Unsupervised Deep Learning for MU-SIMO Joint Transmitter and Noncoherent Receiver Design

Xue, Songyan; Ma, Yi; Yi, Na; Tafazolli, Rahim

doi:10.1109/lwc.2018.2865563

Cited by 40 publications

(37 citation statements)

References 8 publications

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“…This is where FF-DNN can play a central role. The use of FF-DNN for MUD can be found in the literature [14], [15]; and in this paper we will offer a further study. • The FF-DNN after training can yield a low-complexity solution to the CFO classification and MUD.…”

Section: Introductionmentioning

confidence: 94%

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Unsupervised Deep Learning for Blind Multiuser Frequency Synchronization in OFDMA Uplink

Xue

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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In this paper, a novel unsupervised deep learning approach is proposed to tackle the multiuser frequency synchronization problem inherent in orthogonal frequency-division multiple-access (OFDMA) uplink communications. The key idea lies in the use of the feed-forward deep neural network (FF-DNN) for multiuser interference (MUI) cancellation taking advantage of their strong classification capability. Basically, the proposed FF-DNN consists of two essential functional layers. One is called carrier-frequency-offsets (CFOs) classification layer that is responsible for identifying the users' CFO range, and another is called MUI-cancellation layer responsible for joint multiuser detection (MUD) and frequency synchronization. By such means, the proposed FF-DNN approach showcases remarkable MUIcancellation performances without the need of multiuser CFO estimation. In addition, we also exhibit an interesting phenomenon occurred at the CFO-classification stage, where the CFO-classification performance get improved exponentially with the increase of the number of users. This is called multiuser diversity gain in the CFO-classification stage, which is carefully studied in this paper.

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Section: Introductionmentioning

confidence: 94%

“…II. PRELIMINARIES AND PRINCIPLES The use of deep learning for signal detection (i.e., MUD) is equivalent to the classification of received signals y according to the maximum a posteriori (MAP) principle [14] x = arg max…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Deep Learning for Blind Multiuser Frequency Synchronization in OFDMA Uplink

Xue

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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“…Machine learning techniques have been very recently explored at the physical layer of wireless communications [10]- [13]. For example, [11] developed a deep-learning (DL) autoencoder for single-input multiple-output (SIMO) communication systems with deep neural networks (DNNs). In OFDM Youngwook Ko is with Queens University of Belfast, United Kingdom (Email: y.ko@qub.ac.uk).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Machine Intelligence for Automation of Multi-Dimensional Modulation

Choi

2019

IEEE Commun. Lett.

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In this letter, we propose a new unsupervised machine learning technique for a multi-dimensional modulator that can autonomously learn key exploitable features from significant variations of multi-dimensional wireless propagation parameters, followed by a real-time prediction of the best multi-dimensional modulation mode to be used for the next resilient transmission. The proposed method aims to embrace the potential of the unsupervised K-means clustering into the physical layer of noncoherent multi-dimensional transmission. Simulation results show that the proposed scheme can outperform the benchmarks at a cost of simple offline training.

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“…Then, the trained autoencoder can be directly applied to practical systems on line. A DL-based communication system interpreted as an autoencoder performs an end-to-end reconstruction task that jointly optimizes transmitter and receiver as well as learns signal encoding [17], [23], [24], [32]. Considering the advantages of the autoencoder, a complete communication system represented as an autoencoder was proposed to address the challenges of frame synchronization [18], [26], where a competitive performance can be achieved even without extensive hyperparameter tuning.…”

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confidence: 99%

“…Consequently, high data-rate schemes should be developed in DL-based communication systems for future wireless networks. However, in [15], [17], [18], [22], [24]- [26], [32], one-hot vector, being the only data representation, has a low data rate in DL-based communication systems. The reason is that an M × 1 one-hot vector consists of 0s in all entries with the exception of a single 1 [36], e.g., [0, · · · , 0, 1, 0, · · · , 0] T , and there are only M 1 possible transmitted messages that lead to limited data rate, which is a barrier for developing DL-based communication systems in the future.…”

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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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Unsupervised Deep Learning for MU-SIMO Joint Transmitter and Noncoherent Receiver Design

Cited by 40 publications

References 8 publications

Unsupervised Deep Learning for Blind Multiuser Frequency Synchronization in OFDMA Uplink

Unsupervised Deep Learning for Blind Multiuser Frequency Synchronization in OFDMA Uplink

Unsupervised Machine Intelligence for Automation of Multi-Dimensional Modulation

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

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