arXiv subjects

Shenghui Song

Publications and source records attributed to Shenghui Song.

At least 19 recordsLinked to original sources

arXiv2026

Mutual-Coupling-Aware Joint Precoding and Antenna Positioning for Movable-Antenna MU-MIMO Systems

Movable antennas (MAs) improve wireless communications by adapting antenna positions to the propagation environment. However, antenna movement also changes the mutual coupling (MC) among elements of the transmit and receive arrays, which affects the performance of MA-enabled systems. In this paper, we investigate MC-aware joint precoding and antenna positioning for a downlink multiuser multiple-input multiple-output (MU-MIMO) system with MAs at both the base station and users. We establish an MC-aware system model by combining field-response propagation with a circuit-based representation of the transmit and receive arrays. Based on this model, we formulate a weighted sum-rate (WSR) maximization problem that jointly optimizes multi-stream precoders and antenna positions. The problem is challenging because the precoders and positions are coupled through the propagation channels and position-dependent MC matrices. To address this challenge, we reformulate the problem in the antenna-current domain to to efficiently handle the MC matrices and develop a current-domain weighted minimum mean square error (WMMSE) framework for alternating optimization. Furthermore, we handle the nonconvex position updates within the framework by using majorization--minimization (MM) and solve the resulting convex subproblems with a dedicated conic alternating direction method of multipliers (ADMM) algorithm. Simulation results demonstrate that the proposed design improves the WSR over both an MC-aware fixed array and a movable-array design that ignores MC during optimization.

cs.IT↗

arXiv2026

Bistatic Target Detection by Exploiting Both Deterministic Pilots and Unknown Random Data Payloads

Integrated sensing and communication (ISAC) plays a crucial role in 6G, enabling innovative applications such as drone surveillance, urban air mobility, and low-altitude logistics. However, the hybrid ISAC signal, which comprises deterministic pilot and random data payload components, poses challenges for target detection due to two reasons: 1) these two components cause coupled shifts in both the mean and variance of the received signal, and 2) the random data payloads are typically unknown to the sensing receiver in the bistatic setting. Unfortunately, these challenges could not be tackled by existing target detection algorithms. In this paper, a generalized likelihood ratio test (GLRT)-based detector is derived by leveraging the known deterministic pilots and the statistical characteristics of the unknown random data payloads. Due to the analytical intractability of exact performance characterization, we perform an asymptotic analysis for the false alarm probability and detection probability of the proposed detector. The results reveal a deterministic-random trade-off: the deterministic pilots enhance detection through a target-induced mean shift, while the random data payloads provide an additional covariance-domain target signature but simultaneously introduce statistical uncertainty due to their unknown realizations. Simulations validate the theoretical findings and demonstrate the effectiveness of the proposed detector, which highlights the necessity of designing a dedicated detector to fully exploit the signaling resources assigned to random data payloads.

cs.IT↗

arXiv2026

A Unified Two-Stage Generative Diffusion Framework for Channel Estimation and Port Selection in Multiuser MIMO-FAS

Fluid antenna systems (FAS) have emerged as a promising technology for next-generation wireless systems. However, practical multiuser multiple-input multiple-output FAS (MIMO-FAS) faces two inherently coupled challenges: acquiring accurate high-dimensional channel state information (CSI) from limited RF chains and solving the combinatorial port selection problem, where the effectiveness of the latter highly depends on the result of the former. In this paper, we propose a unified two-stage diffusion framework that formulates the joint task as a maximum-a-posteriori (MAP) inference problem equivalent to maximizing the posterior expected utility of the port selection, and approximates it through two sequential stages. For Stage I, we employ a continuous flow-based diffusion model as a powerful implicit prior for 2D FAS channels and perform approximate posterior sampling via a parallel guided generation scheme, enabling accurate multiuser channel recovery even under severely low sub-sampling ratios. For Stage II, we train a discrete diffusion model on optimal labels computed by exhaustive search to propose a pool of promising port selection candidates. The final selection is then determined by maximizing the empirical posterior expected utility over this pool, followed by a lightweight local refinement. Extensive simulations demonstrate that the proposed framework simultaneously achieves accurate channel estimation and delivers over 90% of the deployed rate of the computationally expensive exhaustive search over the estimated channel, while substantially improving the minimum achievable rate relative to practical cascaded schemes.

cs.IT↗

arXiv2026

Model-Driven Deep Learning for Distributed Detection with Low-Bit Quantization

Distributed detection plays a fundamental role in communication-efficient collaborative inference over large-scale networks, where spatially distributed nodes must cooperate using severely quantized messages due to bandwidth constraints. While low-bit quantization is essential for reducing communication overhead, it can significantly degrade detection performance, particularly when prior knowledge of observation noise is unavailable. To address this challenge, we propose a model-driven deep learning framework for distributed detection with low-bit quantization. For fixed local quantizers, we first derive the exact finite-sensor maximum a posteriori (MAP) detection error and prove that, under conditionally independent and identically distributed (i.i.d.) observations, independent local randomizations, and a common quantization-level constraint, identical quantizers preserve the supremum of the average network Chernoff information. Guided by these results, we develop a binary probabilistic quantizer with a trainable neural probability controller and design a tree-structured probabilistic quantizer for scalable multi-bit quantization. The exact finite-sensor MAP error provides the training objective for the binary quantizer, while we derive a computationally tractable Bhattacharyya surrogate objective for training the multi-bit quantizer. For both cases, we derive analytical MAP fusion rules based on the class-conditional probability mass functions (PMFs) of the quantized messages. When the observation statistics are unknown, these PMFs are estimated from held-out labeled data, yielding plug-in MAP detectors. Simulation results show that the proposed method approaches centralized-oracle performance at higher quantization resolutions and remains effective under changes in network size and test-time signal amplitude.

cs.IT↗

arXiv2026

Radio Echo Representation Learning for Semantic Sensing in Multi-Modal ISAC Systems

Semantic sensing extends integrated sensing and communication (ISAC) beyond physical parameter estimation by exploiting task-oriented representations from radio echoes. Nevertheless, existing multimodal sensing remains largely limited to vision-assisted radio physical parameter estimation or typically processes the visual and radio modalities separately, leaving radio representations underutilized for visual task enhancement. Feature-level fusion enables closer cross-modal interaction, while echo interference complicates the learning of user-specific radio representations and its semantic consistency with the visual entities. To address these challenges, this paper proposes a multimodal semantic sensing framework for orthogonal frequency-division multiplexing (OFDM)-ISAC systems. We propose a radio encoder to learn radio representations oriented toward visual localization from superimposed pilot echoes, together with Variational Radio-Visual Grounding (VRVG) to promote semantic consistency between the learned radio representations and visual spatial features. The radio representations are then formulated as semantic probes to highlight the corresponding entities in the visual scene through cross-modal fusion. Experiments using real-world RCooper dataset show that the resulting synergistic radio-visual embeddings enable accurate radio-visual user matching and significantly improve both user-specific visual localization and physical parameter estimation performance. The results substantiate the benefits of radio representation learning and semantically consistent fusion for multimodal semantic sensing.

eess.SP↗

arXiv2026

2D Coprime Pilots for Delay-Doppler Sensing in OFDM-ISAC Systems

Integrated Sensing and Communication (ISAC) is envisioned to endow future 6G systems with seamless sensing capabilities. To support efficient sensing with minimum communication overhead, sparse pilots embedded within communication frames have emerged as a promising solution. Along this line of research, existing studies have achieved engaging results in maximizing the unambiguous sensing region. However, jointly maximizing the sensing region and sensing accuracy remains challenging due to the lack of a unified performance metric and an effective pilot design framework. This paper jointly optimizes the unambiguous sensing region and sensing accuracy for estimating delay-Doppler (DD) parameters in Orthogonal Frequency Division Multiplexing (OFDM)-ISAC systems, where sensing mutual information (SMI) is adopted as a unified performance metric to characterize the overall sensing capability. Specifically, the joint optimization is formulated as an SMI maximization problem by systematically resolving sensing ambiguity and optimizing sensing accuracy. In particular, based on the generalized Bezout identity, we derive a 2D (timefrequency) coprime condition, which, as far as the authors know, is the first necessary and sufficient condition to achieve the unique estimation of DD parameters in the literature. Under this unambiguous condition, we further propose an Adam-Guided Iterative Refinement (AGIR) algorithm to optimize the sensing accuracy. Numerical results demonstrate the advantage of the proposed framework over existing designs, owing to the freedom offered by the 2D coprime condition in optimizing the sensing accuracy.

eess.SP↗

arXiv2026

A Unified Framework for Characterizing General MIMO Channels

Modern MIMO systems are evolving toward higher-dimensional and more flexible architectures, such as distributed and holographic MIMO, offering substantial benefits while giving rise to increasingly complex channel correlation structures. This growing architectural diversity makes it difficult to characterize the fundamental limits of different MIMO systems on a case-by-case basis, motivating a unified analytical framework applicable across a broad range of architectures. This paper addresses this research gap by investigating a generally correlated Rayleigh channel whose vectorized form follows a general Gaussian distribution with an arbitrary covariance matrix. For that purpose, we first prove that the spectral norm of the channel matrix is bounded in the asymptotic regime where the numbers of transmit and receive antennas grow proportionally. We then derive a deterministic approximation for the ergodic mutual information, with an explicit convergence rate governed by the structure of the channel correlation. The approximation is characterized by a pair of matrix-valued self-consistent equations, for which we establish the existence and uniqueness of the solution and propose an iterative numerical algorithm. Furthermore, we develop a framework based on positive linear maps to analyze the stability of these equations, which can facilitate the spectral analysis of broader classes of random matrices. The proposed characterization unifies many existing results for structured MIMO channels as special cases while remaining applicable to a broad class of general MIMO architectures that are difficult to evaluate using existing analytical frameworks. To demonstrate its utility, we apply the developed theory to two representative systems, namely downlink distributed MIMO and uplink heterogeneous MIMO. Numerical results confirm the accuracy of the derived deterministic approximations.

cs.IT↗

arXiv2026

AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC

Multi-modal integrated sensing and communication (ISAC) enables environmental perception and reliable connectivity for intelligent wireless networks. Data-driven multi-modal ISAC models depend heavily on annotated real-world data to learn relationships across sensing and wireless observations, thereby constraining scalable deployment. Although synthetic data generation reduces the burden, adapting existing simulation pipelines to a target deployment requires consistent scene, sensing, wireless, and learning configurations, while mismatches among these coupled components impair sim-to-real transferability. To address the challenge, we propose an agentic artificial intelligence (AI) framework for sim-to-real multi-modal ISAC, named AIMS. Given a natural-language deployment request specifying the target task, deployment conditions, and real-data budget, AIMS derives a deployment-specific sim-to-real configuration and coordinates its execution to produce a deployment-specific task model. A two-agent architecture coordinates scene construction with task learning. A scene construction agent generates geographically grounded, synchronized sensing and wireless records from shared physical states, while a scene understanding agent configures task-relevant modalities and mixture-of-experts (MoE) learning for zero-shot inference or few-shot adaptation. Structured domain knowledge guides dependency-aware planning, while validation evidence supports feedback-driven revision of affected decisions. Experiments on the real-world DeepSense 6G dataset demonstrate improved vehicle detection and beam prediction over the considered simulation and fusion baselines. A separate orchestration benchmark evaluates task interpretation, dependency reasoning, and feedback-driven replanning across diverse deployment requests, showing improved plan correctness with structured domain knowledge and validation feedback.

cs.AI↗

arXiv2026

GSBF: Gaussian Splatting for Environment-Aware Beamforming

Beamforming plays a key role in multiple-input-multiple-output (MIMO) communication systems. However, conventional beamforming design normally requires accurate instantaneous channel state information (CSI) and iterative optimization, which incur substantial pilot overhead and computational complexity. Recognizing that radio propagation is intrinsically governed by the physical geometry, we develop a 3D Gaussian splatting for environment-aware beamforming (GSBF) pipeline based on multi-modal data, which characterizes the environment through a persistent 3D Gaussian representation. Specifically, GSBF models the environmental scattering response with reciprocity-preserving bidirectional spherical Gaussian (Bi-SG) kernels and performs two-sided electromagnetic rasterization to render an angular propagator map. The rendered map is then aggregated through an over-complete array-manifold dictionary and projected to the constant-modulus beamformers, thereby synthesizing beams directly from the access point (AP) pose and user position without online instantaneous CSI. Simulations demonstrate that GSBF consistently outperforms baselines such as exhaustive beam alignment (EBA) with lower latency.

cs.AI↗

arXiv2026

Task-Oriented Precoding for Edge Inference over Large-Scale MIMO Systems

Future wireless networks are expected to support networked artificial intelligence (AI) services, where multiple devices transmit learned features to an edge server for distributed inference. This setting calls for task-oriented physical-layer optimization, where wireless transmission should preserve useful information for inference rather than only maximize the rate or reconstruct the transmitted signals. A key physical-layer control variable is the multiple-input multiple-output precoder, which determines how device features are shaped and combined over wireless channels. Existing task-oriented precoding methods typically adapt the precoder to instantaneous channel state information at the transmitter (CSIT). However, in multi-device MIMO systems, acquiring the aggregate channel, feeding back CSI or optimized precoders, and reoptimizing across coherence blocks introduce substantial overhead. This paper develops a random-matrix-theoretic framework based on statistical CSIT that designs a slow-timescale precoder from channel covariance statistics and training-set feature statistics, without requiring instantaneous CSIT. We adopt maximal coding rate reduction (MCR${^2}$) to measure the class separability of the received features, yielding a task-aware utility for MIMO precoder design. Since this utility still depends on random small-scale fading, we derive a deterministic approximation that converts it into a fixed-point objective depending only on long-term statistics and large-system dimension ratios via random matrix theory. A projected block-coordinate ascent and successive convex approximation algorithm is developed to optimize this deterministic objective under per-device power constraints. Experiments on ModelNet10 verify the approximation and show that the proposed statistical precoder improves task-aware mode allocation and inference performance over competitive benchmarks.

cs.IT↗

arXiv2026

Task-Oriented Communication with Hybrid-Precision Models

Edge inference has emerged as a promising solution for the proliferation of artificial intelligence (AI) services by deploying models at the network edge to circumvent cloud-routing latency. Existing edge inference approaches mainly focused on either cooperative inference to reduce latency or lightweight model design to fit resource-constrained devices. These solutions often address the communication and computation challenges separately, and thus struggle to achieve a balanced trade-off among transmission efficiency, on-device processing cost, and inference accuracy. To bridge this gap, this paper proposes a hybrid-precision task-oriented communication framework for edge inference to holistically balance communication, on-device computation, and utility. In this framework, a binarized front-end is deployed on the edge device to extract and transmit binary features via orthogonal frequency-division multiplexing (OFDM) signals, while a full-precision back-end on the edge server performs the final inference. To ensure model consistency, we introduce an on-device binarization method tailored for split inference and develop an integrated channel-aware transmission scheme featuring subcarrier-based feature calibration. Furthermore, a knowledge distillation (KD)-based training strategy, supported by specialized gradient estimators, is developed to optimize the end-to-end system and inherit semantic knowledge from a full-precision teacher model. Extensive experiments on the large-scale ImageNet dataset demonstrate the superiority of the proposed hybrid system. Our analysis confirms that this design achieves an optimal trade-off among communication efficiency, on-device computational cost, and inference accuracy, outperforming existing edge inference solutions.

eess.SP↗

arXiv2026

Multi-Modal Environment-Aware Beam Management for Massive MIMO: A Geometry-Driven Virtual Base Station Framework

High-frequency massive multiple-input multiple-output (MIMO) systems promise ultra-high data rates. However, efficient beam management remains challenging due to the prohibitive beam training overhead and intricate coordination required in multi-user MIMO (MU-MIMO) scenarios. To address these bottlenecks, environment-aware communications have emerged as a promising paradigm, leveraging site-specific knowledge to circumvent exhaustive pilot-based beam training and streamline multi-user communications. In this paper, we propose an interpretable and geometry-driven framework that utilizes multi-modal environmental data, specifically regional 3D light detection and ranging (LiDAR) point clouds and location information, to construct an offline virtual base station (VBS) database. By modeling dominant reflection paths via mirror symmetry across building facades reconstructed from the point clouds, the VBS database provides a compact and sparse description of the wireless propagation environment. To bridge the semantic gap between geometric information and wireless channels, we develop a coarse channel reconstruction mechanism that estimates channel parameters directly from VBS-derived geometric relationships. Based on the resulting coarse beamspace representation, we design a VBS-assisted orthogonal-pilot (VOP)-based partial beam training scheme to refine the coarse estimates with minimal online training overhead. Finally, to tackle the combinatorial beam selection problem and manage inter-user interference, we propose a hierarchical deep reinforcement learning framework, namely a dual-agent dueling double deep Q-network, for coordinated beam selection (DD3QN-CBS). Simulation results demonstrate consistent gains in both beam training efficiency and beam selection performance over heuristic and learning-based baselines.

eess.SP↗

arXiv2026

Joint Channel Estimation and Cooperative Localization for Near-Field Ultra-Massive MIMO

The next-generation wireless networks are envisioned to jointly support high-rate communications and ubiquitous sensing. Ultra-Massive Multiple-Input Multiple-Output (UM-MIMO) offers abundant spatial Degrees of Freedom (DoFs) for both functions, yet its large aperture shifts electromagnetic propagation into the near field, invalidating conventional far-field (plane-wave) assumptions. While near-field channel modeling has been studied, existing channel estimation methods are inadequate: on-grid designs suffer from non-orthogonal codebooks, and off-grid methods lack convergence guarantees, yielding unreliable estimates. Moreover, channel estimation and localization are typically designed in isolation, preventing the exchange of information that could otherwise enable mutual performance improvement. To address this difficulty, we propose a unified framework that exploits near-field characteristics to jointly design channel estimation and cooperative localization. Specifically, we develop a Variational Newtonized Near-field Channel Estimation (VNNCE) algorithm that extracts position-aware soft information from the channel, and a Gaussian Fusion Cooperative Localization (GFCL) method that leverages this information across multiple Base Stations (BSs) for enhanced accuracy.

eess.SP↗

arXiv2026

Sensing-Assisted Channel Estimation for Flexible-Antenna Systems: A Unified Framework

Flexible-antenna systems, which use a small number of radio frequency (RF) chains to dynamically access a large set of candidate antenna locations, have emerged as a hardware-efficient architecture for 6G networks. Acquiring accurate channel state information (CSI) is critical for these systems, but it typically incurs a prohibitive pilot overhead that scales with the massive number of candidate locations. To address this bottleneck, we propose a unified sensing-assisted channel estimation framework tailored for flexible-antenna systems. It reduces the full CSI reconstruction problem to a consistent two-stage process: it first resolves the dominant DOAs from the uplink data symbols by exploiting the spatial geometry, requiring no dedicated sensing pilot, and then calibrates the associated path gains using a minimal number of calibration pilots. Building on this pipeline, we develop two Newton-MUSIC algorithms tailored to different propagation environments. For line-of-sight (LOS)-dominant environments with uncorrelated sources, we propose SOC-Newton-MUSIC, which leverages second-order covariance (SOC) for low-complexity DOA sensing. For non-line-of-sight (NLOS) environments with coherent multipath, where the number of sources may exceed the number of activated RF chains, we propose FOC-Newton-MUSIC, which exploits fourth-order cumulants (FOC) to restore source identifiability and structurally expand the available spatial degrees of freedom (DOFs) through a continuous difference co-array. In both cases, by reformulating the spatial spectrum search as a continuous optimization problem, we replace exhaustive dense grid searches with parallelized Newton refinements.

eess.SP↗

arXiv2026

Rethinking Mutual Coupling in Movable Antenna MIMO Systems: Modeling and Optimization

Movable antennas (MAs) have attracted growing interest for their ability to improve channel conditions via adaptive antenna movement. Nevertheless, such movement inevitably introduces mutual coupling (MC), whose impact has been largely overlooked in existing MA literature. In this paper, we show that MC is not merely an unavoidable electromagnetic effect, but also a new source of capacity gains in MA-enabled multiple-input multiple-output (MIMO) systems. To leverage MC effects, we develop an optimization framework for both narrowband and wideband systems based on a rigorous circuit-theoretic model. For narrowband systems, capacity maximization is formulated as a non-convex optimization problem, which is solved via a block coordinate ascent (BCA) framework. Because optimizing MA positions is challenging due to analytically intractable MC matrices, we develop a trust region method (TRM)-based algorithm that utilizes Sylvester equations to compute the derivatives of the inverse square roots of the MC matrices. We further consider wideband systems and formulate a sum-rate maximization problem. To find a unified set of MA positions that balances varying subcarrier conditions, the BCA framework and the TRM-based MA position optimization algorithm are extended to wideband systems. Simulation results demonstrate that exploiting MC effects in MA-MIMO systems yields significant performance gains in both narrowband and wideband systems under various channel conditions. These gains highlight the benefits of MC-induced superdirectivity and designable MC matrices.

cs.IT↗

arXiv2026

Rethinking Mutual Coupling in Movable Antenna MIMO Systems

Movable antenna (MA) systems have emerged as a promising technology for future wireless communication systems. The movement of antennas gives rise to mutual coupling (MC) effects, which have been previously ignored and can be exploited to enhance the capacity of multiple-input multiple-output (MIMO) systems. To this end, we first model an MA-enabled point-to-point MIMO communication system with MC effects using a circuit-theoretic framework. The capacity maximization problem is then formulated as a non-concave optimization problem and solved via a block coordinate ascent (BCA)-based algorithm. The subproblem of optimizing MA positions is challenging due to the presence of the analytically intractable MC matrices. To overcome this difficulty, we develop a trust region method (TRM)-based algorithm to optimize MA positions, wherein Sylvester equations are employed to compute the derivatives of the inverse square roots of the MC matrices. Simulation results show significant capacity gains from leveraging MC effects, primarily due to customizable MC matrices and superdirectivity.

cs.IT↗

arXiv2026

Multi-modal Data Driven Virtual Base Station Construction for Massive MIMO Beam Alignment

Massive multiple-input multiple-output (MIMO) is a key enabler for the high data rates required by the sixth-generation networks, yet its performance hinges on effective beam management with low training overhead. This paper proposes an interpretable framework to tackle beam alignment in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) propagation environments. Our approach utilizes multi-modal data to construct virtual base stations (VBSs), which are geometrically defined as mirror images of the base station across reflecting surfaces reconstructed from 3D LiDAR points. These VBSs provide a sparse and spatial representation of the dominant features of the wireless environment. Based on the constructed VBSs, we develop a VBS-assisted beam alignment scheme comprising coarse channel reconstruction followed by partial beam training. Numerical results demonstrate that the proposed method achieves near-optimal performance in terms of spectral efficiency.

cs.IT↗

arXiv2026

Modular Foundation Model Inference at the Edge: Network-Aware Microservice Optimization

Foundation models (FMs) unlock unprecedented multimodal and multitask intelligence, yet their cloud-centric deployment precludes real-time responsiveness and compromises user privacy. Meanwhile, monolithic execution at the edge remains infeasible under stringent resource limits and uncertain network dynamics. To bridge this gap, we propose a microservice-based FM inference framework that exploits the intrinsic functional asymmetry between heavyweight core services and agile light services. Our two-tier deployment strategy ensures robust Quality of Service (QoS) under resource contention. Specifically, core services are placed statically via a long-term network-aware integer program with sparsity constraints to form a fault-tolerant backbone. On the other hand, light services are orchestrated dynamically by a low-complexity online controller that integrates effective capacity theory with Lyapunov optimization, providing probabilistic latency guarantees under real-time workload fluctuations. Simulations demonstrate that our framework achieves over 84% average on-time task completion with moderate deployment costs and maintains strong robustness as the system load scales.

cs.DC↗