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Zhenyu Liu

Bio: Zhenyu Liu is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Channel state information & MIMO. The author has an hindex of 6, co-authored 13 publications receiving 166 citations.

Papers
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Journal ArticleDOI
TL;DR: Two deep learning architectures are proposed, Dual net-MAG and DualNet-ABS, to significantly reduce the CSI feedback payload based on the multipath reciprocity, based on limited feedback and bi-directional reciprocal channel characteristics.
Abstract: Channel state information (CSI) feedback is important for multiple-input multiple-output (MIMO) wireless systems to achieve their capacity gain in frequency division duplex mode. For massive MIMO systems, CSI feedback may consume too much bandwidth and degrade spectrum efficiency. This letter proposes a learning-based CSI feedback framework based on limited feedback and bi-directional reciprocal channel characteristics. The massive MIMO base station exploits the available uplink CSI to help recovering the unknown downlink CSI from low rate user feedback. We propose two deep learning architectures, DualNet-MAG and DualNet-ABS, to significantly reduce the CSI feedback payload based on the multipath reciprocity. DualNet-MAG and DualNet-ABS can exploit the bi-directional correlation of the magnitude and the absolute value of real/imaginary parts of the CSI coefficients, respectively. The experimental results demonstrate that our architectures bring an obvious improvement compared with the downlink-based architecture.

112 citations

Journal ArticleDOI
TL;DR: This work develops an efficient DL-based compression framework CQNet to jointly tackle CSI compression, codeword quantization, and recovery under the bandwidth constraint and proposes a more efficient quantization scheme in the radial coordinate by introducing a novel magnitude-adaptive phase quantization framework.
Abstract: Channel state information (CSI) reporting is important for multiple-input multiple-output (MIMO) wireless transceivers to achieve high capacity and energy efficiency in frequency division duplex (FDD) mode. CSI reporting for massive MIMO systems could consume large bandwidth and degrade spectrum efficiency. Deep learning (DL)-based CSI reporting integrated with channel characteristics has demonstrated success in improving CSI compression and recovery. To further improve the encoding efficiency of CSI feedback, we develop an efficient DL-based compression framework CQNet to jointly tackle CSI compression, codeword quantization, and recovery under the bandwidth constraint. CQNet is directly compatible with other DL-based CSI feedback works for further enhancement. We propose a more efficient quantization scheme in the radial coordinate by introducing a novel magnitude-adaptive phase quantization framework. Compared with traditional CSI reporting, CQNet demonstrates superior CSI feedback efficiency and better CSI reconstruction accuracy.

54 citations

Journal ArticleDOI
TL;DR: A dedicated short-range communication/long-term evolution/WiFi-based vehicular system is developed to support the vehicle-to-vehicle and vehicle- to-pedestrian communication for the safety of vehicles and pedestrians and experiment results reveal that IEEE 802.11p based vehicle-To-Vehicle communication is unstable in the non-line-of-sight conditions.
Abstract: The dedicated short-range communication/wireless access for vehicular environment together with the fourth generation-long-term evolution technologies has been widely accepted as the most promising...

53 citations

Proceedings ArticleDOI
01 Oct 2015
TL;DR: A novel Pedestrian-Oriented Forewarning System (POFS) is proposed to protect the distracted vulnerable pedestrians and shows that POFS can alert the pedestrian using the efficient alert.
Abstract: As the popularity of the smartphone is increasing, the number of people getting involved in accidents with vehicles while using their smartphones is also increasing. Pedestrians divert their attention from walking because of the attraction of smartphone, which causes the accidents. It is critical to design a pedestrian-oriented system to alert the pedestrian smartphone users to imminent dangers while they are immersed in their smartphones. In this paper, a novel Pedestrian-Oriented Forewarning System (POFS) is proposed to protect the distracted vulnerable pedestrians. POFS divides the states of smartphone into four kinds: screen-centric state, voice-centric state, screen-voice state and silent state, and provides the adaptive alert mode on the basis of specific state. An on board unit (OBU) which can achieve IEEE 802.11p and the traditional Wi-Fi protocols is proposed to support the vehicle-to-vehicle (V2V) and vehicle-to-pedestrian (V2P) communication at the same time. An efficient collision estimation algorithm is proposed to provide reliable alert for pedestrian. Experimental studies show that POFS can alert the pedestrian using the efficient alert.

25 citations

Posted Content
TL;DR: Leveraging a Markovian model, a deep convolutional neural network (CNN)-based framework called MarkovNet is developed to efficiently encode CSI feedback to improve accuracy and efficiency and explores important physical insights including spherical normalization of input data and deep learning network optimizations in feedback compression.
Abstract: Forward channel state information (CSI) often plays a vital role in scheduling and capacity-approaching transmission optimization for massive multiple-input multiple-output (MIMO) communication systems. In frequency division duplex (FDD) massive MIMO systems, forwardlink CSI reconstruction at the transmitter relies critically on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent studies on the use of recurrent neural networks (RNNs) have demonstrated strong promises, though the cost of computation and memory remains high, for massive MIMO deployment. In this work, we exploit channel coherence in time to substantially improve the feedback efficiency. Using a Markovian model, we develop a deep convolutional neural network (CNN)-based framework MarkovNet to differentially encode forward CSI in time to effectively improve reconstruction accuracy. Furthermore, we explore important physical insights, including spherical normalization of input data and convolutional layers for feedback compression. We demonstrate substantial performance improvement and complexity reduction over the RNN-based work by our proposed MarkovNet to recover forward CSI estimates accurately. We explore additional practical consideration in feedback quantization, and show that MarkovNet outperforms RNN-based CSI estimation networks at a fraction of the computational cost.

17 citations


Cited by
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Journal ArticleDOI
16 Apr 2018-Sensors
TL;DR: This work discusses how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS.
Abstract: Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.

355 citations

Journal ArticleDOI
TL;DR: Some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches are presented, and promising research directions where ML is likely to make the biggest impact in the near future are pointed to.
Abstract: Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story – ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.

217 citations

Journal ArticleDOI
TL;DR: This paper explores the state-of-the-art of JRC in the levels of coexistence, cooperation, co-design and collaboration, and reviews the entire trends that drive the development of radar sensing and wireless communication using JRC.
Abstract: Joint radar and communication (JRC) technology has become important for civil and military applications for decades. This paper introduces the concepts, characteristics and advantages of JRC technology, presenting the typical applications that have benefited from JRC technology currently and in the future. This paper explores the state-of-the-art of JRC in the levels of coexistence, cooperation, co-design and collaboration. Compared to previous surveys, this paper reviews the entire trends that drive the development of radar sensing and wireless communication using JRC. Specifically, we explore an open research issue on radar and communication operating with mutual benefits based on collaboration, which represents the fourth stage of JRC evolution. This paper provides useful perspectives for future researches of JRC technology.

167 citations

Journal ArticleDOI
TL;DR: In this article, a multiple-rate compressive sensing neural network framework was proposed to compress and quantize the channel state information (CSI) in massive MIMO networks, which not only improves reconstruction accuracy but also decreases storage space at the UE.
Abstract: Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy. Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.

161 citations

Proceedings ArticleDOI
07 Jun 2020
TL;DR: Simulation results show that the proposed CRNet outperforms the state-of-the-art CsiNet under the same computational complexity without any extra information.
Abstract: In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS). However, the feedback becomes expensive with the growing complexity of CSI in massive MIMO system. Recently, deep learning (DL) approaches are used to improve the reconstruction efficiency of CSI feedback. In this paper, a novel feedback network named CRNet is proposed to achieve better performance via extracting CSI features on multiple resolutions. An advanced training scheme that further boosts the network performance is also introduced. Simulation results show that the proposed CRNet outperforms the state-of-the-art CsiNet under the same computational complexity without any extra information. The open source codes are available at https://github.com/Kylin9511/CRNet.

92 citations