arXiv subjects

Robert Caiming Qiu

Publications and source records attributed to Robert Caiming Qiu.

At least 19 recordsLinked to original sources

arXiv2026

CMT-Aware Channel Modeling and Transmit-Power Minimization for Pinching-Antenna Systems

This letter investigates transmit-power minimization for multiuser pinching-antenna system (PAS) from a coupled-mode-theory (CMT)-aware perspective. Existing CMT-based pinching antenna (PA) studies reveal coupling-induced power exchange and radiation behavior, but these effects have not been fully embedded into system-level multi-PA channel modeling and beamforming design. We therefore develop a directional and loss-aware channel model that captures coupling-length-dependent power extraction and the downstream guided-power reduction caused by in-waveguide attenuation and upstream extraction. The model shows that PA design should account for both directional radiation and guided-power evolution, rather than only propagation distance or maximum coupling considered in most existing works. Based on this channel model, we formulate a quality-of-service (QoS)-constrained power minimization problem for continuous PA positioning and finite-codebook activation. For each candidate coupling length, the element-wise positioning and BPSO-based activation use a closed-form zero-forcing (ZF) power metric for low-complexity configuration ranking, thereby avoiding repeated beamforming optimization while excluding rank-deficient candidates and ordering the remaining ones. The selected configuration for each coupling length is then evaluated by optimal fixed-configuration QoS beamforming. Simulation results demonstrate that CMT-aware modeling fundamentally reshapes the preferred PA configuration, maximum coupling is not always power-efficient due to suppressed downstream PA contributions, and finite-codebook activation combined with ZF-based ranking provides a balance between transmit-power performance and deployment complexity.

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arXiv2026

Encoded Jamming Secure Communication for RIS-Assisted Systems

This paper investigates a cooperative jamming (CJ)-aided secure wireless communication system. Conventional CJ schemes transmit Gaussian noise (GN) to improve security, which inherently degrades the legitimate receiver's performance. While the encoded jamming (EJ) scheme mitigates this interference, its advantage over the GN scheme is highly channel-dependent. To address this limitation, we introduce a reconfigurable intelligent surface (RIS)-assisted secure design based on the EJ scheme for secrecy-rate maximization. Starting from the multiple-input multiple-output (MIMO) secrecy-rate expressions for the EJ and GN schemes, we identify the jamming-channel conditions under which the EJ scheme may be limited. For the general MIMO setup, we develop a weighted minimum mean-square error (WMMSE)-based framework for joint precoder and RIS phase optimization. We further provide a large-RIS asymptotic benchmark showing that a jamming-aligned EJ construction achieves a positive gap over a signal-aligned GN benchmark in the considered multiple-input single-output (MISO) setting. Simulation results support the analysis and show that, under the evaluated settings, the RIS-assisted EJ scheme can alleviate the identified spatial bottlenecks and improve the secrecy rate relative to the considered baselines.

cs.IT↗

arXiv2026

V-RIS: Virtual-Aperture DoA Estimation with Sparse RIS

Large-aperture reconfigurable intelligent surfaces (RISs) enable high-resolution 2D direction-of-arrival (DoA) estimation, but existing approaches still tie hardware cost and control overhead to aperture size. To decouple the effective sensing aperture from the number of physically deployed RIS elements, we present V-RIS, a framework for virtual-aperture surface-field reconstruction and DoA estimation. V-RIS uses only four corner subarrays and a single-antenna receiver to reconstruct the virtual-aperture surface field from receiver observations collected under multiple RIS phase configurations, and then performs DoA estimation on the reconstructed virtual-aperture surface field. Our key observation is that, under far-field illumination, the discretized RIS surface field satisfies finite-order spatial recurrences along both aperture axes. We enforce data-level consistency through the RIS-coded receiver observations and propagation consistency through the far-field spatial recurrence, while using a four-corner deployment geometry that retains both contiguous local elements and long aperture baselines. To improve robustness in practical receiver observations, we adopt a bias-invariant receiver-domain loss that suppresses quasi-static hardware distortions and configuration-invariant multipath contributions. Extensive simulations show that V-RIS approaches the DoA accuracy of a full-aperture benchmark while producing cleaner spectra than matrix-completion and least-squares baselines. An outdoor prototype further validates the design: with only 25\% programmable elements, V-RIS keeps both elevation and azimuth errors within $1^\circ$ of the ground truth.

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arXiv2026

FFAvatar: Feed-Forward 4D Head Avatar Reconstruction from Sparse Portrait Images

We present FFAvatar, a Transformer-based 3D Gaussian framework for fast construction of high-quality and animatable 4D head avatars from one or more reference portrait images. Unlike existing feed-forward approaches that require a fixed number of input views, FFAvatar supports incremental reconstruction, progressively refining the avatar representation as additional reference images become available. At the core of our method is an alternating attention mechanism that disentangles identity appearance from expression and viewpoint variations, enabling the reconstruction of a canonical 3D appearance that remains consistent across poses and facial expressions. To balance visual fidelity and computational efficiency, we introduce a sparse-to-dense learning paradigm. Coarse appearance features are first learned using sparse primitives anchored to the FLAME vertex level and are subsequently densified in the UV domain to capture fine-grained geometric and texture details. We further propose a plug-and-play motion refinement module that enables subject-specific dynamic personalization by modeling residual motion beyond parametric deformation. Extensive experiments demonstrate that FFAvatar efficiently produces high-fidelity and controllable 4D head avatars, achieving superior flexibility, driving efficiency, and identity-consistent rendering across diverse expressions and viewpoints.

cs.CV↗

arXiv2026

Reliability-Constrained Blind Beam Alignment for Backscatter-MIMO mounted Target in Cluttered Multipath Channels

Practical ISAC is constrained by static clutter and NLoS multipath, which obscure target-coupled echoes and induce spurious peaks for beam alignment. Existing receiver-side methods largely model targets as passive scatterers, limiting the structural separability of target echoes from the environment. This paper establishes a structural correspondence between these limitations and target-side Backscatter-MIMO responses: reflection modulation enables waveform-domain separation from unmodulated clutter, while retro-directional passive beamforming concentrates the tagged echo toward the BS-facing direction and suppresses NLoS-induced false-peak locking. To operationalize this correspondence, dual-end spatial locking is required to overcome cascaded backscatter loss and provide beam-domain angular information. We propose a downlink-triggered blind dual-end alignment protocol that jointly selects the BS and Backscatter-MIMO codeword indices from the tagged echo observed at the BS, without pilots, CSI feedback, or target synchronization. We further derive a clutter-aware remodulation waveform robust to fractional timing offsets and construct adjustable-width BS/Backscatter-MIMO codebooks via quadratic phase spoiling. For reliability characterization, we derive closed-form expressions for the coherence-averaged end-to-end success probability. The analysis shows that beam narrowing is not universally beneficial: in NLoS-dominated regimes, enlarging the array aperture may degrade alignment reliability. The optimal beamwidth is instead governed by cross-phase competition between discovery and alignment, yielding a nontrivial feasible region with an analytically characterized boundary. Simulations validate the analysis and demonstrate improved reliability-gated locked-link performance under strong clutter, severe NLoS multipath, and finite coherence time.

cs.IT↗

arXiv2026

A Low-Complexity Framework for Multi-access Coded Caching Systems with Arbitrary User-cache Access Topology

This paper studies the multi-access coded caching (MACC) problem with arbitrary user-cache access topology, which extends existing MACC models that rely on highly structured and combinatorially designed topologies. We consider a MACC system consisting of a single server, $Λ$ cache-nodes, and $K$ user-nodes. The server stores $N$ equal-size files, each cache-node has a storage capacity of $M$ files, and each user-node $k\in[K]$ can access an arbitrary subset of cache-nodes $\mathcal{A}_k\subseteq[Λ]$ and retrieve the cached content stored in cache-nodes $\mathcal{A}_k$. The objective is to design a universal framework for the MACC delivery problem. Decoding conflicts among the requested packets are captured by a conflict graph, and the design of the delivery is reduced to a graph coloring problem, where achieving a lower transmission load corresponds to coloring the graph using fewer colors. Under this formulation, the classical DSatur algorithm achieves a transmission load close to the index-coding (IC) converse bound, thereby providing a practical benchmark. However, its computational complexity becomes prohibitive for large-scale graphs. To overcome this limitation, we develop a learning-driven approach using graph neural networks (GNNs) that efficiently constructs coded multicast transmissions with performance close to the theoretical bounds and generalizes across different user-cache access topologies and numbers of users. In addition, we extend the IC converse bound to MACC systems with arbitrary access topology and propose a low-complexity greedy approximation that closely matches the IC converse bound. Numerical results demonstrate that the proposed approach achieves performance close to the DSatur algorithm and the IC converse bound, while significantly reducing computational complexity, making it well-suited for large-scale MACC systems.

cs.IT↗

arXiv2026

On Secure Gradient Coding with Uncoded Groupwise Keys

This paper considers a new secure gradient coding problem with uncoded groupwise keys, formalized as a (K, N, N_r, M, S) secure gradient coding model, where a user aims to compute the sum of the gradients from K datasets with the assistance of N distributed servers. We consider arbitrary heterogeneous data assignment, where each dataset is assigned to at least M servers. The user should recover the sum of gradients from the transmissions of any N_r servers. The security constraint guarantees that even if the user receives the transmitted messages from all servers, it cannot obtain any other information about the datasets except the sum of gradients. Compared to existing secure gradient coding works, we introduce a practical constraint on secret keys, namely uncoded groupwise keys, where the keys are mutually independent and each key is shared by precisely S servers. An achievable secure gradient coding scheme with uncoded groupwise keys is proposed, which is then proven to be optimal if S > M and to be order optimal within a factor of 2 otherwise.

cs.IT↗

arXiv2026

Radio-Frequency Inverse Rendering for Wireless Environment Modeling

Neural rendering paradigms have recently emerged as powerful tools for radio frequency (RF). However, by entangling RF sources with scene geometry and material properties, existing approaches limit downstream manipulation of scene geometry, wireless system configuration, and RF reasoning. To address this, we propose a physically grounded RF inverse rendering (RFIR) framework that explicitly decouples RF emission, geometry, and material electromagnetic properties. Our key insight is an RF-aware bidirectional scattering distribution function, embedded into the Gaussian splatting paradigm as an RF rendering equation. Each Gaussian primitive is endowed with intrinsic physical attributes, including surface normals, material electromagnetic parameters, and roughness, and leveraged by a customized ray-tracing scheme to represent RF signal synthesis. The proposed RFIR generalizes three typical RF tasks: radar cross-section synthesis, received signal strength indicator prediction, and wireless scene editability. Experiments demonstrate significant performance advantages, underscoring the potential for wireless world modeling.

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arXiv2026

Multiaccess Coded Caching with Heterogeneous Retrieval Costs

The multiaccess coded caching (MACC) system, as formulated by Hachem {\it et al.}, consists of a central server with a library of $N$ files, connected to $K$ cache-less users via an error-free shared link, and $K$ cache nodes, each equipped with cache memory of size $M$ files. Each user can access $L$ neighboring cache nodes under a cyclic wrap-around topology. Most existing studies operate under the strong assumption that users can retrieve content from their connected cache nodes at no communication cost. In practice, each user retrieves content from its $L$ different connected cache nodes at varying costs. Additionally, the server also incurs certain costs to transmit the content to the users. In this paper, we focus on a cost-aware MACC system and aim to minimize the total system cost, which includes cache-access costs and broadcast costs. Firstly, we propose a novel coded caching framework based on superposition coding, where the MACC schemes of Cheng \textit{et al.} are layered. Then, a cost-aware optimization problem is derived that optimizes cache placement and minimizes system cost. By identifying a sparsity property of the optimal solution, we propose a structure-aware algorithm with reduced complexity. Simulation results demonstrate that our proposed scheme consistently outperforms the scheme of Cheng {\it et al.} in scenarios with heterogeneous retrieval costs.

cs.IT↗

arXiv2025

Source Localization and Power Estimation through RISs: Performance Analysis and Prototype Validations

This paper investigates the capabilities and effectiveness of backward localization centered on reconfigurable intelligent surfaces (RISs). In the backward sensing paradigm, the region of interest (RoI) is illuminated using a set of diverse radiation patterns. These patterns encode spatial information into a sequence of measurements, which are subsequently processed to reconstruct the RoI. We show that a single RIS can estimate the direction of arrival of incident waves by leveraging configurational diversity, and that the spatial diversity provided by multiple RISs further improves the accuracy of source localization and power estimation. The underlying structure of the sensing operator in the multi-snapshot measurement process is clarified. For single-RIS localization, the sensing operator is decomposed into a product of structured matrices, each corresponding to a specific physical process: wave propagation to and from the RIS, the relative phase offsets of elements with respect to the reference point, and the applied phase configuration of each element. A unified framework for identifying key performance indicators is established by analyzing the conditioning of the sensing operators. In the multi-RIS setting, we derive--via rank analysis--the governing law among the RoI size, the number of elements, and the number of measurements. Upper bounds on the relative error of the least squares reconstruction algorithm are derived. These bounds clarify how key performance indicators affect estimation error and provide valuable guidance for system-level optimization. Numerical experiments confirm that the trend of the relative error is consistent with the theoretical bounds.

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arXiv2025

Metasurface-Enabled Superheterodyne Transmitter With Decoupled Harmonic-Free Signal Generation and Precoding

The evolution of programmable metasurfaces (PM) from passive beamforming to active information transmission marks a paradigm shift for next-generation wireless systems. However, this transition is hindered by fundamental limitations in conventional metasurface transmitter architectures, including restricted modulation orders, symbol-level spatial inconsistency, and significant harmonic interference. These issues stem from the intrinsic coupling between baseband signal processing and radio-frequency beamforming in monolithic designs reliant on simplistic switching mechanisms. This paper proposes a novel metasurface-enabled superheterodyne architecture (MSA) that fundamentally decouples these functionalities. The MSA introduces a dual-stage up-conversion process, comprising a digital up-conversion module for in-phase/quadrature modulation and baseband-to-intermediate frequency conversion, a precoder module for precoding, and a custom-designed magnitude-phase-decoupled metasurface that acts as a reconfigurable reflective mixer array. This decoupling of harmonic-free waveform generation from spatial precoding overcomes the critical drawbacks of existing approaches. Experimental results from a 5.8 GHz proof-of-concept prototype system validate the MSA's superior performance. The system generates spatially isotropic constellations for arbitrary-order QAM modulations, ensures consistent time-frequency signatures for applications like Doppler-spoofing, and achieves data rates up to 20 Mbps within a linear operating region that minimizes nonlinear distortion. The capability of employing spatial diversity and multi-stream interference cancellation has been demonstrated for the first time in a PM-based transmitter.

cs.IT↗

arXiv2025

Reconfigurable Intelligent Surface-Assisted Multiple-Antenna Coded Caching

Reconfigurable Intelligent Surface (RIS) has emerged as a promising technology to enhance the wireless propagation environment for next-generation wireless communication systems. This paper introduces a new RIS-assisted multiple-antenna coded caching problem. Unlike the existing multi-antenna coded caching models, our considered model incorporates a passive RIS with a limited number of elements aimed at enhancing the multicast gain (i.e., Degrees of Freedom (DoF)). The system consists of a server equipped with multiple antennas and several single-antenna users. The RIS, which functions as a passive and configurable relay, improves communication by selectively erasing certain transmission paths between transmit and receive antennas, thereby reducing interference. We first propose a new RIS-assisted interference nulling algorithm to determine the phase-shift coefficients of the RIS. This algorithm achieves faster convergence compared to the existing approach. By strategically nulling certain interference paths in each time slot, the transmission process is divided into multiple interference-free groups. Each group consists of a set of transmit antennas that serve a corresponding set of users without any interference from other groups. The optimal grouping strategy to maximize the DoF is formulated as a combinatorial optimization problem. To efficiently solve this, we design a low-complexity algorithm that identifies the optimal solution and develops a corresponding coded caching scheme to achieve the maximum DoF. Building on the optimal grouping strategy, we introduce a new framework, referred to as RIS-assisted Multiple-Antenna Placement Delivery Array (RMAPDA), to construct the cache placement and delivery phases. Then we propose a general RMAPDA design to achieve the maximum DoF under the optimal grouping strategy.

cs.IT↗

arXiv2025

Fundamental Limits of Distributed Linearly Separable Computation under Cyclic Assignment

This paper studies the master-worker distributed linearly separable computation problem, where the considered computation task, referred to as linearly separable function, is a typical linear transform model widely used in cooperative distributed gradient coding, real-time rendering, linear transformers, etc. %A master asks $\Nsf$ distributed workers to compute a linearly separable function from $\Ksf$ datasets. The computation task on $\Ksf$ datasets can be expressed as $\Ksf_{\rm c}$ linear combinations of $\Ksf$ messages, where each message is the output of an individual function on one dataset. Straggler effect is also considered, such that from the answers of any $\Nsf_{\rm r}$ of the $\Nsf$ distributed workers, the master should accomplish the task. The computation cost is defined as the number of datasets assigned to each worker, while the communication cost is defined as the number of (coded) messages that should be received. The objective is to characterize the optimal tradeoff between the computation and communication costs. The problem has remained so far open, even under the cyclic data assignment.Since in fact various distributed computing schemes were proposed in the literature under the cyclic data assignment, with this paper we close the problem for the cyclic assignment. This paper proposes a new computing scheme with the cyclic assignment based on the concept of interference alignment, by treating each message which cannot be computed by a worker as an interference from this worker. Under the cyclic assignment, the proposed computing scheme is then proved to be optimal when $\Nsf=\Ksf$ and be order optimal within a factor of $2$ otherwise.

cs.IT↗

arXiv2025

Metasurface-Enabled Superheterodyne Transmitter for Arbitrary-Order Modulation with Spatially Isotropic Symbol Distribution

Electromagnetically programmable information metasurfaces, as dynamically controllable 2D metamaterials, hold significant promise as low-profile hardware enabling passive wave control and signal generation for backscatter systems. However, current metasurface-based transmitters architecture fundamentally suffer from hardware non-modularization, forcing all transmitter functions onto nonlinear switch-based unit cells, which introduces symbol mapping inconsistency via phase coupling. Moreover, both temporal coding (limited by unit cell diodes) and space-time coding (impaired by symbol anisotropy) exhibit irreducible harmonic interference and entangled control of amplitude, phase, and beam direction. This paper proposes a metasurface-enabled superheterodyne architecture (MSA), comprising a digital up-conversion (DUC) module performing baseband-to-intermediate frequency (IF) conversion, filtering, and digital-to-analog conversion (DAC), and a reconfigurable metasurface featuring programmable unit cells that independently control both the magnitude and phase of the reflection coefficient. Systematically, the architecture leverages a dual-stage up-conversion process, typical of superheterodyne systems, but uniquely employs the metasurface for the final RF conversion stage. Building upon this framework, a proof-of-concept prototype featuring a 5.8 GHz magnitude-phase decoupled (MPD) metasurface (<15 degree phase deviation per state) and a DAC-based DUC module is presented. Extensive validation confirms the metasurface's capability for distortion-free mixing with arbitrary IF signals while maintaining consistent radiation patterns. The prototype successfully implements diverse QAM modulation schemes (4QAM to 256QAM) in mono-static and bi-static configurations, demonstrating symbol isotropy for spatially separated receivers and achieving a data rate of approximately 20 Mbps (at 5 MHz IF)...

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arXiv2025

Frequency-Space Channel Estimation and Spatial Equalization in Wideband Fluid Antenna System

The Fluid Antenna System (FAS) overcomes the spatial degree-of-freedom limitations of conventional static antenna arrays in wireless communications.This capability critically depends on acquiring full Channel State Information across all accessible ports. Existing studies focus exclusively on narrowband FAS, performing channel estimation solely in the spatial domain. This work proposes a channel estimation and spatial equalization framework for wideband FAS, revealing for the first time an inherent group-sparse structure in aperture-limited FAS channels. First, we establish a group-sparse recovery framework for space-frequency characteristics in FAS, formally characterizing leakage-induced sparsity degradation from limited aperture and bandwidth as a structured group-sparsity problem. By deriving dictionary-adapted group restricted isometry property, we prove tight recovery bounds for a convex $\ell_1/\ell_2$-mixed norm optimization formulation that preserves leakage-aware sparsity patterns. Second, we develop a descending correlation group orthogonal matching pursuit algorithm that systematically relaxes leakage constraints to reduce subcoherence. This approach enables FSC recovery with accelerated convergence and superior performance compared to conventional compressive sensing methods like OMP or GOMP. Third, we formulate spatial equalization as a mixed-integer linear programming problem, complement this with a greedy algorithm maintaining near-optimal performance. Simulation results demonstrate the proposed channel estimation algorithm effectively resolves energy misallocation and enables recovery of weak details, achieving superior recovery accuracy and convergence rate. The SE framework suppresses deep fading phenomena and largely reduces time consumption overhead while maintaining equivalent link reliability.

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arXiv2025

Blind and Topological Interference Managements for Bistatic Integrated Sensing and Communication

Integrated sensing and communication (ISAC) systems provide significant enhancements in performance and resource efficiency compared to individual sensing and communication systems, primarily attributed to the collaborative use of wireless resources, radio waveforms, and hardware platforms. This paper focuses on the bistatic ISAC systems with dispersed multi-receiver and one sensor. Compared to a monostatic ISAC system, the main challenge in the bistatic setting is that the information messages are unknown to the sensor and therefore they are seen as interference, while the channel between the transmitters (TX) and the sensor is unknown to the transmitters. In order to mitigate the interference at the sensor while maximizing the communication degree of freedom, we introduce two strategies, namely, blind interference alignment and topological interference management. Although well-known in the context of Gaussian interference channels, these strategies are novel in the context of bistatic ISAC. For the bistatic ISAC models with heterogeneous coherence times or with heterogeneous connectivity, the achieved ISAC tradeoff points in terms of communication and sensing degrees of freedom are characterized. In particular, we show that the new tradeoff outperforms the time-sharing between the sensing-only and the communication-only schemes. Simulation results demonstrate that the proposed schemes significantly improve the channel estimation error for the sensing task, compared to treating interference as noise at the sensor and successive interference cancellation.

cs.IT↗

arXiv2025

Flexible Multi-Beam Synthesis and Directional Suppression Through Transmissive RIS

Despite extensive research on reconfigurable intelligent surfaces (RISs) in recent years, existing beamforming methods still face significant challenges in achieving flexible and robust beam synthesis, which is an essential capability for a wide range of communication scenarios. This paper introduces a Max-min criterion with nonlinear constraints, leveraging optimization techniques to simultaneously enable flexible multi-beam synthesis and directional suppression using transmissive RIS. Firstly, a realistic model grounded in geometrical optics is introduced to characterize the input/output behaviors of transmissive RISs, effectively bridging the gap between explicit beamforming requirements and practical implementations. Subsequently, a highly efficient algorithm for constrained Max-min optimizations involving quadratic forms is developed. By introducing an auxiliary variable and applying the compensated convexity transform, we successfully reformulate the original non-convex problem and obtain the optimal solution iteratively. This approach is readily applicable to a wide range of constrained Max-min optimization problems. Finally, numerical simulations and prototype experiments are conducted to validate the effectiveness of the proposed framework. The results demonstrate that the proposed algorithm can effectively enhance or selectively suppress signal beams in designated spatial directions, outperforming existing methods in terms of beam control accuracy and robustness. This framework provides valuable insights and references for practical communications applications such as physical layer security and interference mitigation.

cs.IT↗

arXiv2025

WiCAL: Accurate Wi-Fi-Based 3D Localization Enabled by Collaborative Antenna Arrays

Accurate 3D localization is essential for realizing advanced sensing functionalities in next-generation Wi-Fi communication systems. This study investigates the potential of multistatic localization in Wi-Fi networks through the deployment of multiple cooperative antenna arrays. The collaborative gain offered by these arrays is twofold: (i) intra-array coherent gain at the wavelength scale among antenna elements, and (ii) inter-array cooperative gain across arrays. To evaluate the feasibility and performance of this approach, we develop WiCAL (Wi-Fi Collaborative Antenna Localization), a system built upon commercial Wi-Fi infrastructure equipped with uniform rectangular arrays. These arrays are driven by multiplexing embedded radio frequency chains available in standard access points or user devices, thereby eliminating the need for sophisticated, costly, and power-hungry multi-transceiver modules typically required in multiple-input and multiple-output systems. To address phase offsets introduced by RF chain multiplexing, we propose a three-stage, fine-grained phase alignment scheme to synchronize signals across antenna elements within each array. A bidirectional spatial smoothing MUSIC algorithm is employed to estimate angles of arrival (AoAs) and mitigate performance degradation caused by correlated interference. To further exploit inter-array cooperative gain, we elaborate on the synchronization mechanism among distributed URAs, which enables direct position determination by bypassing intermediate angle estimation. Once synchronized, the distributed URAs effectively form a virtual large-scale array, significantly enhancing spatial resolution and localization accuracy.

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