Self-Interference-Cancelation-Based SLNR Precoding Design for Full-Duplex Relay-Assisted System

Han, Shuai; Zhang, Yi; Meng, Weixiao; Chen, Hsiao‐Hwa

doi:10.1109/tvt.2018.2840824

Cited by 44 publications

(23 citation statements)

References 34 publications

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“…Over the past several decades, much research has been devoted to developing efficient and reliable communication networks [1], [2]. Let us focus on 5G and take massive MIMO as an example.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

Huang

Guo

Gui

et al. 2020

IEEE Wireless Commun.

334

161

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The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the current communication systems, which were designed on the basis of conventional communication theories, significantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learningbased communication methods are presented along with the research opportunities and challenges.In particular, novel communication frameworks of non-orthogonal multiple access (NOMA), massive 2 multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We vision that the appealing deep learning-based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.

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“…Over the past several decades, much research has been devoted to developing efficient and reliable communication networks [1], [2]. Let us focus on 5G and take massive MIMO as an example.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

Huang

Guo

Gui

et al. 2020

IEEE Wireless Commun.

334

161

View full text Add to dashboard Cite

show abstract

“…To this end, the spectral efficiency and transmission latency shall be greatly improved and reduced, respectively, under massive connected devices. To fulfill these requirements, novel radio access technologies are required, such as novel multiple access (MA) technologies, network architectures, encoding, and modulation methods [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Aided Detection Method for FTN-Based NOMA

Pan

Wang

et al. 2020

Wireless Communications and Mobile Computing

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The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses. Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology. The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.

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“…The radar-assisted detection and identification method has the advantage of working under all weather conditions and is capable of extracting additional features of the UAV due to the micro-Doppler features [7]. According to the current technology development plan, 5G will adopt the previous generations of wireless solutions (3G and 4G) and introduce new promising mmWave technology so that a 5G base station not only can serve as a communication site for 5G networks but can also provide many powerful radar sensors in a functional device network, allowing for the detection of drones in urban environments [8][9][10]. The background of the UAV flight area is usually complex with other aircraft and birds; therefore, advanced clutter suppression and target detection algorithms are required.…”

Section: Introductionmentioning

confidence: 99%

Radar-Assisted UAV Detection and Identification Based on 5G in the Internet of Things

Zhao

Yang

et al. 2019

Wireless Communications and Mobile Computing

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Unmanned aerial vehicles (UAVs) have broad application potential for the Internet of Things (IoT) due to their small size, low cost, and flexible control. At present, the main positioning method for UAVs is the use of GPS. However, GPS positioning may be affected by stronger electromagnetic signals from spoofing attacks. In this study, a radar-assisted positioning method based on 5G millimeter waves is proposed. In 5G end-to-end network slices, the rotors of UAVs can be detected and identified by deploying 5G millimeter wave radar. High-resolution range profile (HRRP) is used to obtain the UAV location in the detection zone. Micro-Doppler characteristics are used to identify the UAVs and the cepstrum method is used to extract the number and speed information of the UAV rotor. The sinusoidal frequency modulation (SFM) parameter optimization method is used to separate multiple UAVs. The proposed method provides information on the number of UAVs, the position of the UAV, the number of rotors, and the rotation speed of each rotor. The simulation results show that the proposed radar detection method is well suited for UAV detection and identification and provides a valid GPS-independent method for UAV tracking.

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Self-Interference-Cancelation-Based SLNR Precoding Design for Full-Duplex Relay-Assisted System

Cited by 44 publications

References 34 publications

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

A Deep Learning-Aided Detection Method for FTN-Based NOMA

Radar-Assisted UAV Detection and Identification Based on 5G in the Internet of Things

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