arXiv subjects

Zhaohui Yang

Publications and source records attributed to Zhaohui Yang.

At least 19 recordsLinked to original sources

arXiv2026

SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control for Humanoid Loco-Manipulation

Humanoid loco-manipulation requires an effective interface between high-level task policies and whole-body control. Existing interfaces typically rely on a single action representation for both task intent and whole-body coordination. This may burden the high-level policy or leave one aspect underspecified. We present SP-HLM, a framework built on a sparse-pose-driven interface. The interface combines five task-critical body poses with a learned latent motion prior to represent spatial intent and whole-body coordination in complementary forms. SP-VLA, a vision-language-action model, first predicts task-relevant sparse poses and finger joint positions from multimodal observations and language instructions using asynchronous action-chunk inference. Given these sparse targets, SP-MGM, a motion generation model trained through two-stage distillation, reconstructs coordinated full-body motion in real time. A pretrained motion encoder maps this motion to a compact latent representation. SP-GMT, a general motion tracker, then integrates the explicit sparse targets with this implicit motion prior for whole-body control. Experiments in simulation and on a real humanoid robot demonstrate improved motion tracking, whole-body coordination, and loco-manipulation success over existing approaches. Additional results and videos are available on our \href{https://sp-hlm.github.io/}{\textcolor{blue}{project page}}.

cs.RO↗

arXiv2026

Mobile Embodied Agent Network (MEAN): Integrated Framework, Enabling Technologies, and Task-Driven Deployment

Mobile networks are evolving from human-centered, data-oriented communication infrastructures toward machine- and/or agent-dominant, task-oriented platforms that are to serve the rapidly increasing networked machines and embodied agents. In order to fulfill the newly arose complex task objectives, the framework of future mobile network is to be innovated to reflect and facilitate the intrinsic interplay and integration of the underlying processes of perception, communication, computation, decision, action, and feedback, etc. This survey establishes the mobile embodied agent network (MEAN) as a task-oriented networking paradigm in which networking and distributed intelligence are jointly organized around closed-loop embodied task execution. We first establish a unified MEAN framework that explains how network resources and intelligent functions jointly support embodied task execution, and extend its performance indicators from conventional communication metrics to embodied task outcomes and cross-layer system effects. With this framework, we analyze three complementary enabling mechanisms that form a cognition-interaction-adaption loop. Large artificial intelligence (AI) models provide high-level cognition, token communication connects this cognition with distributed agents through shared semantic representations, and reinforcement learning closes the loop by adapting system operation to network, task, and physical feedback. We further investigate the bidirectional interplay between MEAN and emerging technologies, showing how they mutually extend system capabilities and reshape task-oriented network design. Finally, we discuss open challenges and future research directions toward the practical realization of MEAN over real joint communication and computation platform.

cs.NI↗

arXiv2026

Full-Domain Coupler: A Wireless Native Neural Backbone for Channel Representation and Deduction

Data representation is a fundamental issue in deep learning. However, as wireless data scales and deeply couples across many physical domains such as time, space, and frequency, existing wireless artificial intelligence (AI) technologies lack dedicated representation solutions. Instead, they mainly rely on stitching general-purpose networks, a tool-driven paradigm that inevitably results in structural redundancy and bottlenecks in information flow. To fill this gap, this paper proposes Coupler, a wireless native-AI neural backbone designed for representation learning of channel state information (CSI)--the pivotal data in wireless systems. Leveraging the revealed physical insights of channel tensors, Coupler decomposes representation learning into individual domains on a layer-by-layer basis, and then couples the learned domain-specific features through a dimension-staggered cascade. This full-domain interleaved learning architecture enables superior parameter efficiency and fine-grained multi-domain feature fusion. Based on this backbone, we use the complex-domain multilayer perceptrons (CMLPs) as spatial and frequency domain learners, while employing three optional mechanisms--convolution, attention, or gating--to capture temporal dependencies. This results in a series of efficient channel learning schemes with diverse functionalities and extreme lightweights, showcasing the compactness, versatility and flexibility of Coupler. We evaluate these schemes on channel deduction, a general representation task encompassing channel estimation, interpolation, prediction, and feedback. Extensive experimental evaluations validate their significant performance gains and robust applicability even for real-world measured data, demonstrating the potential of Coupler as a promising basic architecture in the design of wireless foundation models.

eess.SP↗

arXiv2026

AWM: An All-in-One Wireless Foundation Model for Multi-User Channel Acquisition and Localization Without Downstream Tuning

Wireless foundation models (WFMs) seek to unify diverse wireless tasks and scenarios, yet existing methods still rely on task- or scenario-specific components or deployment-time fine-tuning. This paper presents an all-in-one wireless model (AWM), a WFM that unifies multi-user channel acquisition and localization, despite their distinct data modalities and functional objectives. We reveal that user location and channel state information (CSI) at different frequencies can be viewed as attribute-specific measurements of the same wireless link. By introducing in-scenario same-attribute reference pairs as prompts, AWM converts heterogeneous attributes into alignable relativity representations, formulating intra-band channel interpolation, inter-band channel prediction, and wireless localization as unified wireless-state transformations. Further, a multi-task MateFormer is developed to perform source-attribute relativity extraction, cross-attribute relativity alignment, and target-attribute synthesis within a unified architecture, avoiding task-specific output heads or downstream fine-tuning. Comprehensive experiments demonstrate the effectiveness of model unification, including substantial gains from cross-task knowledge sharing, seamless generalization to unseen scenarios, and an efficient closed-loop service for mobile users.

eess.SP↗

arXiv2026

Beyond Token Accuracy: Prioritizing What Matters for Visual Reconstruction

Tokens have become a unified interface for multimodal foundation models, making visual-token communication a natural paradigm for efficient image delivery. However, existing methods typically rely on static policies that cannot jointly adapt to image content and channel conditions. Moreover, their token-level utility objectives do not necessarily translate into improved image reconstruction quality. In this paper, we propose AdapToC, an adaptive, reconstruction-oriented visual-token communication framework. At the transmitter, an adaptive selector jointly models image content, channel state, and communication budget to perform instance-wise resource allocation. Rather than using a fixed token rate and protection policy, it dynamically determines how many tokens should be transmitted and assigns different protection levels according to token importance and current channel conditions. At the receiver, an adaptive MaskGIT receiver incorporates channel reliability into contextual token modeling. It distinguishes tokens with different reliability levels, preserves high-confidence observations, corrects potentially corrupted tokens, and iteratively reconstructs missing content from the received evidence and global visual context. By co-designing token quantity, unequal protection, and reliability-aware recovery for image-level reconstruction quality, AdapToC achieves a peak mean PSNR gain of 4.20 dB over the strongest static baseline under matched communication costs and state-of-the-art performance among the evaluated visual-token communication methods.

cs.CV↗

arXiv2026

Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN)

Mobile embodied AI networks (MEAN) enable embodied agents to perceive, reason, communicate, and act in wireless environments. In such networks, agent mobility can improve channel conditions, while semantic compression can reduce transmission payloads. However, movement consumes energy, and stronger compression incurs additional computational cost. This paper studies joint movement, semantic compression, and transmit power design for an uplink MEAN system. We formulate a max-min energy efficiency (EE) problem by jointly optimizing transmit power, movement distance, and semantic compression ratio under controllable power constraints. The problem is non-convex due to the coupled signal-to-interference-plus-noise ratio (SINR), mobility-dependent channel gains, and fractional EE objective. To solve it, we propose an alternating optimization (AO)-Dinkelbach algorithm, where the fractional objective is handled by the Dinkelbach transformation, transmit power is updated via successive convex approximation (SCA), and movement distance is updated by coordinate-wise grid search. Simulation results show that the proposed scheme outperforms no-mobility and no-compression baselines, demonstrating the benefit of jointly exploiting mobility control, semantic compression, and power allocation in MEAN.

cs.IT↗

arXiv2026

Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin

The acquisition of high-dimensional channel state information (CSI) in wireless MIMO-OFDM communications usually requires high pilot overhead, or relies on accurate and complete positional or environmental information. In this paper, we propose a geometry-aided channel deduction (GCD) approach, which utilizes an uncalibrated digital twin (DT) with only approximate environmental geometry and positions to assist the channel acquisition. The key rationale behind is that, even imprecise geometric information, which can be easily obtained in advance through radio sensing technologies or existing geographic databases, provides certain structural features about the current channel; meanwhile, the coarse instantaneous channel estimates using only a small amount of pilots provide dedicated information that aligns with the channel structure and further compensates for the geometry inaccuracy and other channel unknowns. To this end, we first extract geometric features from the DT, which contain only simple structural information of the channel. Then we propose random prompt augmentation, a novel method to generate an appropriate prompt that converts geometric multi-path structure into a CSI-like representation while suppressing the disturbance of other unknown channel parameters. The prompt is then fused with the pilot-based instantaneous channel estimate via a channel deduction network. To further enhance the network's versatility, we incorporate pilot configurations into the existing learning architecture to support variable pilot patterns. Comprehensive experiments validate the superiority of the proposed method, which demonstrates high channel acquisition quality, low pilot overhead, and strong robustness. Furthermore, the structural prompt also serves as scenario-related context, enabling our approach to generalize well in new scenarios.

eess.SP↗

arXiv2026

MIMO-OFDM AI Receiver Based on Incrementally Conditioned Diffusion with Soft Decision

Conventional iterative receiver usually begins with channel estimation using sparse pilot observations and follows with data detection based on the channel estimates, and then updates channel estimation using decision feedback, and so on. In Artificial Intelligence (AI)-based receiver design, it is also crucial to make use of such progressively enriched data observations to enhance the generative channel estimation. However, the statistical characteristics and reliability of the data decisions always evolve with the channel estimation processes, which brings great challenges to the design of the overall learning framework and algorithms. In this paper, we propose \textit{Diff-Rx}, an incrementally conditioned diffusion-based receiver with co-designed data detection and channel estimation, for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Specifically, we develop a condition-adaptive post-training method which enables the generative channel estimator to adapt to the conditioning inputs that are progressively enriched by soft data decisions, and also to implicitly align the pilot- and data-induced channel feature spaces to mitigate potential estimation errors. The threshold-free soft decisions provide smooth condition updates without condition-specific reliability tuning. We further develop a conditional diffusion transformer that is capable of performing robust channel estimation under noisy observations and various pilot patterns while reducing the conventional multi-step diffusion generation to one single step. Simulations on both statistical and site-specific ray-tracing channels show that, Diff-Rx exhibits consistent gains across different noise levels, pilot densities and modulation schemes, and works well at a pilot density as low as 1/32 while achieving significant improvement in channel estimation and data detection performances.

eess.SP↗

arXiv2026

Quantum Compiler Design for Fault-Tolerant Quantum Computing

Scalable quantum computation is expected to rely on fault-tolerant quantum computation (FTQC), in which quantum error correction (QEC) suppresses physical errors sufficiently to support reliable logical operations. This requires quantum compilation to move beyond general-purpose circuit optimization toward encoding-aware and protocol-structured compilation across the full stack of fault-tolerant quantum computers. Beyond circuit synthesis and hardware mapping, an FTQC compiler must lower algorithm-level operations into the logical gate set supported by the chosen code, coordinate encoded data and ancilla resources, realize logical operations together with repeated syndrome extraction under hardware constraints, and provide the resulting measurement stream to real-time decoding. This survey presents a full-stack view of compiler design for QEC-protected quantum computation. We organize existing work into three interacting layers: logical-level QEC compilation, physical-level QEC realization, and decoder runtime integration. At the logical level, we review surface-code lattice-surgery compilers, beyond-surface-code code-surgery frameworks including emerging qLDPC approaches, and compilation support for non-Clifford operations such as magic-state distillation and code switching. At the physical level, we survey hardware-aware QEC realization on superconducting, trapped-ion, and neutral-atom platforms. We further examine decoder models, real-time decoding systems, and frame-management mechanisms that close the feedback loop during fault-tolerant execution. Finally, we identify open challenges in cross-layer optimization, qLDPC compilation, compiler-decoder co-design, runtime adaptivity, and the development of integrated and benchmarkable FTQC compilation stacks. An actively maintained paper list is available at: github.com/chenghongz/QEC-compiler-design.

quant-ph↗

arXiv2026

GadIR: A Spatial-Topology Preserving Compiler for Quantum Many-Body Systems Simulation

Simulating quantum many-body systems has been one of the most important applications of quantum computation. For simulation, the Hamiltonian of a physical system is compiled into quantum programs with native instructions for quantum hardware. In previous works, the Hamiltonian is represented as Pauli strings, then compiled and optimized based on the quantum circuit model. Such representation paradigm neglects the spatial topology of original physical models, which is vital information to reducing the overhead of compiling many-body systems Hamiltonians. To address such neglect, we introduce a spatial-topology preserving compiler for quantum many-body simulation. Using Pauli gadgets as the representations of the Hamiltonian, we introduce our intermediate representation -- GadIR, to preserve the spatial-topology information of original physical models. Our compiler frontend performs the group reduction algorithm based on Pauli gadget model, which is a hardware-independent optimization. Our compiler backend performs trotterization and scheduling on Pauli gadgets, then synthesizes the Pauli gadgets into hardware-native quantum programs. We evaluate our compiler on all the canonical quantum many-body system models, while achieving a significant reduction on compilation overhead regarding four major quantum architectures. Overall, our spatial-topology preserving IR exploits the compilation optimization space for quantum many-body systems Hamiltonian.

quant-ph↗

arXiv2026

CRB-Guided Sensing and Resource Allocation for Human Pose Prediction in Integrated Sensing, Communication, and Computation Systems

Integrated sensing, communication, and computation (ISCC) provides a promising framework for indoor human-centric applications. In these applications, short-term human pose prediction facilitates continuous human pose tracking and proactive resource allocation. This paper proposes a Cramer-Rao bound (CRB)-guided sensing framework and investigates a problem of minimizing prediction error in resource-constrained ISCC systems. Specifically, a pose prediction model (ET-Mamba) is first developed to predict human joint positions for continuous tracking. To account for computation-resource limitations, lightweight prediction heads are attached to different inference layers, enabling adaptive-depth pose prediction. A CRB-guided perturbation strategy is then introduced to translate sensing uncertainty at different sensing SNR levels into point-cloud perturbations. Based on that, an empirical relationship among pose prediction error, sensing SNR, and model inference depth is established. Furthermore, to improve prediction accuracy under limited resources, this paper formulates a resource allocation optimization problem that minimizes the pose prediction error by jointly optimizing the beamforming matrix, model inference depth, and computation frequency. To solve this mixed-integer non-convex optimization problem, we propose an alternating optimization (AO)-based algorithm, where closed-form updates and semidefinite programming (SDP) are integrated into the iterative solution process. Simulation results show that the proposed method effectively improves pose prediction performance by up to 35 percent under resource constraints, verifying the effectiveness of conducting joint sensing, communication, and computation design in ISCC systems.

cs.IT↗

arXiv2026

Bunny Codes: Broadening Superconducting Quantum Error Correction Capability through Advanced Control Engineering

Drawing on advances in superconducting qubit control schemes that unlock enriched native gate sets at the hardware level, we systematically examine how harnessing this enlarged physical two-qubit gate pool---specifically CNOT and CXSWAP---streamlines syndrome extraction for certain qLDPC codes with nonlocal stabilizers. Through an exhaustive search, we discover a set of qLDPC codes with various stabilizer weights and distances that can be implemented on the two-dimensional nearest-neighbor qubit connectivity native to superconducting hardware while achieving performance equivalent to that of the direct CNOT implementation requiring long-range interactions. We refer to those codes as Bunny codes. Across all code distances we examine, the best Bunny codes with weight-6 stabilizers in periodic boundary conditions have a code rate approximately $3\times$ that of the toric code; when converted to open boundary conditions, they retain an approximately $2\times$ code rate advantage over the rotated surface code. In circuit-level simulation, we find that some Bunny codes exhibit logical error rates an order of magnitude lower than toric codes with comparable code rates. Our results demonstrate that high-performance quantum error correction can be achieved using an expanded gate set rather than long-range couplers, thereby significantly reducing hardware complexity.

quant-ph↗

arXiv2026

Efficient Compilation for Hamiltonian Simulation via Global Binary Symplectic Form Simplification

Hamiltonian simulation is a core quantum workload, underpinning variational quantum algorithms and Trotterized time evolution. Such programs are expressed as Pauli exponential sequences, exhibiting structural patterns that are highly amenable to high-level synthesis and optimization. Existing compilers, however, fail to fully unlock the optimization potential of their global algebraic structure, even when employing advanced graph- or tableau-based methods. We present Symphony, a holistic compilation approach built on the binary symplectic form (BSF) representation of Pauli strings. Unlike prior group-wise BSF simplification and path-based Pauli network synthesis, Symphony applies generalized controlled-Pauli Clifford transformations directly to a global BSF tableau, adaptively reducing active Pauli rows and emitting eligible two-qubit blocks other than single-qubit rotations in a forward Clifford frame. Following algebraic simplification, Symphony performs a causality-preserving block rescheduling heuristic that respects frame-induced dependencies while exposing extensive two-qubit block parallelism opportunities. This streamlined compilation style comprehensively exploits simultaneous simplification and commutativity opportunities, achieving efficient global optimization without relying on computationally expensive heuristics or long-horizon searches. Across the generic Hamiltonian simulation benchmarks in HamLib, Symphony achieves average reductions of 59% in two-qubit gate count and 91% in circuit depth. It strictly Pareto-dominates prior state-of-the-art compilers, requiring 1.14--1.58$\times$ fewer two-qubit gates and especially shrinking two-qubit circuit depth by a substantial factor of 1.87--5.67$\times$ on average.

quant-ph↗

arXiv2026

Lifting connectivity bottlenecks in superconducting quantum processors via enriched native two-qubit gates

Limited qubit connectivity is a central architectural constraint in superconducting quantum processors, whose planar layouts require additional gates to mediate interactions between distant qubits. Here, we use the AshN control scheme, where rich two-qubit control on every nearest-neighbour pair allows a logical interaction and the required qubit routing to be merged into a single native operation, effectively transforming a sparse hardware graph into a more connected computational architecture. For the benchmark instances studied, the resulting synthesis capability enables reliable execution on constrained one- and two-dimensional lattices, with compiled two-qubit gate counts approaching those of an all-to-all-connected reference. Across seven benchmark circuits on one- and two-dimensional topologies, the AshN-based implementation achieves geometric-mean reductions of $45.2\%$ and $43.7\%$ in two-qubit gate count compared with controlled-Z-based compilation, respectively. Using AshN gates, we prepare an eight-qubit two-excitation Dicke state with a fidelity of $0.736$ and certify its genuine multipartite entanglement using a fully positive-partial-transpose witness, whereas the same witness does not certify entanglement for the CZ-based implementation. The state fidelity and entanglement certification remain robust across the tested lattice configurations, including those with up to three connectivity defects. Our work establishes native-gate engineering as a practical approach to mitigating connectivity constraints.

quant-ph↗

arXiv2026

Recursive Flow: A Generative Framework for MIMO Channel Estimation

Channel estimation is a fundamental challenge in massive multiple-input multiple-output systems, where estimation accuracy governs the spectral efficiency and link reliability. In this work, we introduce Recursive Flow (RC-Flow), a novel solver that leverages pre-trained flow matching priors to robustly recover channel state information from noisy, under-determined measurements. Different from conventional open-loop generative models, our approach establishes a closed-loop refinement framework via a serial restart mechanism and anchored trajectory rectification. By synergizing flow-consistent prior directions with data-fidelity proximal projections, the proposed RC-Flow achieves robust channel reconstruction and delivers state-of-the-art performance across diverse noise levels, particularly in noise-dominated scenarios. The framework is further augmented by an adaptive dual-scheduling strategy, offering flexible management of the trade-off between convergence speed and reconstruction accuracy. Theoretically, we analyze the Jacobian spectral radius of the recursive operator to prove its global asymptotic stability. Numerical results demonstrate that RC-Flow reduces inference latency by two orders of magnitude while achieving a 2.7 dB performance gain in low signal-to-noise ratio regimes compared to the score-based baseline.

cs.IT↗

arXiv2026

Federated Unlearning Over Wireless Networks

To comply with stringent data privacy regulations, federated unlearning (FU) has emerged as a critical paradigm. However, its implementation over wireless networks introduces severe communication latency and reliability challenges due to iterative calibration requirements and physical-layer channel uncertainties. In this paper, we investigate the problem of delay minimization for federated unlearning networks (FUN). Specifically, we establish a comprehensive system model that jointly incorporates the convergence behavior of the FUN algorithm, local device computation dynamics, and a worst-case robust transmission model operating under bounded channel state information (CSI) error. To solve the resulting non-convex joint resource allocation problem, we propose an efficient iterative algorithm. By exploiting the monotonicity and convexity properties of the system constraints, the problem is decomposed via a uniform scan over the local accuracy parameter, within which the optimal delay, bandwidth, power, and computation frequency are determined utilizing nested bisection and golden-section searches. Both theoretical analysis and extensive numerical results demonstrate that the proposed algorithm achieves polynomial complexity and significantly reduces the overall unlearning completion time compared to conventional baseline schemes.

cs.IT↗

arXiv2026

Integrated Sensing, Communication, and Computing in Multi-Tier Systems: Joint Hybrid Beamforming Design and Computation Resource Allocation

This paper proposes a novel integrated sensing, communication, and computing (ISCC) framework over a cloud-edge-device collaborative architecture, where passive sensing is enabled by reusing uplink offloading signals to extract sensing information directly at the edge without incurring additional transmission overhead. Nevertheless, such signal reuse introduces an inherent tradeoff between communication efficiency and sensing coverage. To address this challenge, we adopt a hybrid beamforming architecture under practical hardware constraints. In addition, the integration of sensing tasks creates significant resource contention at the mobile edge computing (MEC) server, where latency-sensitive device tasks and computation-intensive sensing inference tasks compete for limited processing capacity. To alleviate this computation burden, we introduce a split inference mechanism that strategically partitions intelligent sensing tasks between the edge and the cloud. Building upon this framework, we formulate a joint optimization problem to minimize the average computation latency of all device tasks subject to strict sensing performance constraints. To tackle the high non-convexity of the formulated problem, we develop an efficient alternating optimization algorithm. In particular, we design a two-layer framework to jointly determine the optimal DNN splitting point and computation resource allocation and employ a weighted minimum mean square error (WMMSE)-based approach with manifold optimization for hybrid beamforming design. Numerical results demonstrate that the proposed framework achieves a superior tradeoff between sensing accuracy and computation latency compared to the benchmark schemes.

eess.SP↗

arXiv2026

Energy Efficiency Maximization for FAS-Assisted Downlink Communication in Mobile Embodied AI Networks (MEAN) over Interference Channels

In this paper, we investigate a fluid antenna system (FAS)-assisted downlink mobile embodied AI network (MEAN) over interference channels, where multiple base station (BS)-agent pairs reuse the same spectrum. The BSs employ FASs to improve the communication quality, while the mobile embodied artificial intelligence (AI) agents can adjust their positions according to environment-aware channel information, such as a channel-to-interference-plus-noise map (CINM). Considering both co-channel interference and the energy consumption caused by communication and agent movement, we formulate an energy efficiency (EE) maximization problem by jointly optimizing the agent positions, FAS port selections, and transmit powers. To solve this mixed-integer non-convex problem, we first derive the optimal transmit power in closed form for given agent positions and FAS ports. We then develop an iterative algorithm with adaptive FAS-port optimization and sequential agent-position optimization, together with a low-complexity power-update method. Simulation results demonstrate that the proposed design outperforms the considered benchmark schemes and provides improved feasibility under severe noise conditions.

eess.SP↗