arXiv subjects

Dongming Wang

Publications and source records attributed to Dongming Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Experimental Performance of Bidirectional Phase Coherent Transmission and Sensing for mmWave Cell-free Massive MIMO Systems with Reciprocity Calibration

Phase synchronization among distributed transmission reception points (TRPs) is a prerequisite for enabling coherent joint transmission and high-precision sensing in millimeter wave (mmWave) cell-free massive multiple-input and multiple-output (MIMO) systems. This paper proposes a bidirectional calibration scheme and a calibration coefficient estimation method for phase synchronization, and presents a calibration coefficient phase tracking method using unilateral uplink/downlink channel state information (CSI). Furthermore, this paper introduces the use of reciprocity calibration to eliminate non-ideal factors in sensing and leverages sensing results to achieve calibration coefficient phase tracking in dynamic scenarios, thus enabling bidirectional empowerment of both communication and sensing. Simulation results demonstrate that the proposed method can effectively implement reciprocal calibration with lower overhead, enabling coherent collaborative transmission, and resolving non-ideal factors to acquire lower sensing error in sensing applications. Experimental results show that, in the mmWave band, over-the-air (OTA) bidirectional calibration enables coherent collaborative transmission for both collaborative TRPs and collaborative user equipments (UEs), achieving beamforming gain and long-time coherent sensing capabilities.

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arXiv2026

Base Station Sleeping Strategy Based on Load Sharing in Ultra-Dense Networks

To address the issues of high operational costs and low energy efficiency (EE) caused by the dense deployment of small base stations (s-BSs) in 5G ultra-dense networks (UDNs), this paper first constructs a multi-objective mathematical optimization model targeting maximizing EE and minimizing the number of active BSs. The model incorporates key constraints including BS operational state, user equipment (UE)-BS connection relationship, and load threshold, laying a theoretical foundation for the coordinated optimization of energy conservation and quality of service. Based on this model, an integrated solution combining UE-BS initial connection optimization and load-sharing based BS sleeping is proposed. In the initial connection phase, with communication quality and BS load as dual constraints, efficient matching between UEs and optimal BSs is achieved through three sequential steps: communication feasibility screening, redundant connection removal, and overload load redistribution. This resolves the problems of load imbalance and difficult identification of redundant BSs in UDNs arising from unordered initial connections. In the BS sleeping phase, a BS sleeping index, comprehensively considering UE transferability and backup BS resources, is innovatively introduced to quantify BS dormancy priority. Through a closed-loop process involving low-load BS screening, adjacent BS load evaluation, and load sharing by two takeover BSs based on their capacity, accurate dormancy of redundant BSs and collaborative load migration are realized. Simulation results in a typical UDNs scenario demonstrate that, compared with the traditional baseline scheme, the proposed solution exhibits significant advantages in convergence speed, optimization of the number of active BSs, and EE improvement.

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arXiv2026

Flexible-Duplex Cell-Free Architecture for Secure Uplink Communications in Low-Altitude Wireless Networks

Low-altitude wireless networks (LAWNs) are expected to play a central role in future 6G infrastructures, yet uplink transmissions of uncrewed aerial vehicles (UAVs) remain vulnerable to eavesdropping due to their limited transmit power, constrained antenna resources, and highly exposed air-ground propagation conditions. To address this fundamental bottleneck, we propose a flexible-duplex cell-free (CF) architecture in which each distributed access point (AP) can dynamically operate either as a receive AP for UAV uplink collection or as a transmit AP that generates cooperative artificial noise (AN) for secrecy enhancement. Such AP-level duplex flexibility introduces an additional spatial degree of freedom that enables distributed and adaptive protection against wiretapping in LAWNs. Building upon this architecture, we formulate a max-min secrecy-rate problem that jointly optimizes AP mode selection, receive combining, and AN covariance design. This tightly coupled and nonconvex optimization is tackled by first deriving the optimal receive combiners in closed form, followed by developing a penalty dual decomposition (PDD) algorithm with guaranteed convergence to a stationary solution. To further reduce computational burden, we propose a low-complexity sequential scheme that determines AP modes via a heuristic metric and then updates the AN covariance matrices through closed-form iterations embedded in the PDD framework. Simulation results show that the proposed flexible-duplex architecture yields substantial secrecy-rate gains over CF systems with fixed AP roles. The joint optimization method attains the highest secrecy performance, while the low-complexity approach achieves over 90% of the optimal performance with an order-of-magnitude lower computational complexity, offering a practical solution for secure uplink communications in LAWNs.

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arXiv2026

NeRF-VIO: Map-Based Visual-Inertial Odometry with Initialization Leveraging Neural Radiance Fields

A prior map serves as a foundational reference for localization in context-aware applications such as augmented reality (AR). Providing valuable contextual information about the environment, the prior map is a vital tool for mitigating drift. In this paper, we propose a map-based visual-inertial localization algorithm (NeRF-VIO) with initialization using neural radiance fields (NeRF). Our algorithm utilizes a multilayer perceptron model and redefines the loss function as the geodesic distance on $SE(3)$, ensuring the invariance of the initialization model under a frame change within $\mathfrak{se}(3)$. The evaluation demonstrates that our model outperforms existing NeRF-based initialization solution in both accuracy and efficiency. By integrating a two-stage update mechanism within a multi-state constraint Kalman filter (MSCKF) framework, the state of NeRF-VIO is constrained by both captured images from an onboard camera and rendered images from a pre-trained NeRF model. The proposed algorithm is validated using a real-world AR dataset, the results indicate that our two-stage update pipeline outperforms MSCKF across all data sequences.

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arXiv2025

Bayesian Probability Fusion for Multi-AP Collaborative Sensing in Mobile Networks

Integrated sensing and communication is widely acknowledged as a foundational technology for next-generation mobile networks. Compared with monostatic sensing, multi-access point (AP) collaborative sensing endows mobile networks with broader, more accurate, and resilient sensing capabilities, which are critical for diverse location-based sectors. This paper focuses on collaborative sensing in multi-AP networks and proposes a Bayesian probability fusion framework for target parameter estimation using orthogonal frequency-division multiplexing waveform. The framework models multi-AP received signals as probability distributions to capture stochastic observations from channel noise and scattering coefficients. Prior information is then incorporated into the joint probability density function to cast the problem as a constrained maximum a posteriori estimation. To address the high-dimensional optimization, we develop a prior-constrained gradient ascent (PCGA) algorithm that decouples correlated parameters and performs efficient gradient updates guided by the target prior. Theoretical analysis covers optimal fusion weights for global signal-to-noise ratio maximization, PCGA convergence, and the Cramer-Rao lower bound of the estimator, with insights applicable to broader fusion schemes. Extensive numerical simulations and real-world experiments with commercial devices show the framework reduces transmission overhead by 90% versus signal fusion and lowers estimation error by 41% relative to parameter fusion. Notably, field tests achieve submeter accuracy with 50% probability in typical coverage of mmWave APs. These improvements highlight a favorable balance between communication efficiency and estimation accuracy for practical multi-AP sensing deployment. The dataset is released for research purposes and is publicly available at: http://pmldatanet.com.cn/dataapp/multimodal

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arXiv2025

Average Achievable Rate Analysis of Cell-Free Massive MIMO in the Finite Blocklength Regime with Imperfect CSI

Acquiring perfect channel state information (CSI) introduces substantial challenges in cell-free massive MIMO (CF-mMIMO) systems, primarily due to the large dimensionality of channel parameters, especially under ultra-reliable low-latency communication (uRLLC) constraints. Furthermore, the impact of imperfect CSI on the average achievable rate within the finite blocklength regime remains largely unexplored. Motivated by this gap, this paper proposes a novel analytical framework that provides a closed-form expression for the average achievable rate with imperfect CSI in the Laplace domain. We demonstrate analytically that both the channel dispersion and the expected channel capacity can be expressed explicitly in terms of the Laplace transform of the large-scale fading component. Numerical simulations confirm that the derived expressions match closely with Monte Carlo simulations, verifying their accuracy. Furthermore, we theoretically show that although imperfect CSI degrades performance in the finite blocklength regime, the inherent characteristics of CF-mMIMO architecture effectively mitigates this loss.

cs.IT↗

arXiv2025

DoF Analysis and Beamforming Design for Active IRS-aided Multi-user MIMO Wireless Communication in Rank-deficient Channels

Due to its ability of significantly improving data rate, intelligent reflecting surface (IRS) will be a potential crucial technique for the future generation wireless networks like 6G. In this paper, we will focus on the analysis of degree of freedom (DoF) in IRS-aided multi-user MIMO network. Firstly, the DoF upper bound of IRS-aided single-user MIMO network, i.e., the achievable maximum DoF of such a system, is derived, and the corresponding results are extended to the case of IRS-aided multiuser MIMO by using the matrix rank inequalities. In particular, in serious rank-deficient, also called low-rank, channels like line-of-sight (LoS), the network DoF may doubles over no-IRS with the help of IRS. To verify the rate performance gain from augmented DoF, three closed-form beamforming methods, null-space projection plus maximize transmit power and maximize receive power (NSP-MTP-MRP), Schmidt orthogonalization plus minimum mean square error (SO-MMSE) and two-layer leakage plus MMSE (TLL-MMSE) are proposed to achieve the maximum DoF. Simulation results shows that IRS does make a dramatic rate enhancement. For example, in a serious deficient channel, the sum-rate of the proposed TLL-MMSE aided by IRS is about twice that of no IRS. This means that IRS may achieve a significant DoF improvement in such a channel.

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arXiv2025

Distributed satellite information networks: Architecture, enabling technologies, and trends

Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, grant-free massive access, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

cs.IT↗

arXiv2025

A Novel Massive Random Access in Cell-Free Massive MIMO Systems for High-Speed Mobility with OTFS Modulation

In the research of next-generation wireless communication technologies, orthogonal time frequency space (OTFS) modulation is emerging as a promising technique for high-speed mobile environments due to its superior efficiency and robustness in doubly selective channels. Additionally, the cell-free architecture, which eliminates the issues associated with cell boundaries, offers broader coverage for radio access networks. By combining cell-free network architecture with OTFS modulation, the system may meet the demands of massive random access required by machine-type communication devices in high-speed scenarios. This paper explores a massive random access scheme based on OTFS modulation within a cell-free architecture. A transceiver model for uplink OTFS signals involving multiple access points (APs) is developed, where channel estimation with fractional channel parameters is approximated as a block sparse matrix recovery problem. Building on existing superimposed and embedded preamble schemes, a hybrid preamble scheme is proposed. This scheme leverages superimposed and embedded preambles to respectively achieve rough and accurate active user equipment (UEs) detection (AUD), as well as precise channel estimation, under the condition of supporting a large number of access UEs. Moreover, this study introduces a generalized approximate message passing and pattern coupling sparse Bayesian learning with Laplacian prior (GAMP-PCSBL-La) algorithm, which effectively captures block sparse features after discrete cosine transform (DCT), delivering precise estimation results with reduced computational complexity. Simulation results demonstrate that the proposed scheme is effective and provides superior performance compared to other existing schemes.

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arXiv2025

Algorithmic Detection of Jacobi Stability for Systems of Second Order Differential Equations

This paper introduces an algorithmic approach to the analysis of Jacobi stability of systems of second order ordinary differential equations (ODEs) via the Kosambi--Cartan--Chern (KCC) theory. We develop an efficient symbolic program using Maple for computing the second KCC invariant for systems of second order ODEs in arbitrary dimension. The program allows us to systematically analyze Jacobi stability of a system of second order ODEs by means of real solving and solution classification using symbolic computation. The effectiveness of the proposed approach is illustrated by a model of wound strings, a two-dimensional airfoil model with cubic nonlinearity in supersonic flow and a 3-DOF tractor seat-operator model. The computational results on Jacobi stability of these models are further verified by numerical simulations. Moreover, our algorithmic approach allows us to detect hand-guided computation errors in published papers.

cs.SC↗

arXiv2025

Novel Synchronization Scheme for Cooperative ISAC Systems

Carrier frequency and timing synchronization play the fundamental roles in cooperative integrating communication and sensing (ISAC). To mitigate the effects of synchronization error, this paper develops a novel synchronization scheme in cell-free massive multiple-input multiple-output (mMIMO) systems. First, we characterize the impacts of pilot contamination on synchronization performance, i.e., Cramer-Rao bound (CRB). Furthermore, a maximum likelihood algorithm is presented to estimate the CFO and TO among the pairing APs. Then, to minimize the sum of CRBs, we devise a synchronization strategy based on a pilot-sharing scheme by jointly optimizing the cluster classification, synchronization overhead, and pilot-sharing scheme, while simultaneously considering the overhead and each AP's synchronization requirements. To solve this NP-hard problem, we simplify it into two sub-problems, namely cluster classification problem and the pilot sharing problem. To strike a balance between synchronization performance and overhead, we first classify the clusters by using the K-means algorithm, and propose a criteria to find a good set of master APs. Then, the pilot-sharing scheme is obtained by using the swap-matching operations. Simulation results validate the accuracy of our derivations and demonstrate the effectiveness of the proposed scheme over the benchmark schemes.

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arXiv2024

Two-Timescale Design for AP Mode Selection of Cooperative ISAC Networks

As an emerging technology, cooperative bi-static integrated sensing and communication (ISAC) is promising to achieve high-precision sensing, high-rate communication as well as self-interference (SI) avoidance. This paper investigates the two-timescale design for access point (AP) mode selection to realize the full potential of the cooperative bi-static ISAC network with low system overhead, where the beamforming at the APs is adapted to the rapidly-changing instantaneous channel state information (CSI), while the AP mode is adapted to the slowly-changing statistical CSI. We first apply the minimum mean square error (MMSE) estimator to estimate the channel between the APs and the channels from the APs to the user equipments (UEs). Then we adopt the low-complexity maximum ratio transmission (MRT) beamforming and the maximum ratio combining (MRC) detector, and derive the closed-form expressions of the communication rate and the sensing signal-to-interference-plus-noise-ratio (SINR). We formulate a non-convex integer optimization problem to maximize the minimum sensing SINR under the communication quality of service (QoS) constraints. McCormick envelope relaxation and successive convex approximation (SCA) techniques are applied to solve the challenging non-convex integer optimization problem. Simulation results validate the closed-form expressions and prove the convergence and effectiveness of the proposed AP mode selection scheme.

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arXiv2024

Cooperative ISAC-empowered Low-Altitude Economy

This paper proposes a cooperative integrated sensing and communication (ISAC) scheme for the low-altitude sensing scenario, aiming at estimating the parameters of the unmanned aerial vehicles (UAVs) and enhancing the sensing performance via cooperation. The proposed scheme consists of two stages. In Stage I, we formulate the monostatic parameter estimation problem via using a tensor decomposition model. By leveraging the Vandermonde structure of the factor matrix, a spatial smoothing tensor decomposition scheme is introduced to estimate the UAVs' parameters. To further reduce the computational complexity, we design a reduced-dimensional (RD) angle of arrival (AoA) estimation algorithm based on generalized Rayleigh quotient (GRQ). In Stage II, the positions and true velocities of the UAVs are determined through the data fusion across multiple base stations (BSs). Specifically, we first develop a false removing minimum spanning tree (MST)-based data association method to accurately match the BSs' parameter estimations to the same UAV. Then, a Pareto optimality method and a residual weighting scheme are developed to facilitate the position and velocity estimation, respectively. We further extend our approach to the dual-polarized system. Simulation results validate the effectiveness of the proposed schemes in comparison to the conventional techniques.

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arXiv2024

Performance Analysis of uRLLC in scalable Cell-free Radio Access Network System

As a critical component of beyond fifth-generation (B5G) and sixth-generation (6G) mobile communication systems, ultra-reliable low-latency communication (uRLLC) imposes stringent requirements on latency and reliability. In recent years, with the improvement of mobile communication network, centralized and distributed processing schemes for cellfree massive multiple-input multiple-output (CF-mMIMO) have attracted significant research attention. This paper investigates the performance of a novel scalable cell-free radio access network (CF-RAN) architecture featuring multiple edge distributed units (EDUs) under the finite block length regime. Closed expressions for the upper and lower bounds of its expected spectral efficiency (SE) performance are derived, where centralized and fully distributed deployment can be treated as two special cases, respectively. Furthermore, the spatial distribution of user equipments (UEs) and remote radio units (RRUs) is examined and the analysis reveals that the interleaving RRUs deployment associated with the EDU can enhance SE performance under finite block length constraints with specific transmission error probability. The paper also compares Monte Carlo simulation results with multi-RRU clustering-based collaborative processing, validating the accuracy of the space-time exchange theory in the scalable CF-RAN scenario. By deploying scalable EDUs, a practical trade-off between latency and reliability can be achieved through spatial degree-of-freedom (DoF), offering a distributed and scalable realization of the space-time exchange theory.

cs.IT↗

arXiv2024

Access Point Deployment for Localizing Accuracy and User Rate in Cell-Free Systems

Evolving next-generation mobile networks is designed to provide ubiquitous coverage and networked sensing. With utility of multi-view sensing and multi-node joint transmission, cell-free is a promising technique to realize this prospect. This paper aims to tackle the problem of access point (AP) deployment in cell-free systems to balance the sensing accuracy and user rate. By merging the D-optimality with Euclidean criterion, a novel integrated metric is proposed to be the objective function for both max-sum and max-min problems, which respectively guarantee the overall and lowest performance in multi-user communication and target tracking scenario. To solve the corresponding high dimensional non-convex multi-objective problem, the Soft actor-critic (SAC) is utilized to avoid risk of local optimal result. Numerical results demonstrate that proposed SAC-based APs deployment method achieves $20\%$ of overall performance and $120\%$ of lowest performance.

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arXiv2024

Passive Integrated Sensing and Communication Scheme based on RF Fingerprint Information Extraction for Cell-Free RAN

This paper investigates how to achieve integrated sensing and communication (ISAC) based on a cell-free radio access network (CF-RAN) architecture with a minimum footprint of communication resources. We propose a new passive sensing scheme. The scheme is based on the radio frequency (RF) fingerprint learning of the RF radio unit (RRU) to build an RF fingerprint library of RRUs. The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side. The receiver extracts the channel parameters from the signal and estimates the channel environment, thus locating the reflectors in the environment. The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture. Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.

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arXiv2024

Hybrid Precoding with Per-Beam Timing Advance for Asynchronous Cell-free mmWave Massive MIMO-OFDM Systems

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is regarded as one of the promising technologies for next-generation wireless networks. However, due to its distributed architecture, geographically separated access points (APs) jointly serve a large number of user-equipments (UEs), there will inevitably be a discrepancies in the arrival time of transmitted signals. In this paper, we investigate millimeter-wave (mmWave) CF-mMIMO orthogonal frequency division multiplexing (OFDM) systems with asynchronous reception in a wide area coverage scenario, where asynchronous timing offsets may extend far beyond the cyclic prefix (CP) range. A comprehensive asynchronous beam-domain signal transmission model is presented for mmWave CF-mMIMO-OFDM systems in both downlink and uplink, incorporating phase offset, inter-carrier interference (ICI) and inter-symbol interference (ISI). To address the issue of asynchronous reception, we propose a novel per-beam timing advance (PBTA) hybrid precoding architecture and analyze the spectral efficiency (SE) in the beam domain for downlink and uplink asynchronous receptions. Both scalable centralized and distributed implementations are taken into account, and the asynchronous delay phase is utilized to design precoding/combining vectors. Furthermore, we formulate the sum rate maximization problem and develop two low-complexity joint beam selection and UE association algorithms considering the impact of asynchronous timing offset exceeding the CP range. Simulation results demonstrate that the performance will be severely limited by ICI and ISI, and our proposed PBTA hybrid precoding architecture effectively mitigates asynchronous interference compared to the nearest AAU/UE-based timing-advance scheme. Additionally, numerical results show that our proposed low-complexity joint beam selection and UE association algorithms achieve superior SE performance.

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arXiv2024

Target Localization in Cooperative ISAC Systems: A Scheme Based on 5G NR OFDM Signals

The integration of sensing capabilities into communication systems, by sharing physical resources, has a significant potential for reducing spectrum, hardware, and energy costs while inspiring innovative applications. Cooperative networks, in particular, are expected to enhance sensing services by enlarging the coverage area and enriching sensing measurements, thus improving the service availability and accuracy. This paper proposes a cooperative integrated sensing and communication (ISAC) framework by leveraging information-bearing orthogonal frequency division multiplexing (OFDM) signals transmitted by access points (APs). Specifically, we propose a two-stage scheme for target localization, where communication signals are reused as sensing reference signals based on the system information shared at the central processing unit (CPU). In Stage I, we propose a twodimensional fast Fourier transform (2D-FFT)-based algorithm to measure the ranges of scattered paths induced by targets, through the extraction of delay and Doppler information from the sensing channels between APs. Then, the target locations are estimated in Stage II based on these range measurements. Considering the potential occurrence of ill-conditioned measurements with large error during the extraction of time-frequency information, we propose an efficient algorithm to match the range measurements with the targets while eliminating ill-onditioned measurements, achieving high-accuracy target localization. In addition, based on the transmission configurations defined in the fifth generation (5G) standards, we elucidate the performance trade-offs in both communication and sensing, and extend the proposed sensing scheme for general scenarios. Finally, numerical results confirm the effectiveness of our sensing scheme and the cooperative gain of the ISAC framework.

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