Publications | Kunzan Liu

See Google Scholar for a full publication list.

2024

Spectral-temporal-spatial Customization via Modulating Multimodal Nonlinear Pulse Propagation

Tong Qiu, Honghao Cao, Kunzan Liu, Li-Yu Yu, Manuel Levy, Eva Lendaro, Fan Wang, and Sixian You

Nature Communications, 2024

@article{qiu2024spectral,
  title = {Spectral-temporal-spatial Customization via Modulating Multimodal Nonlinear Pulse Propagation},
  author = {Qiu, Tong and Cao, Honghao and Liu, Kunzan and Yu, Li-Yu and Levy, Manuel and Lendaro, Eva and Wang, Fan and You, Sixian},
  journal = {Nature Communications},
  volume = {15},
  number = {1},
  pages = {2031},
  year = {2024},
  publisher = {Nature Publishing Group UK London}
}

Deep and Dynamic Metabolic and Structural Imaging in Living Tissues

Kunzan Liu, Honghao Cao, Kasey Shashaty, Li-Yu Yu, Sarah Spitz, Francesca Michela Pramotton, Zhengpeng Wan, Ellen L. Kan, Erin N. Tevonian, Manuel Levy, Eva Lendaro, Roger D. Kamm, Linda G. Griffith, Fan Wang, Tong Qiu, and Sixian You

arXiv preprint, 2024

arXiv Bib Website

@article{liu2024deep,
  title = {Deep and Dynamic Metabolic and Structural Imaging in Living Tissues},
  author = {Liu, Kunzan and Cao, Honghao and Shashaty, Kasey and Yu, Li-Yu and Spitz, Sarah and Pramotton, Francesca Michela and Wan, Zhengpeng and Kan, Ellen L. and Tevonian, Erin N. and Levy, Manuel and Lendaro, Eva and Kamm, Roger D. and Griffith, Linda G. and Wang, Fan and Qiu, Tong and You, Sixian},
  journal = {arXiv preprint},
  year = {2024},
}

2023

Sensing RISs: Enabling Dimension-Independent CSI Acquisition for Beamforming

Jieao Zhu, Kunzan Liu, Zhongzhichao Wan, Linglong Dai, Tie Jun Cui, and H. Vincent Poor

IEEE Transactions on Information Theory, 2023

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Reconfigurable intelligent surfaces (RISs) are envisioned as a potentially transformative technology for future wireless communications. However, RISs’ inability to process signals and the attendant increased channel dimension have brought new challenges to RIS-assisted systems, including significantly increased pilot overhead required for channel estimation. To address these problems, several prior contributions that enhance the hardware architecture of RISs or develop algorithms to exploit the channels’ mathematical properties have been made, where the required pilot overhead is reduced to be proportional to the number of RIS elements. In this paper, we propose a dimension-independent channel state information (CSI) acquisition approach in which the required pilot overhead is independent of the number of RIS elements. Specifically, in contrast to traditional signal transmission methods, where signals from the base station (BS) and the users are transmitted in different time slots, we propose a novel method in which signals are transmitted from the BS and the user simultaneously during CSI acquisition. With this method, an electromagnetic interference random field (IRF) will be induced on the RIS, and we propose the structure of sensing RIS to capture its features. Moreover, we develop three algorithms for parameter estimation in this system, in which one of the proposed vM-EM algorithm is analyzed with the fixed-point perturbation method to obtain an asymptotic achievable bound. In addition, we also derive the Cramér-Rao lower bound (CRLB) and an asymptotic expression for characterizing the best possible performance of the proposed algorithms. Simulation results verify that our proposed signal transmission method and the corresponding algorithms can achieve dimension-independent CSI acquisition for beamforming.
@article{zhu2023sensing, title = {Sensing RISs: Enabling Dimension-Independent CSI Acquisition for Beamforming}, author = {Zhu, Jieao and Liu, Kunzan and Wan, Zhongzhichao and Dai, Linglong and Cui, Tie Jun and Poor, H. Vincent}, journal = {IEEE Transactions on Information Theory}, volume = {69}, number = {6}, pages = {3795--3813}, year = {2023}, publisher = {IEEE}, }

Learned, Uncertainty-driven Adaptive Acquisition for Photon-Efficient Multiphoton Microscopy

Cassandra Tong Ye, Jiashu Han, Kunzan Liu, Anastasios Angelopoulos, Linda Griffith, Kristina Monakhova, and Sixian You

arXiv preprint, 2023

arXiv Bib Website

@article{ye2023learned,
  title = {Learned, Uncertainty-driven Adaptive Acquisition for Photon-Efficient Multiphoton Microscopy},
  author = {Ye, Cassandra Tong and Han, Jiashu and Liu, Kunzan and Angelopoulos, Anastasios and Griffith, Linda and Monakhova, Kristina and You, Sixian},
  journal = {arXiv preprint},
  year = {2023},
}

2022

Compact User-Specific Reconfigurable Intelligent Surfaces for Uplink Transmission

Kunzan Liu, Zijian Zhang, Linglong Dai, and Lajos Hanzo

IEEE Transactions on Communications, 2022

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Large-scale antenna arrays employed by the base station (BS) constitute an essential next-generation communications technique. However, due to the constraints of size, cost, and power consumption, it is usually considered unrealistic to use a large-scale antenna array at the user side. Inspired by the emerging technique of reconfigurable intelligent surfaces (RIS), we firstly propose the concept of user-specific RIS (US-RIS) for facilitating the employment of a large-scale antenna array at the user side in a cost- and energy-efficient way. In contrast to the existing employments of RIS, which belong to the family of base-station-specific RISs (BSS-RISs), the US-RIS concept by definition facilitates the employment of RIS at the user side for the first time. This is achieved by conceiving a multi-layer structure to realize a compact form-factor. Furthermore, our theoretical results demonstrate that, in contrast to the existing single-layer structure, where only the phase of the signal reflected from RIS can be adjusted, the amplitude of the signal penetrating multi-layer US-RIS can also be partially controlled, which brings about a new degree of freedom (DoF) for beamformer design that can be beneficially exploited for performance enhancement. In addition, based on the proposed multi-layer US-RIS, we formulate the signal-to-noise ratio (SNR) maximization problem of US-RIS-aided communications. Due to the non-convexity of the problem introduced by this multi-layer structure, we propose a multi-layer transmit beamformer design relying on an iterative algorithm for finding the optimal solution by alternately updating each variable. Finally, our simulation results verify the superiority of the proposed multi-layer US-RIS as a compact realization of a large-scale antenna array at the user side for uplink transmission.
@article{liu2022compact, title = {Compact User-Specific Reconfigurable Intelligent Surfaces for Uplink Transmission}, author = {Liu, Kunzan and Zhang, Zijian and Dai, Linglong and Hanzo, Lajos}, journal = {IEEE Transactions on Communications}, volume = {70}, number = {1}, pages = {680--692}, year = {2022}, publisher = {IEEE}, }
Active Reconfigurable Intelligent Surface: Fully-Connected or Sub-Connected?

Kunzan Liu, Zijian Zhang, Linglong Dai, Shenheng Xu, and Fan Yang

IEEE Communications Letters, 2022

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To overcome the “multiplicative fading effect” introduced by passive reconfigurable intelligent surface (RIS), the concept of active RIS has been recently proposed to amplify the radiated signals. However, the existing fully-connected architecture of active RIS consumes high power due to the additionally integrated active components. To address this issue, we propose the sub-connected architecture of active RIS. Different from fully-connected architecture, where each element integrates a dedicated power amplifier, in the sub-connected architecture, multiple elements control their phase shifts independently but share a same power amplifier, which significantly reduces the number of power amplifiers for power saving at the cost of fewer degrees of freedom (DoFs) for beamforming design. Fortunately, our analysis reveals that performance loss introduced by the subconnected architecture is slight, indicating that it can achieve much higher energy efficiency (EE). Furthermore, we formulate the EE maximization problem in the active RIS-aided system for both architectures and develop a corresponding joint beamforming design. Simulation results verify the proposed sub-connected architecture as an energy-efficient realization of active RIS.
@article{liu2022active, title = {Active Reconfigurable Intelligent Surface: Fully-Connected or Sub-Connected?}, author = {Liu, Kunzan and Zhang, Zijian and Dai, Linglong and Xu, Shenheng and Yang, Fan}, journal = {IEEE Communications Letters}, volume = {26}, number = {1}, pages = {167--171}, year = {2022}, publisher = {IEEE}, }
Multiple-Sample Training for Neural Image Compression

Tongda Xu, Yan Wang, Dailan He, Chenjian Gao, Han Gao, Kunzan Liu, and Hongwei Qin

Advances in Neural Information Processing Systems (NIPS), 2022

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This paper considers the problem of lossy neural image compression (NIC). Current state-of-the-art (sota) methods adopt uniform posterior to approximate quantization noise, and single-sample pathwise estimator to approximate the gradient of evidence lower bound (ELBO). In this paper, we propose to train NIC with multiple-sample importance weighted autoencoder (IWAE) target, which is tighter than ELBO and converges to log likelihood as sample size increases. First, we identify that the uniform posterior of NIC has special properties, which affect the variance and bias of pathwise and score function estimators of the IWAE target. Moreover, we provide insights on a commonly adopted trick in NIC from gradient variance perspective. Based on those analysis, we further propose multiple-sample NIC (MS-NIC), an enhanced IWAE target for NIC. Experimental results demonstrate that it improves sota NIC methods. Our MS-NIC is plug-and-play, and can be easily extended to other neural compression tasks.
@article{xu2022multiple, title = {Multiple-Sample Training for Neural Image Compression}, author = {Xu, Tongda and Wang, Yan and He, Dailan and Gao, Chenjian and Gao, Han and Liu, Kunzan and Qin, Hongwei}, journal = {Advances in Neural Information Processing Systems (NIPS)}, volume = {35}, pages = {1502--1515}, year = {2022}, }

2021

Dimensionality-reduced Secure Outlier Detection on Union of Subspaces

Kunzan Liu, Yuchen Jiao, Ye Jin, Xu Xiang, and Yuantao Gu

IEEE International Conference on Cryptography, Security and Privacy (CSP), 2021

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In the problem of outlier detection (OD) on a union of subspaces (UoS), inliers are assumed to lie around a union of low-dimensional subspaces, and the goal is to detect the outliers that are not close to any of these subspaces. Among various algorithms, sparse self-representation-based ones have attracted much attention because of their theoretical performance guarantee. However, these algorithms need direct access to all raw data, and thus have poor data security and privacy protection capability. To solve this problem, in this paper we propose a new algorithm called dimensionality-reduced secure outlier detection (DrSOD), which uses random projection as a preprocessing step to avoid direct access to the raw data. We theoretically prove that DrSOD can correctly detect outliers with overwhelming probability under connectivity assumptions. In addition, the random projection step improves the computational efficiency of the algorithm. Experiments on synthetic and real-world datasets also demonstrate the effectiveness and efficiency of DrSOD.
@article{liu2021dimensionality, title = {Dimensionality-reduced Secure Outlier Detection on Union of Subspaces}, author = {Liu, Kunzan and Jiao, Yuchen and Jin, Ye and Xiang, Xu and Gu, Yuantao}, journal = {IEEE International Conference on Cryptography, Security and Privacy (CSP)}, pages = {101--106}, year = {2021}, publisher = {IEEE}, }
On The Energy-Efficiency Fairness of Reconfigurable Intelligent Surface-Aided Cell-Free Network

Kunzan Liu, and Zijian Zhang

IEEE Vehicular Technology Conference (VTC), 2021

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With the ability to overcome the inter-cell interferences, cell-free network is a promising network paradigm to achieve high spectrum efficiency for future 6G communications. However, the increasing number of distributed base stations in cell-free network introduces very high power consumption. To address this issue, inspired by the reconfigurable intelligent surface (RIS) technique with low power consumption, the concept of RIS-aided cell-free network has been recently proposed to improve the spectrum efficiency at a low cost of power consumption. In this paper, we take a further step to investigate the energy-efficiency fairness (EEF) of RIS-aided cell-free network. Specifically, we formulate the precoding design problem to maximize the energy efficiency of the worst user in a wideband RIS-aided cell-free network. To solve this problem, we then propose an iterative precoding algorithm by using Lagrangian transform and fractional programming (FP) to tackle the highly decoupled optimization objective. Through alternatingly solving subproblems of subcarrier assignment, power allocation, combining, and precoding, the objective will finally converge. Simulation results demonstrate the effectiveness of the proposed precoding algorithm, and the energy efficiency of users in cell-free network can be efficiently increased by the aid of RISs.
@article{liu2021on, title = {On The Energy-Efficiency Fairness of Reconfigurable Intelligent Surface-Aided Cell-Free Network}, author = {Liu, Kunzan and Zhang, Zijian}, journal = {IEEE Vehicular Technology Conference (VTC)}, pages = {1--6}, year = {2021}, publisher = {IEEE}, }
User-Side RIS: Realizing Large-Scale Array at User Side

Kunzan Liu, Zijian Zhang, and Linglong Dai

IEEE Global Communications Conference (GLOBECOM), 2021

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Massive multiple-input multiple-output (MIMO) with a large-scale antenna array at the base station (BS) side is one of the most essential techniques for 5G wireless communications. However, due to the forbidden hardware cost and mismatched size, it is physically limited to deploy massive MIMO at the user side. To break this limitation, inspired by the promising technique called reconfigurable intelligent surface (RIS), we firstly propose the concept of user-side RIS (US-RIS) which is a cost- and energy-efficient realization for large-scale array at the user side. Different from the existing RISs that work as base-station-side RISs (BSS-RISs), US-RIS is the first usage of RIS at the user side. Then, we propose a novel architecture of user with the aid of US-RIS with a multi-layer structure for compact implementation. Based on the proposed multi-layer US-RIS, we formulate the signal-to-noise ratio (SNR) maximization problem in the US-RIS-aided communications. To tackle the challenge of solving this non-convex problem, we propose a multi-layer precoding design that can obtain the optimal parameters at transceivers and US-RIS by iterative optimization. Finally, simulation results are shown to verify the practicability and superiorities of the proposed multi-layer US-RIS as a realization of the large-scale array at the user side.
@article{liu2021user, title = {User-Side RIS: Realizing Large-Scale Array at User Side}, author = {Liu, Kunzan and Zhang, Zijian and Dai, Linglong}, journal = {IEEE Global Communications Conference (GLOBECOM)}, pages = {1--6}, year = {2021}, publisher = {IEEE}, }