdfft?md5=3d77ea85d489b20d44597e1922b0aaba&pid=1-s2.0-S0045790624005901-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Next-Gen solutions: Deep learning-enhanced design of joint cognitive radar and communication systems for noisy channel environments\",\"authors\":\"\",\"doi\":\"10.1016/j.compeleceng.2024.109663\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In recent years, the dual-function radar and communication (DFRC) paradigm has emerged as a focal point in addressing spectrum congestion challenges. However, prevailing research heavily relies on computationally complex likelihood-based approaches for communication signals with an added Gaussian noise based single waveform. Note that, a single waveform for diverse scenarios e.g., presence of a communication receiver in the radar main lobe, side lobe, etc., may lead to a deteriorated detection performance in a DFRC design. Therefore, in this paper, we present a cognitive DFRC architecture that utilizes a diverse set of orthogonal waveforms at the transmitter. Specifically, based on a perception-action cycle, a QAM-based waveform is employed for communication when both the radar target and communication receiver are within the main lobe, while a PSK-based waveform is used when the radar target is in the main lobe and the communication receiver is in the side lobes. Furthermore, to enhance the feature-based estimation, the communication receiver integrates a Convolutional Neural Network (CNN) architecture designed to autonomously learn and extract features from received signals with different Signal-to-Noise ratio (SNR). Next, the adaptive nature of the system enables proficient discernment of the received signal type and its corresponding SNR value. Moreover, deep learning techniques are applied in realistic scenarios with various channel impairments to extract features from received signals, departing significantly from likelihood-based methods and reducing computational complexity. The proposed methodology’s effectiveness is validated through Monte Carlo simulations, underscoring its potential to address challenges associated with DFRC under real-world conditions.</p></div>\",\"PeriodicalId\":50630,\"journal\":{\"name\":\"Computers & Electrical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0045790624005901/pdfft?md5=3d77ea85d489b20d44597e1922b0aaba&pid=1-s2.0-S0045790624005901-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Electrical Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0045790624005901\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045790624005901","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Next-Gen solutions: Deep learning-enhanced design of joint cognitive radar and communication systems for noisy channel environments
In recent years, the dual-function radar and communication (DFRC) paradigm has emerged as a focal point in addressing spectrum congestion challenges. However, prevailing research heavily relies on computationally complex likelihood-based approaches for communication signals with an added Gaussian noise based single waveform. Note that, a single waveform for diverse scenarios e.g., presence of a communication receiver in the radar main lobe, side lobe, etc., may lead to a deteriorated detection performance in a DFRC design. Therefore, in this paper, we present a cognitive DFRC architecture that utilizes a diverse set of orthogonal waveforms at the transmitter. Specifically, based on a perception-action cycle, a QAM-based waveform is employed for communication when both the radar target and communication receiver are within the main lobe, while a PSK-based waveform is used when the radar target is in the main lobe and the communication receiver is in the side lobes. Furthermore, to enhance the feature-based estimation, the communication receiver integrates a Convolutional Neural Network (CNN) architecture designed to autonomously learn and extract features from received signals with different Signal-to-Noise ratio (SNR). Next, the adaptive nature of the system enables proficient discernment of the received signal type and its corresponding SNR value. Moreover, deep learning techniques are applied in realistic scenarios with various channel impairments to extract features from received signals, departing significantly from likelihood-based methods and reducing computational complexity. The proposed methodology’s effectiveness is validated through Monte Carlo simulations, underscoring its potential to address challenges associated with DFRC under real-world conditions.
期刊介绍:
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.
Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.