Adaptive Modulation and Filter Configuration in Universal Filtered Multi-Carrier Systems

Chen, Xiao; Wu, Liang; Zhang, Zaichen; Dang, Jian; Wang, Jiangzhou

doi:10.1109/twc.2017.2786231

Cited by 34 publications

(25 citation statements)

References 35 publications

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“…The number of the IDFT points is set as N = 64; the number of sub-bands is B = 2, which are allocated to two different UEs. Following the setting of [19], each sub-band occupies n s = 16 subcarriers. Transmitters using the length L Dolph-Chebyshev filter whose Fourier transform side-lobe magnitude is α dB below the mainlobe magnitude, where L = 6, α = 120.…”

Section: Simulation Resultsmentioning

confidence: 99%

Joint Timing Offset and Channel Estimation for Multi-User UFMC Uplink

Chu

Wang

2020

IEEE Wireless Commun. Lett.

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Universal filtered multi-carrier (UFMC), which groups and filters subcarriers before transmission, is a potential multi-carrier modulation technique investigated for the emerging Machine-Type Communications (MTC). Considering the relaxed timing synchronization requirement of UFMC, we design a novel joint timing synchronization and channel estimation method for multi-user UFMC uplink transmission. Aiming at reducing overhead for higher system performance, the joint estimation problem is formulated using atomic norm minimization that enhances the sparsity of timing offset in the continuous frequency domain. Simulation results show that the proposed method can achieve considerable performance gain, as compared with its counterparts.

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Section: Simulation Resultsmentioning

confidence: 99%

Joint Timing Offset and Channel Estimation for Multi-User UFMC Uplink

Chu

Wang

2020

IEEE Wireless Commun. Lett.

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“…On the one hand, the trained autoencoder for a certain value of M cannot work in the scenarios with different values of M. On the other hand, the performance of DL-based communication system will not be optimal when the trained autoencoder is applied to different SNR scenarios. For these reasons, there is a need for an adaptive transmission strategy for the autoencoder to improve the applicability and optimize the system performances, such as maximizing the data rate and satisfying the QoS constraint [37], [38].…”

Section: B Motivationsmentioning

confidence: 99%

Data-Rate Driven Transmission Strategies for Deep Learning-Based Communication Systems

Chen

Cheng

Zhang

et al. 2020

IEEE Trans. Commun.

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Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. In this paper, we focus on the fundamental problems of DL-based communication systems, including high rate transmission and performance analysis. To address the limited data rate issue, we first consider the error rate constraint and design a transmission algorithm to adaptively select the transmission vectors to maximize the data rate for various channel scenarios. Furthermore, a novel generalized data representation (GDR) scheme is proposed to improve the data rate of DLbased communication systems. Then, we analyze the effect of signal-to-noise ratio (SNR) and mean squared error performance of the proposed DL-based communication systems. Finally, numerical results show that the proposed adaptive transmission and GDR schemes achieve higher data rate and have lower training complexity than the conventional one-hot vector scheme. Both the new schemes and the conventional scheme have comparable block error rate (BLER) performance. According to both theoretical analysis and simulated results, it is suggested that low or wide-range training SNR is beneficial to attain good BLER performance for practical transmission with various channel scenarios. ). 2 Autoencoder, communication systems, data rate, deep learning, transmission strategy. I. INTRODUCTIONTo ensure high demand for various communication applications and services, the next-generation network must be able to deliver enhanced mobile broadband, ultra-reliable and low-latency communications (URLLC), and massive Internet of Things (IoT) ecosystems [1]- [4]. The primary concern is to satisfy the exponential rise in the number of user equipments and the traffic capacity of future communication systems. Hence, several promising technologies have been proposed, and they include massive multi-input and multi-output (MIMO) transmissions, millimeter wave (mmWave) communications, ultra-dense networks (UDNs) [5]-[9]. However, there exist a number of limitations for conventional communication systems, such as unavailable channel state information in complex transmission scenario, high complexity to process big data, and sub-optimal performance caused by conventional block structure. For this reason, with the significant development of deep learning (DL) [10]-[12], researchers are attempting to apply the machine learning (ML), especially DL technologies, to communication system design for new benefits [13]-[16] that cannot be obtained using the conventional approaches.As a promising technique, deep learning applies a useful and insightful way to implement communication systems using deep neural networks (NNs). Different from the conventional communication system that consists of multiple independent blocks (e.g., source/channel coding, modulation, channel estimation, equalization), the DL-based communication system can jointly optimize transmitter and receiver for end-to-end performance without block structure [17], [18]. DL-based system design is promising for f...

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“…In conventional multiantenna systems, e.g., [6], [7], orthogonal multiple access (OMA) schemes are often exploited. In these OMA techniques, the resources (time, frequency, or code) are allocated to each user exclusively and hence multiuser interference can be avoided.…”

Section: Introductionmentioning

confidence: 99%

On Optimal Beamforming Design for Downlink MISO NOMA Systems

Zhu

Wang

Huang

et al. 2020

IEEE Trans. Veh. Technol.

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This work focuses on the beamforming design for downlink multiple-input single-output (MISO) nonorthogonal multiple access (NOMA) systems. The beamforming vectors are designed by solving a total transmission power minimization (TPM) problem with quality-of-service (QoS) constraints. In order to solve the proposed nonconvex optimization problem, we provide an efficient method using semidefinite relaxation. Moreover, for the first time, we characterize the optimal beamforming in a closed form with quasi-degradation condition, which is proven to achieve the same performance as dirtypaper coding (DPC). For the special case with two users, we further show that the original nonconvex TPM problem can be equivalently transferred into a convex optimization problem and easily solved via standard optimization tools. In addition, the optimal beamforming is also characterized in a closed form and we show that it achieves the same performance as the DPC. In the simulation, we show that our proposed optimal NOMA beamforming outperforms OMA schemes and can even achieve the same performance as DPC. Our solutions dramatically simplifies the problem of beamforming design in the downlink MISO NOMA systems and improve the system performance.

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Adaptive Modulation and Filter Configuration in Universal Filtered Multi-Carrier Systems

Cited by 34 publications

References 35 publications

Joint Timing Offset and Channel Estimation for Multi-User UFMC Uplink

Joint Timing Offset and Channel Estimation for Multi-User UFMC Uplink

Data-Rate Driven Transmission Strategies for Deep Learning-Based Communication Systems

On Optimal Beamforming Design for Downlink MISO NOMA Systems

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