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H. Vincent Poor

Publications and source records attributed to H. Vincent Poor.

At least 19 recordsLinked to original sources
arXiv2026

Design of DoF-Robust MIMO via Generalized Pinching Antennas

Spatial degrees of freedom (DoF) are fundamental to the performance of multiple-input multiple-output (MIMO) systems. However, the use of conventional fixed-location antennas introduces disparities in the users' distances to a common base station, which makes the multi-user channel matrix more susceptible to rank deficiency and, consequently, a loss of spatial DoF. This paper shows that generalized pinching antennas can be used to design a new form of MIMO robust to the loss of spatial DoF. In particular, by deploying pinching antennas in close proximity to the users, the resulting multi-user channel Gram matrix can have a well-conditioned, almost diagonal structure, which reduces its sensitivity to the users' spatial deployment as well as the risk of rank deficiency. Two performance metrics, namely the rank-deficiency probability and the outage probability, are used to evaluate the performance of pinching-antenna-assisted MIMO transmission. The presented analytical and simulation results demonstrate the capability of pinching antennas to preserve spatial DoF and provide ubiquitous connectivity by effectively supporting users at spatially separated locations.

cs.IT↗
arXiv2026

Singular Linear Differential-Algebraic Equations with Constant Coefficients Necessarily Admit Non-Computable Solutions

If a regular linear differential-algebraic equation has a unique solution, this solution is known to be Turing computable. In contrast, this paper shows that singular linear differential-algebraic equations exhibit fundamentally different computability properties. More precisely, we show that for every linear differential-algebraic equation (LDAE) with constant computable coefficients, there exists a computable input function with strong regularity properties such that the LDAE admits a unique solution, yet this solution is not Turing computable. In fact, the solution is non-computable at certain computable times.

cs.CC↗
arXiv2026

Generative Diffusion Models for Secure UAV-ISAC with Real-Time Target Tracking

Unmanned aerial vehicle (UAV) platforms with integrated sensing and communication (ISAC) are prime candidates for surveillance missions, in which a dual-function drone tracks a mobile target and alerts a ground station when the target enters a protected region. Such alerts generate event-triggered communication traffic. By observing the change in the traffic, an eavesdropper (Eve) can therefore learn in real time whether the target is detected inside the protected region without decoding any message. Existing physical-layer security techniques protect the message content or hide that a transmission occurs, but the composition of the traffic remains exposed. We formulate the threat as traffic-composition covertness and propose a keyless diffusion-based transmission scheme that hides the triggered traffic. The scheme superposes the beamformed information streams of the UAV with a message-agnostic safeguard waveform generated by a conditional denoising diffusion implicit model. Training shapes the safeguard distribution such that triggered and non-triggered slots become statistically indistinguishable at Eve. The legitimate user shares no secret key with the UAV. We formulate the joint design as a rate maximization under covertness, reliability, and sensing constraints, with the sensing accuracy measured by the posterior Cramér-Rao bound (PCRB) of the beam-dependent tracking problem. Our numerical results show that, with multiple transmit antennas and an Eve signal-to-noise ratio (SNR) of $5$~dB, a learned Eve stays near the blind guess of $0.5$ for every safeguard beam split, a detector that knows the safeguard model reaches $0.71$ to $0.73$ inside Bob's main lobe and $0.53$ to $0.55$ outside it, while the average position PCRB varies only between $0.59$ and $0.70$~m$^2$.

cs.IT↗
arXiv2026

Large Language Models over Networks: Collaborative Intelligence under Resource Constraints

Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a growing class of applications, including those operating under intermittent connectivity, sub-second latency budgets, data-residency constraints, or sustained high-volume inference. On-device deployment is in turn constrained by limited computation and memory. No single endpoint can deliver high-quality service across this spectrum. This article focuses on collaborative intelligence, a paradigm in which multiple independent LLMs distributed across device and cloud endpoints collaborate at the task level through natural language or structured messages. Such collaboration strives for superior response quality under heterogeneous resource constraints spanning computation, memory, communication, and cost across network tiers. We present collaborative inference along two complementary and composable dimensions: vertical device-cloud collaboration and horizontal multi-agent collaboration, which can be combined into hybrid topologies in practice. We then examine learning to collaborate, addressing the training of routing policies and the development of cooperative capabilities among LLMs. Finally, we identify open research challenges including scaling under resource heterogeneity and trustworthy collaborative intelligence.

eess.SP↗
arXiv2026

Fourier-Curve Constellations under Tangential Perturbation: Covariance-Aware Soft Demapping on Coded Links

A Fourier-curve constellation places $M$ points on a closed curve through $k$ complex slots. A Gaussian perturbation along the curve's tangent, whether injected as artificial noise or arising from first-order jitter of the curve parameter, gives every symbol an observation with a symbol-dependent rank-one covariance, and the maximum-likelihood symbol metric differs from the Euclidean rule by one rank-one correction per candidate. We realize this metric as a max-log soft demapper beside a Euclidean correlator bank at $2kM$ additional multiply--accumulate operations per symbol. On a regular $(3,6)$ LDPC-coded link at $(k,M){=}(20,64)$ it recovers $5.1$\,dB of the Euclidean mismatch at BLER${=}10^{-1}$ under natural labeling and $1.1$\,dB under Gray labeling, of which an average-covariance receiver recovers $0.7$\,dB and nothing measurable, respectively, and it makes the tangential perturbation $0.2$ to $1.0$\,dB cheaper than white noise of the same power on the same codebook. The per-tone phase orientation of the curve acts as an orthogonal rotation, so these results hold at every orientation, and the demapper decodes at the level of an exactly oriented receiver up to $0.2$\,rad of orientation error per component. A bit-interleaved coded-modulation achievable rate corroborates the ordering, a Woodbury extension keeps the rank-one structure under per-tone Rician fading, and $6$-bit lookup-table quantization costs no measurable degradation.

cs.IT↗
arXiv2026

Quickest Change Detection with Diffusion-Integrated Scores

Classical CUSUM relies on the log-likelihood ratio of the underlying distributions, which cannot generally be computed from finite pre- and post-change samples alone. We propose diffusion-integrated score CUSUM (DI-SCUSUM), a training-free detector. We add Gaussian noise to the samples to form two smooth density estimates and calculate their Hyvärinen scores exactly, without training a score network. For each incoming observation, we sample a diffusion time, perturb the observation, and use the importance-weighted score difference as an increment in the DI-SCUSUM recursion. Under the assumption that observations follow the fixed empirical distributions, the post-change mean increment is proportional to the Kullback-Leibler (KL) divergence from the smoothed post-change to the smoothed pre-change empirical distribution. We establish exponential false-alarm scaling and a first-order delay bound that, for a fixed threshold and increment scaling, is inversely proportional to the KL divergence. In the calibrated anisotropic Gaussian simulation, DI-SCUSUM nearly matches likelihood-ratio CUSUM and reduces the measured detection delay by about 91% relative to score-based CUSUM. On MNIST and Oxford-IIIT Pet, DI-SCUSUM also has lower empirical conditional detection delay than SCUSUM at comparable false-alarm levels.

stat.ML↗
arXiv2026

NOEMA: Executable Contracts for Learned Wireless Comparisons

Learning-based wireless research increasingly combines simulation, external model training, and benchmarking. Rebuilding these stages for every experiment is time-consuming, while differences introduced between them can silently change the conditions of a comparison. We present NOEMA, an open-source toolkit that prepares model development and baseline evaluation from a shared, machine-checkable wireless scenario. From the same scenario, NOEMA can prepare benchmark execution, capture aligned training data, and export differentiable components for model development. Researchers can therefore focus on training and selecting their models, then return the selected component to the same controlled experiment for evaluation. NOEMA checks the returned model and verifies that declared comparison conditions, including available information, random conditions, and failure accounting, remain consistent through benchmarking and reporting. An included agentic-control example applies these contracts to a language-model supervisor that selects allocation policies and power budgets from historical link feedback. All 43 regression tests of the workflow checks produced their expected outcomes, including rejection of all 31 cases containing known inconsistencies. Separate fidelity tests showed that captured data and returned-model behavior were preserved. In a 20-replicate quadrature phase-shift keying phase-tracking study, the mean bit-error-rate difference between the learned receiver and five- pilot smoothing was -0.01526 (95% confidence interval: -0.01547 to -0.01506). NOEMA therefore provides both a reusable path for developing learned wireless components and a way to keep their final comparisons inspectable and checkable.

eess.SP↗
arXiv2026

Anchor-ECC: Local Integrity Checking for Watermarked LLM Outputs via Error-Correcting Codes

LLM watermarking has become an effective approach to distinguishing AI-generated text from human-written text by embedding detectable patterns during generation. However, a small post-generation edit may change the meaning of the text without removing its overall watermark signal, creating a risk that the modified content is still attributed to the original model. We propose Anchor-ECC, which incorporates the error-correcting code (ECC) constraints and explicit boundary anchors into the watermark structure and pairs them with a dynamic-programming decoder to detect and localize post-generation edits. Across Qwen3-8B, Mistral-7B-Instruct-v0.3, and OPT-125M, the approximate-hard setting achieves about 99.7% block-level true positive rate (TPR) with at most 7.6% false alarm rate (FAR) for edit detection under mixed insertions, deletions, and substitutions, while preserving the distinction between watermarked outputs and unwatermarked text. Additional quality experiments identify lower-perplexity configurations that retain strong edit-detection performance. Together, these results extend LLM watermarking from source identification to local integrity verification while supporting configurable trade-offs between detection reliability and generation quality.

cs.CR↗
arXiv2026

Block Erasure Channel and Block z-Channel with Bounded Decoders and Finite Blocklength

Block error rate is a standard metric in finite blocklength (FBL) communication, yet it conflates two qualitatively different failure modes: block confusions, where the decoder selects a wrong codeword, and block erasures, where it declares a loss. Higher-layer protocols treat physical-layer failures as erasures, but this cross-layer assumption lacks FBL justification. We derive a rigorous upper bound and a companion lower-side estimate on the block confusion probability (BLCP) and block erasure probability (BLEP) for bounded-distance decoders over additive white Gaussian noise (AWGN) channels at finite blocklength, recasting the coding problem as a geometric sphere packing one. We analyze the sensitivity of these bounds to blocklength and signalto-noise ratio, characterize the envelope of the upper bound, and derive closed-form Chernoff approximations. Extending the model to idle transmission blocks, we bound the false alarm probability (FAP) and show that a block z-channel abstraction emerges from the bounded-decoding geometry. Numerical results confirm that confusion and false alarm probabilities lie far below the error rate constraint, providing quantitative physicallayer support for the block erasure channel and block z-channel abstractions assumed in protocol design.

cs.IT↗
arXiv2026

How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?

To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a promising approach by combining the advantages of federated and split learning. However, despite its advantages, existing SFL studies have largely overlooked the strategic interactions among self-interested participants during the SFL process. In this framework, the SFL model owner can choose the cut layer to balance the training load between the server and clients, ensuring the necessary level of privacy for the clients. Additionally, the SFL model owner sets incentives to encourage client participation in the SFL process. The optimization strategies employed by the SFL model owner influence clients' decisions regarding the amount of data they contribute, taking into account the shared incentives over clients and anticipated energy consumption from both computation and networking during SFL. To address this framework, we model the problem using a hierarchical decision-making approach, formulated as a single-leader multi-follower Stackelberg game. We demonstrate the existence and uniqueness of the Nash equilibrium among clients and analyze the Stackelberg equilibrium by examining the leader's game. Furthermore, we discuss privacy concerns related to differential privacy and the criteria for selecting the minimum required cut layer. Our findings show that the Stackelberg equilibrium solution maximizes the utility for both the clients and the SFL model owner while achieving a well-balanced trade-off between model accuracy and the associated computing and networking overhead during the SFL process.

cs.GT↗
arXiv2026

The Deterministic Broadcast Channel Revisited: A Bipartite Graph Approach

The general broadcast channel (BC) has attracted considerable attention because it is a central problem in network or multi-user information theory. The deterministic BC (det-BC), in which both output symbols are functions of the input symbol, plays a vital role in understanding general BCs. In the 1970s, Marton and Pinsker investigated the det-BC independently using a random coding approach and hence quantified its capacity region as functions of the entropy of output symbols. In this paper, we present a purely deterministic and combinatorial treatment of the deterministic BC, which is in contrast to Marton and Pinsker's probabilistic treatment. In particular, we show that each det-BC can be uniquely and completely characterized by a bipartite graph. Based on the bipartite graph model, we present the explicit capacity region of the general det-BC and a deterministic capacity-achieving coding scheme. Specifically, each point on the capacity region boundary is expressed as an explicit function of the degree sequences of the two parts, with boundary coordinates parametrized by the tangent slope. As a result, a closed-form capacity region, which was previously unknown except for special cases such as the Blackwell channel, can be analytically determined, without optimizing the probability distribution of input symbols numerically. For a bipartite graph consisting of multiple isolated subgraphs, there can exist a commonly decodable message while preserving the maximum achievable sum rate, and its achievable rate is explicitly characterized. Finally, the bipartite-graph formulation allows us to construct optimal finite-blocklength coding for the det-BC through binary linear programming.

cs.IT↗
arXiv2026

Adversarial Water-Filling: Theory, Algorithms, and a Domain-Specific Wireless Foundation Model

Competitive resource allocation problems over frequency and space can be formulated as minimax interaction between transmit power and worst-case interference. This formulation naturally arises in multi-operator low Earth orbit (LEO) satellite spectrum sharing, where transmissions from competing constellations interfere in real-time. Under Gaussian channels, the corresponding power-allocation problem admits a convex-concave formulation with a unique saddle point. Discrete constellations yield generally nonconvex mercury/water-filling formulations. In this paper we propose the adversarial water-filling (AWF) problem with corresponding theory and algorithms for these settings. In addition, we develop a domain-specific wireless foundation model for AWF to learn the AWF search dynamics. The architecture incorporates permutation-invariant channel representations, a constraint-aware graph neural network (GNN) with sparse message passing, and global latent variables capturing the low-dimensional water level implied by the AWF optimality. Through learned projected extragradient iterations, the model approximates stationary solutions of the constrained minimax problem arising under mercury/water-filling. We further establish projected-stationarity/Karush-Kuhn-Tucker consistency and conditional local convergence of the learned AWF dynamics under local regularity and stability conditions. Experiments demonstrate empirical generalization across unseen problem sizes, constraint structures, discrete constellations, and channel-weighted objectives, while achieving a median speedup exceeding one order of magnitude over Mirror-Prox on matched instances at comparable first-order solution quality. The related code can be found at https://github.com/convexsoft/AWF.

cs.IT↗
arXiv2026

MAP-Based Task-Oriented Precoding for Multiuser Communication

We propose a task-oriented multiuser wireless communication framework for distributed classification based on a MAP-driven system design under wireless channel impairments. \textcolor{black}{By deriving tractable approximations of the MAP error bound}, the proposed approach enables the design of learning-based feature extraction and precoding strategies. Unlike existing approaches that optimize intermediate reconstruction, information-theoretic, or feature-separability objectives, the proposed formulation directly optimizes objectives derived from the MAP decision error, thereby providing a more direct connection to the final classification performance. Simulation results demonstrate that the proposed schemes achieve higher classification accuracy than their counterparts, with lower or comparable computational complexity.

cs.IT↗
arXiv2026

Cooperative Multi-Satellite ISAC Networks: Centralized vs. Distributed Sensing

This paper investigates a downlink multi-satellite integrated sensing and communication (ISAC) network, in which multiple satellites simultaneously transmit ISAC signals to provide communication services to ground user equipments and enable cooperative sensing of airborne targets through multiple gateways. To support this dual functionality, we introduce communication and sensing beamforming designs based on uniform planar arrays with optimized power allocation. Building on these designs, we propose two cooperative sensing frameworks, namely centralized and distributed. In the centralized framework, each gateway forwards its sensing observations to a central unit (CU), where the positions of multiple targets are jointly estimated from the aggregated data. To mitigate the signaling overhead inherent in centralized processing, a distributed framework is further proposed, in which each gateway independently estimates target positions and transmits only the local estimates to the CU. To associate estimates from different gateways, a data association problem based on the squared Euclidean distance is formulated and efficiently solved using the Hungarian algorithm. The final target positions are then obtained by minimizing the distance error. Simulation results demonstrate that the proposed centralized and distributed frameworks significantly outperform existing sensing schemes while satisfying communication performance requirements. Finally, we evaluate the sensing-communication trade-off from the viewpoints of sensing accuracy and communication power consumption under the proposed frameworks.

eess.SP↗
arXiv2026

Secret-key-based physical layer security for feedback-aided unsourced random access

This work introduces security for unsourced random access (URA) via a physical-layer security approach. To achieve confidentiality, the proposed system opportunistically exploits intrinsic features of feedback-aided URA without altering its original structure or operational characteristics. As a result, the system preserves URA's efficiency, including low delay and minimal signaling overhead, while ensuring secure communication. To secure transmission, each user generates a secret key from a feedback signal broadcast by the BS in a previous transmission round, which depends on the BS-user channel and can thus be treated as private. Each user then encrypts its data using the secret key before transmission. Along with the encrypted data, only the parity bits of the LDPC-encoded key are transmitted, enabling secret key recovery at the legitimate receiver via Slepian-Wolf decoding with side information. We propose a receiver algorithm to recover both the encrypted data and the encoded secret key at the legitimate receiver. We further present a theoretical analysis to derive analytical error probabilities for both the legitimate receiver and the passive eavesdropper, as well as to quantify the additional load imposed by the security measures on the URA system. It is shown, based on both theoretical analysis and simulation results, that meaningful secrecy is achieved with only negligible extra overhead compared to the standard URA system.

cs.IT↗
arXiv2026

When Phase Doesn't Matter: Self-Coherent Over-the-Air Computation at Sub-THz

Over-the-air computation (OAC) enables efficient function aggregation in wireless networks by exploiting the superposition property of the multiple-access channel. However, practical deployment of OAC is severely challenged by the reliance on accurate carrier synchronization and coherent reception, which are costly and fragile, especially in short-range and low-complexity systems. In this work, we propose a \emph{self-coherent, synthesizer-free over-the-air computation framework} based on \emph{Kramers--Kronig (KK) reception}. By transmitting a biased aggregate waveform and employing direct detection followed by KK phase reconstruction at the receiver, the proposed scheme eliminates the need for explicit carrier recovery while preserving coherent-like signal aggregation. We develop a signal-domain system model for multi-user OAC under KK reception and provide a synchronization-relaxation analysis demonstrating that the proposed architecture fundamentally removes carrier-frequency offset (CFO) sensitivity between transmitters and receiver. By shifting synchronization complexity away from strict carrier-phase tracking and eliminating distributed phase alignment requirements, the framework reduces control overhead and improves scalability in multi-user aggregation. A detailed per-symbol mean-squared error (MSE) characterization isolates the impact of channel mismatch and KK reconstruction noise, showing that the proposed self-coherent architecture approaches the theoretical performance limits of baseband OAC under practical operating conditions. Finally, we demonstrate that the approach is particularly well suited for mmWave and sub-THz systems, where oscillator phase instability otherwise represents a fundamental bottleneck to scalable coherent OAC.

cs.IT↗
arXiv2026

On the Gaussian-Quadratic Rate-Distortion Function for Vector Sources with Individual Distortion Constraints

This paper investigates the Gaussian-quadratic lossy compression with arbitrary source length under individual distortion constraints. The rate-distortion function (RDF) is lower-bounded by a Hadamard inequality-based rate, which is tight if and only if the semidefinite condition (SDC) holds. Otherwise, this bound becomes loose, and analytical results are lacking. Moreover, the fundamental quantitative relationship between source correlations and the RDF remains incomplete. In this paper, we provide new theoretical results under different source covariance matrices and distortion constraints. First, under arbitrary covariance and distortion constraints, we obtain the spectral properties of the optimal source reconstruction achieving the RDF, and a stronger scalar inequality version of the SDC. We propose a class of source covariance matrices based on hierarchical correlations and show that studying the two-type correlation (2-TC) model is sufficient to establish the analytical foundation for the broader class. Under this covariance, we obtain the RDF with source correlations explicitly incorporated when the SDC holds, and analyze the SDC from the perspectives of distortion constraints and source correlations. Next, under the 2-TC covariance and two-type distortion (2-TD) constraints, we establish the complete RDFs over seven regions on a distortion plane, with the optimal distortion (rate) allocations determined in each region. It is revealed that the essence of pursuing the complete RDF lies in thoroughly analyzing the correlations between the optimal distortions. Finally, under isotropic correlation and identical constraints, we provide the per-component compression rate and show that exploiting correlations can significantly reduce compression costs.

cs.IT↗
arXiv2026

Rainbow Beamforming for Wideband LEO Satellite Communications: Principles, Applications, and Technical Challenges

Low Earth Orbit (LEO) satellite communications (SATCOM) has emerged as a key enabler of global connectivity for 6G networks. To overcome the significant path loss of space-to-ground links, high-gain directional beamforming (BF) is indispensable. As LEO systems evolve toward wider bandwidths to support data-intensive applications, however, they encounter a fundamental physical limitation known as the beam-squint effect, which induces frequency-dependent beam misalignment. Conventionally, the beam-squint effect has been treated as a critical performance impairment that must be mitigated. This article introduces a paradigm shift in wideband LEO satellite systems by redefining beam-squint as a valuable source of frequency-spatial diversity and presents the principles of rainbow BF. Rather than mitigating beam squint, rainbow BF deliberately exploits it to generate frequency-dependent beams, enabling different frequency components to illuminate distinct spatial directions using only a single or a small number of radio frequency chains. By supporting dynamic frequency-spatial beam allocation, rainbow BF offers enhanced flexibility and scalability for wideband LEO SATCOM. We further illustrate the benefits of rainbow BF through three representative LEO SATCOM applications: i) massive multiple access to overcome the latency and throughput bottlenecks of conventional beam hopping; ii) integrated sensing and communications for simultaneous target detection and data transmission; and iii) rapid satellite acquisition to reduce search overhead and improve link reliability. Finally, we discuss key implementation challenges and outline promising future research directions for rainbow BF in wideband LEO SATCOM.

cs.IT↗