Table of Content

    25 March 2022, Volume 20 Issue 1
    Special Topic
    Recent Progress in Research and Development of Reconfigurable Intelligent Surface
    YUAN Yifei, GU Qi, WANG Anna, WU Dan, LI Ya
    2022, 20(1):  3-13.  doi:10.12142/ZTECOM.202201002
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    We aim to provide a comprehensive overview of the progress in research and development of the reconfigurable intelligent surface (RIS) over the last 2–3 years in this paper, especially when the RIS is used as relays in next-generation mobile networks. Major areas of research in academia are outlined, including fundamental performance, channel estimation, joint optimization with antenna precoding at base stations, propagation channel modeling and meta-material devices of RIS elements. Development in industry is surveyed from the aspects of performance potentials and issues, realistic joint optimization algorithms, control mechanisms, field trials and related activities in standardization development organizations (SDOs). Our views on how to carry out the engineering-aspect study on RIS for 6G systems are also presented, which cover the realistic performance, the comparison with other topological improvements, approaches for channel modeling, factors for designing control mechanisms and the timeline for RIS standardization.

    Some Observations and Thoughts about Reconfigurable Intelligent Surface Application for 5G Evolution and 6G
    HOU Xiaolin, LI Xiang, WANG Xin, CHEN Lan, SUYAMA Satoshi
    2022, 20(1):  14-20.  doi:10.12142/ZTECOM.202201003
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    Reconfigurable intelligent surface (RIS) is one of the hottest research topics for 5G evolution and 6G. It is expected that RIS can improve the system capacity and coverage with low cost and power consumption. This paper first discusses typical applications of RIS for 5G evolution and 6G, including RIS-aided smart channels and RIS-aided mega multiple-input multiple-output (MIMO). Then, several observations from RIS trials and system-level simulations are presented, especially those on the deployment strategy and the potential performance gain of RIS for coverage enhancement. The near-field effect and a two-step dynamic RIS beamforming method are also discussed. Finally, we summarize the challenges and opportunities of the RIS technology for 5G evolution and 6G, including hardware design, system and channel modeling, algorithm design and optimization, and standardization. We also suggest a step-by-step commercialization strategy as a conclusion.

    Recent Developments of Transmissive Reconfigurable Intelligent Surfaces: A Review
    TANG Junwen, XU Shenheng, YANG Fan, LI Maokun
    2022, 20(1):  21-27.  doi:10.12142/ZTECOM.202201004
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    Reconfigurable intelligent surface (RIS) is considered as one of the key technologies for the next-generation mobile communication systems. The transmissive RIS is able to achieve dynamic beamforming capability while transmitting an in-band RF signal through its aperture, and has promising prospects in various practical application scenarios. This paper reviews some of the latest developments of the transmissive RIS. The approaches for transmissive RIS designs are classified and described briefly. Numerous designs with different phase resolutions, such as 1-bit, 2-bit or continuous 360° phase shifts, are presented, with detailed discussions on their operating mechanisms and transmission performances. The design solutions for various transmissive RIS elements are summarized and compared.

    IRS‑Enabled Spectrum Sharing: Interference Modeling, Channel Estimation and Robust Passive Beamforming
    GUAN Xinrong, WU Qingqing
    2022, 20(1):  28-35.  doi:10.12142/ZTECOM.202201005
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    Intelligent reflecting surface (IRS), with its unique capability of smartly reconfiguring wireless channels, provides a new solution to improving spectrum efficiency, reducing energy consumption and saving deployment/hardware cost for future wireless networks. In this paper, IRS-enabled spectrum sharing is investigated, from the perspectives of interference modeling, efficient channel estimation and robust passive beamforming design. Specifically, we first characterize the interference in a spectrum sharing system consisting of a single primary user (PU) pair and a single secondary user (SU) pair, and extend it to the large-scale network by leveraging the Poisson point process (PPP). Then, we propose an efficient channel estimation framework based on decoupling the cascaded IRS channels. Moreover, the tradeoff between spectrum efficiency and energy efficiency is derived from the view of channel estimation accuracy. Finally, we discuss the robust passive beamforming design in presence of imperfect channel estimation and nonideal/discrete phase shifts. It is hoped that this paper provides useful guidance for unlocking the full potential of IRS for achieving efficient spectrum sharing for future wireless networks.

    Resource Allocation for Two‑Tier RIS‑Assisted Heterogeneous NOMA Networks
    XU Yongjun, YANG Zhaohui, HUANG Chongwen, YUEN Chau, GUI Guan
    2022, 20(1):  36-47.  doi:10.12142/ZTECOM.202201006
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    Reconfigurable intelligent surface (RIS) as a promising technology has been proposed to change weak communication environments. However, most of the current resource allocation (RA) schemes have focused on RIS-assisted homogeneous networks, and there is still no open works about RA schemes of RIS-assisted heterogeneous networks (HetNets). In this paper, we design an RA scheme for a RIS-assisted HetNet with non-orthogonal multiple access to improve spectrum efficiency and transmission rates. In particular, we jointly optimize the transmit power of the small-cell base station and the phase-shift matrix of the RIS to maximize the sum rates of all small-cell users, subject to the unit modulus constraint, the minimum signal-to-interference-plus-noise ratio constraint, and the cross-tier interference constraint for protecting communication quality of microcell users. An efficient suboptimal RA scheme is proposed based on the alternating iteration approach, and successive convex approximation and logarithmic transformation approach. Simulation results verify the effectiveness of the proposed scheme in terms of data rates.

    Markovian Cascaded Channel Estimation for RIS Aided Massive MIMO Using 1‑Bit ADCs and Oversampling
    SHAO Zhichao, YAN Wenjing, YUAN Xiaojun
    2022, 20(1):  48-56.  doi:10.12142/ZTECOM.202201007
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    A reconfigurable intelligent surface (RIS) aided massive multiple-input multiple-output (MIMO) system is considered, where the base station employs a large antenna array with low-cost and low-power 1-bit analog-to-digital converters (ADCs). To compensate for the performance loss caused by the coarse quantization, oversampling is applied at the receiver. The main challenge for the acquisition of cascaded channel state information in such a system is to handle the distortion caused by the 1-bit quantization and the sample correlation caused by oversampling. In this work, Bussgang decomposition is applied to deal with the coarse quantization, and a Markov chain is developed to characterize the banded structure of the oversampling filter. An approximate message-passing based algorithm is proposed for the estimation of the cascaded channels. Simulation results demonstrate that our proposed 1-bit systems with oversampling can approach the 2-bit systems in terms of the mean square error performance while the former consumes much less power at the receiver.

    RIS: Spatial‑Wideband Effect Analysis and Off‑Grid Channel Estimation
    JIAN Mengnan, ZHANG Nan, CHEN Yijian
    2022, 20(1):  57-62.  doi:10.12142/ZTECOM.202201008
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    As a critical candidate technology for 5G-advanced and 6G, reconfigurable intelligent surfaces (RIS) have received extensive attention from academia and industry. RIS has the promising features of passiveness, reconfigurable ability, and low cost. RIS channel estimation faces the challenges of high matrix dimension, passive estimation, and spatial-wideband effect. In this article, we analyze the impact of the spatial-wideband effect on the RIS channel to account for the propagation delay across RIS elements and estimate sparse channel parameters such as angle and gain through a super-resolution compressive sensing (CS) algorithm. The simulation results explore the influence of the spatial-wideband effect on the RIS channel and verify the effectiveness of the proposed algorithm.

    Dual‑Polarized RIS‑Based STBC Transmission with Polarization Coupling Analysis
    ZHOU Mingyong, CHEN Xiangyu, TANG Wankai, KE Jun Chen, JIN Shi, CHENG Qiang, CUI Tie Jun
    2022, 20(1):  63-75.  doi:10.12142/ZTECOM.202201009
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    The rapid development of the reconfigurable intelligent surface (RIS) technology has given rise to a new paradigm of wireless transmitters. At present, most research works on RIS-based transmitters focus on single-polarized RISs. In this paper, we propose a dual-polarized RIS-based transmitter, which realizes 4-transmit space-time block coding (STBC) transmission by properly partitioning RIS’s unit cells and utilizing the degree of freedom of polarization. The proposed scheme is evaluated through a prototype system that utilizes a fabricated dual-polarized phase-adjustable RIS. In particular, the polarization coupling phenomenon in each unit cell of the employed dual-polarized RIS is modeled and analyzed. The experimental results are in good agreement with the theoretical modeling and analysis results, and an initial research effort is made on characterizing the polarization coupling property in the dual-polarized RIS.

    Research Paper
    Metric Learning for Semantic‑Based Clothes Retrieval
    YANG Bo, GUO Caili, LI Zheng
    2022, 20(1):  76-82.  doi:10.12142/ZTECOM.202201010
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    Existing clothes retrieval methods mostly adopt binary supervision in metric learning. For each iteration, only the clothes belonging to the same instance are positive samples, and all other clothes are “indistinguishable” negative samples, which causes the following problem. The relevance between the query and candidates is only treated as relevant or irrelevant, which makes the model difficult to learn the continuous semantic similarities between clothes. Clothes that do not belong to the same instance are completely considered irrelevant and are uniformly pushed away from the query by an equal margin in the embedding space, which is not consistent with the ideal retrieval results. Motivated by this, we propose a novel method called semantic-based clothes retrieval (SCR). In SCR, we measure the semantic similarities between clothes and design a new adaptive loss based on these similarities. The margin in the proposed adaptive loss can vary with different semantic similarities between the anchor and negative samples. In this way, more coherent embedding space can be learned, where candidates with higher semantic similarities are mapped closer to the query than those with lower ones. We use Recall@K and normalized Discounted Cumulative Gain (nDCG) as evaluation metrics to conduct experiments on the DeepFashion dataset and have achieved better performance.