arXiv subjects

Chengzhi Ma

Publications and source records attributed to Chengzhi Ma.

9 recordsLinked to original sources

arXiv2026

A New Paradigm Towards Reconfigurable Environment: Reconfigurable Distributed Antennas and Reflecting Surface

Reconfigurable distributed antennas and reflecting surface (RDARS) has emerged as a transformative solution to address the stringent requirements of future wireless networks. By combining distributed active antennas with reconfigurable passive reflecting surfaces, RDARS integrates the advantages of both active transmission and passive wave control in a cost-effective and energy-efficient manner. This hybrid architecture enables enhanced coverage, improved spectral efficiency, and seamless support for integrated communication and sensing. In this article, we first introduce the fundamental architecture and working principles of RDARS, followed by practical benefits and comparisons with recently proposed intelligent surface variants. We then highlight the signal-to-noise ratio (SNR) gains in representative applications of RDARS-aided communication and sensing scenarios, where RDARS demonstrates clear advantages over conventional reconfigurable intelligent surfaces. Finally, we outline key challenges related to practical implementation and resource allocation, and discuss potential research directions. With its unique hybrid mode synergy, RDARS is envisioned to play a pivotal role in shaping the evolution of next-generation intelligent communication systems.

cs.IT↗

arXiv2026

Dirac branch-cut modes with relativistic transport

Emergent Dirac fields, exhibiting effective relativistic physics, are most commonly associated with bulk and surface states in materials such as graphene and topological insulators. Here we identify a previously unexplored class of Dirac states that propagate along branch-cut defects in a complex Dirac mass field, unlike the well-known Jackiw-Rebbi and Jackiw-Rossi states localized at domain-wall and vortex defects. These traveling-wave defect states, termed Dirac branch-cut (DBC) modes, obey an effective one-dimensional relativistic Dirac equation with a reduced mass determined by the phase difference across the branch cut. Using acoustic metamaterials, we experimentally demonstrate a range of relativistic phenomena exhibited by DBC modes, including relativistic dispersion, energy-independent confinement, Klein tunnelling, and transport along freeform (e.g., spiral) trajectories. Our results establish branch-cut defects as a distinct mechanism for Dirac defect states beyond domain walls and vortices, and extend relativistic Dirac physics from bulk and surface states to propagating modes confined to defect boundaries.

physics.optics↗

arXiv2025

Biochemical Computing Mode for Sequential Logic

Recent years have witnessed the growing scholarly interest in the next-generation general-purpose computers. Various innovative computing modes have been proposed, such as optical, quantum phenomena, and DNA-based modes. Sequential logic circuits are a critical factor that enables these modes to function as general-purpose computers, given their essential role in facilitating continuous computation and memory storage through their ability to store states. However, compared to computability, it is often overlooked due to the difficulty of its implementation. In this paper, we first demonstrate sequential mapping, a crucial necessary condition for electronic computers to realize sequential logic circuits, and highlight this distinctive property of general-purpose computers in the context of logic gate circuits. To achieve computational functionalities comparable to those of electronic computers, we utilize the control effect of enzymes on enzymatic reactions to design a logic gate model that is composed of small molecules and driven by enzymes, subsequently propose a biochemical computing mode. Furthermore, we mathematically analyze the static and dynamic input-output properties of biochemical logic gate components and prove that the biochemical computing mode satisfies sequential mapping similar to electronic computers. When combined with the storage characteristics of NOT-AND gates, it can realize sequential logic circuits. The findings can serve as a theoretical foundation for developing general-purpose biochemical computers.

cs.ET↗

arXiv2025

DPF-Net: Physical Imaging Model Embedded Data-Driven Underwater Image Enhancement

Due to the complex interplay of light absorption and scattering in the underwater environment, underwater images experience significant degradation. This research presents a two-stage underwater image enhancement network called the Data-Driven and Physical Parameters Fusion Network (DPF-Net), which harnesses the robustness of physical imaging models alongside the generality and efficiency of data-driven methods. We first train a physical parameter estimate module using synthetic datasets to guarantee the trustworthiness of the physical parameters, rather than solely learning the fitting relationship between raw and reference images by the application of the imaging equation, as is common in prior studies. This module is subsequently trained in conjunction with an enhancement network, where the estimated physical parameters are integrated into a data-driven model within the embedding space. To maintain the uniformity of the restoration process amid underwater imaging degradation, we propose a physics-based degradation consistency loss. Additionally, we suggest an innovative weak reference loss term utilizing the entire dataset, which alleviates our model's reliance on the quality of individual reference images. Our proposed DPF-Net demonstrates superior performance compared to other benchmark methods across multiple test sets, achieving state-of-the-art results. The source code and pre-trained models are available on the project home page: https://github.com/OUCVisionGroup/DPF-Net.

cs.CV↗

arXiv2025

Depth-Assisted Network for Indiscernible Marine Object Counting with Adaptive Motion-Differentiated Feature Encoding

Indiscernible marine object counting encounters numerous challenges, including limited visibility in underwater scenes, mutual occlusion and overlap among objects, and the dynamic similarity in appearance, color, and texture between the background and foreground. These factors significantly complicate the counting process. To address the scarcity of video-based indiscernible object counting datasets, we have developed a novel dataset comprising 50 videos, from which approximately 800 frames have been extracted and annotated with around 40,800 point-wise object labels. This dataset accurately represents real underwater environments where indiscernible marine objects are intricately integrated with their surroundings, thereby comprehensively illustrating the aforementioned challenges in object counting. To address these challenges, we propose a depth-assisted network with adaptive motion-differentiated feature encoding. The network consists of a backbone encoding module and three branches: a depth-assisting branch, a density estimation branch, and a motion weight generation branch. Depth-aware features extracted by the depth-assisting branch are enhanced via a depth-enhanced encoder to improve object representation. Meanwhile, weights from the motion weight generation branch refine multi-scale perception features in the adaptive flow estimation module. Experimental results demonstrate that our method not only achieves state-of-the-art performance on the proposed dataset but also yields competitive results on three additional video-based crowd counting datasets. The pre-trained model, code, and dataset are publicly available at https://github.com/OUCVisionGroup/VIMOC-Net.

cs.CV↗

arXiv2025

Joint Beamforming Optimization and Mode Selection for RDARS-Aided MIMO Systems

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective solution for green communications in 6G. However, its further extensive use has been greatly limited due to its fully passive characteristics. Considering the appealing distribution gains of distributed antenna systems (DAS), a flexible reconfigurable architecture called reconfigurable distributed antenna and reflecting surface (RDARS) is proposed. RDARS encompasses DAS and RIS as two special cases and maintains the advantages of distributed antennas while reducing the hardware cost by replacing some active antennas with low-cost passive reflecting surfaces. In this paper, we present a RDARS-aided uplink multi-user communication system and investigate the system transmission reliability with the newly proposed architecture. Specifically, in addition to the distribution gain and the reflection gain provided by the connection and reflection modes, respectively, we also consider the dynamic mode switching of each element which introduces an additional degree of freedom (DoF) and thus results in a selection gain. As such, we aim to minimize the total sum mean-square-error (MSE) of all data streams by jointly optimizing the receive beamforming matrix, the reflection phase shifts and the channel-aware placement of elements in the connection mode. Numerical results demonstrate the superiority of the proposed architecture compared to the conventional fully passive RIS or DAS. Furthermore, some insights about the practical implementation of RDARS are provided.

cs.IT↗

arXiv2024

Robust Beamforming Design and Antenna Selection for Dynamic HRIS-aided MISO System

In this paper, we propose a dynamic hybrid active-passive reconfigurable intelligent surface (HRIS) to enhance multiple-input-single-output (MISO) communications, leveraging the property of dynamically placing active elements. Specifically, considering the impact of hardware impairments (HWIs), we investigate channel-aware configurations of the receive antennas at the base station (BS) and the active/passive elements at the HRIS to improve transmission reliability. To this end, we address the average mean-square-error (MSE) minimization problem for the HRIS-aided MISO system by jointly optimizing the BS receive antenna selection matrix, the reflection phase coefficients, the reflection amplitude matrix, and the mode selection matrix of the HRIS. To overcome the non-convexity and intractability of this problem, we first transform the binary and discrete variables into continuous ones, and then propose a penalty-based exact block coordinate descent (PEBCD) algorithm to alternately solve these subproblems. Numerical simulations demonstrate the significant superiority of our proposed scheme over conventional benchmark schemes.

cs.IT↗

arXiv2023

RDARS Empowered Massive MIMO System: Two-Timescale Transceiver Design with Imperfect CSI

In this paper, we investigate a novel reconfigurable distributed antennas and reflecting surface (RDARS) aided multi-user massive MIMO system with imperfect CSI and propose a practical two-timescale (TTS) transceiver design to reduce the communication overhead and computational complexity of the system. In the RDARS-aided system, not only distribution gain but also reflection gain can be obtained by a flexible combination of the distributed antennas and reflecting surface, which differentiates the system from the others and also makes the TTS design challenging. To enable the optimal TTS transceiver design, the achievable rate of the system is first derived in closed-form. Then the TTS design aiming at the weighted sum rate maximization is considered. To solve the challenging non-convex optimization problem with high-order design variables, i.e., the transmit powers and the phase shifts at the RDARS, a block coordinate descent based method is proposed to find the optimal solutions in semi-closed forms iteratively. Specifically, two efficient algorithms are proposed with provable convergence for the optimal phase shift design, i.e., Riemannian Gradient Ascent based algorithm by exploiting the unit-modulus constraints, and Two-Tier Majorization-Minimization based algorithm with closed-form optimal solutions in each iteration. Simulation results validate the effectiveness of the proposed algorithm and demonstrate the superiority of deploying RDARS in massive MIMO systems to provide substantial rate improvement with a significantly reduced total number of active antennas/RF chains and lower transmit power when compared to the DAS and RIS-aided systems.

cs.IT↗

arXiv2023

Reconfigurable Distributed Antennas and Reflecting Surface: A New Architecture for Wireless Communications

Distributed Antenna Systems (DASs) employ multiple antenna arrays in remote radio units to achieve highly directional transmission and provide great coverage performance for future-generation networks. However, the utilization of active antenna arrays results in a significant increase in hardware costs and power consumption for DAS. To address these issues, integrating DAS with Reconfigurable Intelligent Surfaces (RIS) offers a viable approach to ensure transmission performance while maintaining low hardware costs and power consumption. To incorporate the merits of RIS into the DAS from practical consideration, a novel architecture of ``Reconfigurable Distributed Antennas and Reflecting Surfaces (RDARS)'' is proposed in this paper. Specifically, based on the design of the additional direct-through state together with the existing high-quality fronthaul link, any element of the RDARS can be dynamically programmed to connect with the base station (BS) via fibers and perform the connected mode as remote distributed antennas of the BS to receive or transmit signals. Additionally, RDARS also inherits the low-cost and low-energy-consumption benefits of fully passive RISs by default configuring the elements as passive to perform the reflection mode. As a result, RDARS offers flexible control over the trade-off between distribution gain and reflection gain to enhance performance. The ergodic achievable rate under the RDARS architecture is analyzed and closed-form expression with meaningful insights is derived. The theoretical analysis and simulation results prove that the RDARS achieves a higher achievable rate than both DAS and RIS. A RDARS prototype with 256 elements is built for real experiments which shows that the RDARS-aided system can achieve an additional 21% and 170% throughput improvement over DAS and RIS-aided systems, respectively.

cs.IT↗