arXiv subjects

Chongwen Huang

Publications and source records attributed to Chongwen Huang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Electromagnetic Channel Modeling and Capacity Analysis for HMIMO Communications

Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of these systems, it is crucial to develop a universal EM-compliant channel model. This paper addresses this necessity by proposing a comprehensive EM channel model tailored for realistic multi-path environments, accounting for the combined effects of antenna array configurations and propagation conditions in HMIMO communications. Both polarization phenomena and spatial correlation are incorporated into this probabilistic channel model. Additionally, physical constraints of antenna configurations, such as mutual coupling effects and energy consumption, are integrated into the channel modeling framework. Simulation results validate the effectiveness of the proposed probabilistic channel model, indicating that traditional Rician and Rayleigh fading models cannot accurately depict the channel characteristics and underestimate the channel capacity. More importantly, the proposed channel model outperforms free-space Green's functions in accurately depicting both near-field gain and multi-path effects in radiative near-field regions. These gains are much more evident in tri-polarized systems, highlighting the necessity of polarization interference elimination techniques. Moreover, the theoretical analysis accurately verifies that capacity decreases with expanding communication regions of two-user communications.

cs.IT↗

arXiv2025

Device-Free 3D Drone Localization in RIS-Assisted mmWave MIMO Networks

In this paper, we investigate the potential of reconfigurable intelligent surfaces (RISs) in facilitating passive/device-free three-dimensional (3D) drone localization within existing cellular infrastructure operating at millimeter-wave (mmWave) frequencies and employing multiple antennas at the transceivers. The developed localization system operates in the bi-static mode without requiring direct communication between the drone and the base station. We analyze the theoretical performance limits via Fisher information analysis and Cramér Rao lower bounds (CRLBs). Furthermore, we develop a low-complexity yet effective drone localization algorithm based on coordinate gradient descent and examine the impact of factors such as radar cross section (RCS) of the drone and training overhead on system performance. It is demonstrated that integrating RIS yields significant benefits over its RIS-free counterpart, as evidenced by both theoretical analyses and numerical simulations.

eess.SP↗

arXiv2025

Movable Antenna-Assisted Integrated Sensing and Communication Systems

Movable antennas (MAs) enhance flexibility in beamforming gain and interference suppression by adjusting position within certain areas of the transceivers. In this paper, we propose an MA-assisted integrated sensing and communication framework, wherein MAs are deployed for reconfiguring the channel array responses at both the receiver and transmitter of a base station. Then, we develop an optimization framework aimed at maximizing the sensing signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the receive beamforming vector, the transmit beamforming matrix, and the positions of MAs while meeting the minimum SINR requirement for each user. To address this nonconvex problem involving complex coupled variables, we devise an alternating optimization-based algorithm that incorporates techniques including the Charnes-Cooper transform, second-order Taylor expansion, and successive convex approximation (SCA). Specifically, the closed form of the received vector and the optimal transmit matrix can be first obtained in each iteration. Subsequently, the solutions for the positions of the transmit and receive MAs are obtained using the SCA method based on the second-order Taylor expansion. The simulation results show that the proposed scheme has significant advantages over the other baseline schemes. In particular, the proposed scheme has the ability to match the performance of the fixed position antenna scheme while utilizing fewer resources.

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arXiv2024

Generative AI Empowered Semantic Feature Multiple Access (SFMA) Over Wireless Networks

This paper investigates a novel generative artificial intelligence (GAI) empowered multi-user semantic communication system called semantic feature multiple access (SFMA) for video transmission, which comprises a base station (BS) and paired users. The BS generates and combines semantic information of several frames simultaneously requested by paired users into a single signal. Users recover their frames from this combined signal and input the recovered frames into a GAI-based video frame interpolation model to generate the intermediate frame. To optimize transmission rates and temporal gaps between simultaneously transmitted frames, we formulate an optimization problem to maximize the system sum rate while minimizing temporal gaps. Since the standard signal-to-interference-plus-noise ratio (SINR) equation does not accurately capture the performance of our semantic communication system, we introduce a weight parameter into the SINR equation to better represent the system's performance. Due to its dependence on transmit power, we propose a three-step solution. First, we develop a user pairing algorithm that pairs two users with the highest preference value, a weighted combination of semantic transmission rate and temporal gap. Second, we optimize inter-group power allocation by formulating an optimization problem that allocates proper transmit power across all user groups to maximize system sum rates while satisfying each user's minimum rate requirement. Third, we address intra-group power allocation to enhance each user's performance. Simulation results demonstrate that our method improves transmission rates by up to 24.8%, 45.8%, and 66.1% compared to fixed-power non-orthogonal multiple access (F-NOMA), orthogonal joint source-channel coding (O-JSCC), and orthogonal frequency division multiple access (OFDMA), respectively.

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arXiv2024

Electromagnetic Information Theory for Holographic MIMO Communications

Holographic multiple-input multiple-output (HMIMO) utilizes a compact antenna array to form a nearly continuous aperture, thereby enhancing higher capacity and more flexible configurations compared with conventional MIMO systems, making it attractive in current scientific research. Key questions naturally arise regarding the potential of HMIMO to surpass Shannon's theoretical limits and how far its capabilities can be extended. However, the traditional Shannon information theory falls short in addressing these inquiries because it only focuses on the information itself while neglecting the underlying carrier, electromagnetic (EM) waves, and environmental interactions. To fill up the gap between the theoretical analysis and the practical application for HMIMO systems, we introduce electromagnetic information theory (EIT) in this paper. This paper begins by laying the foundation for HMIMO-oriented EIT, encompassing EM wave equations and communication regions. In the context of HMIMO systems, the resultant physical limitations are presented, involving Chu's limit, Harrington's limit, Hannan's limit, and the evaluation of coupling effects. Field sampling and HMIMO-assisted oversampling are also discussed to guide the optimal HMIMO design within the EIT framework. To comprehensively depict the EM-compliant propagation process, we present the approximate and exact channel modeling approaches in near-/far-field zones. Furthermore, we discuss both traditional Shannon's information theory, employing the probabilistic method, and Kolmogorov information theory, utilizing the functional analysis, for HMIMO-oriented EIT systems.

cs.IT↗

arXiv2024

Joint Beamforming Optimization for Active STAR-RIS-Assisted ISAC Systems

In this paper, we investigate an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted integrated sensing and communications (ISAC) system, where the dual-function base station (DFBS) operates in full-duplex (FD) mode to provide communication services and performs targets sensing simultaneously. Meanwhile, we consider multiple targets and multiple users scenario as well as the self-interference at the FD DFBS. Through jointly optimizing the DFBS and active STAR-RIS beamforming under different work modes, our purpose is to achieve the maximum communication sum-rate, while satisfying the minimum radar signal-to-interference-plus-noise ratio (SINR) constraint, the active STAR-RIS hardware constraints and the total power constraint of DFBS and active STAR-RIS. To tackle the complex non-convex optimization problem formulated, an efficient alternating optimization algorithm is proposed. Specifically, the fractional programming method is first leveraged to turn the original problem into a more tractable one, and subsequently the transformed problem is decomposed into several sub-problems. Next, we develop a derivation method to obtain the closed-form expression of the radar receiving beamforming, and then the DFBS transmit beamforming is optimized under the radar SINR requirement and total power constraints. After that, the active STAR-RIS reflection and transmission beamforming are optimized by majorization minimization, complex circle manifold and convex optimization techniques. Finally, the proposed schemes are conducted through numerical simulations to show their benefits and efficiency.

cs.IT↗

arXiv2024

Electromagnetic Modeling and Capacity Analysis of Rydberg Atom-Based MIMO System

Rydberg atom-based antennas exploit the quantum properties of highly excited Rydberg atoms, providing unique advantages over classical antennas, such as high sensitivity, broad frequency range, and compact size. Despite the increasing interests in their applications in antenna and communication engineering, two key properties, involving the lack of polarization multiplexing and isotropic reception without mutual coupling, remain unexplored in the analysis of Rydberg atom-based spatial multiplexing, i.e., multiple-input and multiple-output (MIMO), communications. Generally, the design considerations for any antenna, even for atomic ones, can be extracted to factors such as radiation patterns, efficiency, and polarization, allowing them to be seamlessly integrated into existing system models. In this letter, we extract the antenna properties from relevant quantum characteristics, enabling electromagnetic modeling and capacity analysis of Rydberg MIMO systems in both far-field and near-field scenarios. By employing ray-based method for far-field analysis and dyadic Green's function for near-field calculation, our results indicate that Rydberg atom-based antenna arrays offer specific advantages over classical dipole-type arrays in single-polarization MIMO communications.

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arXiv2024

Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate

How to reduce the pilot overhead required for channel estimation? How to deal with the channel dynamic changes and error propagation in channel prediction? To jointly address these two critical issues in next-generation transceiver design, in this paper, we propose a novel framework named channel deduction for high-dimensional channel acquisition in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. Specifically, it makes use of the outdated channel information of past time slots, performs coarse estimation for the current channel with a relatively small number of pilots, and then fuses these two information to obtain a complete representation of the present channel. The rationale is to align the current channel representation to both the latent channel features within the past samples and the coarse estimate of current channel at the pilots, which, in a sense, behaves as a complementary combination of estimation and prediction and thus reduces the overall overhead. To fully exploit the highly nonlinear correlations in time, space, and frequency domains, we resort to learning-based implementation approaches. By using the highly efficient complex-domain multilayer perceptron (MLP)-mixer for crossing space-frequency domain representation and the recurrence-based or attention-based mechanisms for the past-present interaction, we respectively design two different channel deduction neural networks (CDNets). We provide a general procedure of data collection, training, and deployment to standardize the application of CDNets. Comprehensive experimental evaluations in accuracy, robustness, and efficiency demonstrate the superiority of the proposed approach, which reduces the pilot overhead by up to 88.9% compared to state-of-the-art estimation approaches and enables continuous operating even under unknown user movement and error propagation.

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arXiv2024

Multi-hop RIS-aided Learning Model Sharing for Urban Air Mobility

Urban Air Mobility (UAM), powered by flying cars, is poised to revolutionize urban transportation by expanding vehicle travel from the ground to the air. This advancement promises to alleviate congestion and enable faster commutes. However, the fast travel speeds mean vehicles will encounter vastly different environments during a single journey. As a result, onboard learning systems need access to extensive environmental data, leading to high costs in data collection and training. These demands conflict with the limited in-vehicle computing and battery resources. Fortunately, learning model sharing offers a solution. Well-trained local Deep Learning (DL) models can be shared with other vehicles, reducing the need for redundant data collection and training. However, this sharing process relies heavily on efficient vehicular communications in UAM. To address these challenges, this paper leverages the multi-hop Reconfigurable Intelligent Surface (RIS) technology to improve DL model sharing between distant flying cars. We also employ knowledge distillation to reduce the size of the shared DL models and enable efficient integration of non-identical models at the receiver. Our approach enhances model sharing and onboard learning performance for cars entering new environments. Simulation results show that our scheme improves the total reward by 85% compared to benchmark methods.

cs.NI↗

arXiv2024

Electromagnetic Hybrid Beamforming for Holographic Communications

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover the whole space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) scheme based on a three-dimensional (3D) superdirective holographic antenna array. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves the real-time adjustment of the radiation pattern to adapt it to the dynamic wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beamforming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation patterns and the mutual coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results demonstrate that our proposed EHB scheme with a 3D holographic array achieves a relatively flat superdirective beamforming gain and allows for programmable focusing directions throughout the entire spatial domain. Furthermore, they also verify that the proposed scheme achieves a sum rate gain of over 150% compared to traditional beamforming algorithms.

cs.IT↗

arXiv2024

E-Healthcare Systems: Integrated Sensing, Computing, and Semantic Communication with Physical Layer Security

This paper introduces an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for smart healthcare systems. The framework is evaluated in the context of smart healthcare, optimising the transmit beamforming matrix and semantic extraction ratio for improved data rates, sensing accuracy, and general data protection regulation (GDPR) compliance, while considering IoRT device computing capabilities. Semantic metrics such as semantic transmission rate and semantic secrecy rate are derived to evaluate data rate performance and GDPR risk, respectively, while the Cramér-Rao Bound (CRB) assesses sensing performance. Simulation results demonstrate the framework's effectiveness in ensuring reliable sensing, high data rates, and secure communication.

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arXiv2024

RIS-aided Trajectory Optimization in Layered Urban Air Mobility

Urban Air Mobility (UAM) relies on developing aerospace industries, where safe aviation and efficient communication are critical features of aircraft. However, it is challenging for aircraft to sustain efficient air-ground communication in urban circumstances. Without continuous air-ground communication, aircraft may experience course deviation and safety accidents. To address these problems, a reconfigurable intelligent surface(RIS)-aided trajectory optimization scheme is proposed enabling efficient air-ground communication and safe aviation in UAM with a layered airspace structure. This paper first devises a dual-plane RIS communication scheme for layered airspace. It fully engages the omnidirectional and directional signal attributes to reduce the transmission delay of the air-ground communication. Based on the dual-plane RIS configuration, we jointly develop the intra- and inter-layer trajectory scheme to optimize communication and safe aviation. In the intra-layer trajectory optimization, we propose a dual-time-scale flight scheme to improve communication capacity and horizontal flight safety. Meanwhile, we propose a safe layer-switching method to ensure collision avoidance during vertical flight in the inter-layer trajectory optimization. The communication load of the proposed scheme can be improved 40% and the time of safe separation restoration can be lessened 66% compared with the benchmarks in the layered airspace.

cs.CE↗

arXiv2024

Electromagnetic Normalization of Channel Matrix for Holographic MIMO Communications

Holographic multiple-input and multiple-output (MIMO) communications introduce innovative antenna array configurations, such as dense arrays and volumetric arrays, which offer notable advantages over conventional planar arrays with half-wavelength element spacing. However, accurately assessing the performance of these new holographic MIMO systems necessitates careful consideration of channel matrix normalization, as it is influenced by array gain, which, in turn, depends on the array topology. Traditional normalization methods may be insufficient for assessing these advanced array topologies, potentially resulting in misleading or inaccurate evaluations. In this study, we propose electromagnetic normalization approaches for the channel matrix that accommodate arbitrary array topologies, drawing on the array gains from analytical, physical, and full-wave methods. Additionally, we introduce a normalization method for near-field MIMO channels based on a rigorous dyadic Green's function approach, which accounts for potential losses of gain at near field. Finally, we perform capacity analyses under quasi-static, ergodic, and near-field conditions, through adopting the proposed normalization techniques. Our findings indicate that channel matrix normalization should reflect the realized gains of the antenna array in target directions. Failing to accurately normalize the channel matrix can result in errors when evaluating the performance limits and benefits of unconventional holographic array topologies, potentially compromising the optimal design of holographic MIMO systems.

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arXiv2024

Multi-Sources Fusion Learning for Multi-Points NLOS Localization in OFDM System

Accurate localization of mobile terminals is a pivotal aspect of integrated sensing and communication systems. Traditional fingerprint-based localization methods, which infer coordinates from channel information within pre-set rectangular areas, often face challenges due to the heterogeneous distribution of fingerprints inherent in non-line-of-sight (NLOS) scenarios, particularly within orthogonal frequency division multiplexing systems. To overcome this limitation, we develop a novel multi-sources information fusion learning framework referred to as the Autosync Multi-Domains NLOS Localization (AMDNLoc). Specifically, AMDNLoc employs a two-stage matched filter fused with a target tracking algorithm and iterative centroid-based clustering to automatically and irregularly segment NLOS regions, ensuring uniform distribution within channel state information across frequency, power, and time-delay domains. Additionally, the framework utilizes a segment-specific linear classifier array, coupled with deep residual network-based feature extraction and fusion, to establish the correlation function between fingerprint features and coordinates within these regions. Simulation results reveal that AMDNLoc achieves an impressive NLOS localization accuracy of 1.46 meters on typical wireless artificial intelligence research datasets and demonstrates significant improvements in interpretability, adaptability, and scalability.

cs.IT↗

arXiv2024

Exploring Hannan Limitation for 3D Antenna Array

Hannan Limitation successfully links the directivity characteristics of 2D arrays with the aperture gain limit, providing the radiation efficiency upper limit for large 2D planar antenna arrays. This demonstrates the inevitable radiation efficiency degradation caused by mutual coupling effects between array elements. However, this limitation is derived based on the assumption of infinitely large 2D arrays, which means that it is not an accurate law for small-size arrays. In this paper, we extend this theory and propose an estimation formula for the radiation efficiency upper limit of finite-sized 2D arrays. Furthermore, we analyze a 3D array structure consisting of two parallel 2D arrays. Specifically, we provide evaluation formulas for the mutual coupling strengths for both infinite and finite size arrays and derive the fundamental efficiency limit of 3D arrays. Moreover, based on the established gain limit of antenna arrays with fixed aperture sizes, we derive the achievable gain limit of finite size 3D arrays. Besides the performance analyses, we also investigate the spatial radiation characteristics of the considered 3D array structure, offering a feasible region for 2D phase settings under a given energy attenuation threshold. Through simulations, we demonstrate the effectiveness of our proposed theories and gain advantages of 3D arrays for better spatial coverage under various scenarios.

cs.IT↗

arXiv2024

Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method.

cs.IT↗

arXiv2024

Robust Continuous-Time Beam Tracking with Liquid Neural Network

Millimeter-wave (mmWave) technology is increasingly recognized as a pivotal technology of the sixth-generation communication networks due to the large amounts of available spectrum at high frequencies. However, the huge overhead associated with beam training imposes a significant challenge in mmWave communications, particularly in urban environments with high background noise. To reduce this high overhead, we propose a novel solution for robust continuous-time beam tracking with liquid neural network, which dynamically adjust the narrow mmWave beams to ensure real-time beam alignment with mobile users. Through extensive simulations, we validate the effectiveness of our proposed method and demonstrate its superiority over existing state-of-the-art deep-learning-based approaches. Specifically, our scheme achieves at most 46.9% higher normalized spectral efficiency than the baselines when the user is moving at 5 m/s, demonstrating the potential of liquid neural networks to enhance mmWave mobile communication performance.

cs.IT↗

arXiv2024

Green Probabilistic Semantic Communication over Wireless Networks

In this paper, we propose a multi-user green semantic communication system facilitated by a probabilistic knowledge graph (PKG). By integrating probability into the knowledge graph, we enable probabilistic semantic communication (PSC) and represent semantic information accordingly. On this basis, a semantic compression model designed for multi-user downlink task-oriented communication is introduced, utilizing the semantic compression ratio (SCR) as a parameter to connect the computation and communication processes of information transmission. Based on the rate-splitting multiple access (RSMA) technology, we derive mathematical expressions for system transmission energy consumption and related formulations. Subsequently, the multi-user green semantic communication system is modeled and the optimal problem with the goal of minimizing system energy consumption comprehensively considering the computation and communication process under given constrains is formulated. In order to address the optimal problem, we propose an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design, semantic compression ratio, and computation capacity allocation. Simulation results validate the effectiveness of our approach, demonstrating the superiority of our system over methods using Space Division Multiple Access (SDMA) and non-orthogonal multiple access (NOMA) instead of RSMA, and highlighting the benefits of our PSC compression model.

cs.ET↗