arXiv subjects

Yulin Shao

Publications and source records attributed to Yulin Shao.

At least 19 recordsLinked to original sources

arXiv2026

6G Uplink Waveform and Rank Selection under Distributed Reception

The development of 6G uplink is expanding waveform and spatial transmission options, while distributed reception makes their relative merits depend on the spatial and frequency structure of the aggregate channel. We study the joint selection of cyclic prefix orthogonal frequency-division multiplexing and discrete Fourier transform spread OFDM at rank 1 and noncoherent rank 2 under common user equipment power constraints and centralized linear minimum mean-square error reception. We derive waveform switching thresholds at both ranks and a rank selection margin, establishing conditions for selecting among the four modes based on available transmit power, DFT despreading loss, and spatial multiplexing gain. We then characterize how port gains and correlations at individual access points (APs) form aggregate spatial separation and influence mode selection. At fixed port gains and available transmit powers, improving spatial separation without reducing it on any subcarrier increases the rank selection margin, while its effect on the preferred waveform within rank 2 depends on the frequency distribution of the improvement. Numerical results show that different AP deployments can favor different waveforms and ranks, even with the same receive antenna budget and comparable aggregate channel gains, including waveform switches at a fixed preferred rank.

cs.IT↗

arXiv2026

Low-PAPR Rank-2 DFT-s-OFDM Waveform for 6G Uplink

Rank-2 discrete Fourier transform-spread orthogonal frequency-division multiplexing (DFT-s-OFDM) is attracting renewed interest for 6G uplink, offering not only spatial multiplexing gains but also an additional degree of freedom for peak-to-average power ratio (PAPR) reduction. We exploit this freedom by selectively exchanging constellation symbols between the two layers before DFT spreading, preserving the transmitted information while reshaping the resulting waveforms. Building on this idea, we propose Cyclic Layer Assignment with Shared-basis Synthesis (CLASS). CLASS employs carefully designed cyclic assignment patterns to generate diverse waveform candidates, while reusing a small number of waveform components to avoid synthesizing each candidate separately. We further establish the optimality of the cyclic design in maximizing waveform diversity under a minimum-basis constraint when equal group sizes are feasible. With only six inverse DFTs, CLASS explores 16 assignments and reduces the 99th-percentile worst-layer PAPR by 1.41 dB relative to fixed assignment, outperforming representative selected mapping (SLM) and partial transmit sequences (PTS) schemes under the same synthesis budget.

eess.SP↗

arXiv2026

Harnessing Inter-Waveguide Evanescent Coupling in Pinching-Antenna Systems

In multi-waveguide pinching-antenna systems (PASS), waveguides are typically modeled as independent signal-transport paths. However, when dielectric waveguides are closely spaced, their evanescent electromagnetic fields overlap, allowing guided signals to exchange energy before radiation. While such inter-waveguide coupling is conventionally regarded as undesirable crosstalk, this paper reveals an opportunity to harness it as an additional analog beamforming resource. The key insight is that coupling, through its interaction with sequential signal extraction by distributed pinching antennas (PAs), can reshape coherent signal combining at the receivers without increasing the available signal power. Consequently, stronger coupling does not necessarily yield better performance; rather, the coupling-induced phase relationships determine whether the radiated signals combine constructively or destructively. To characterize and exploit this mechanism, we establish a physics-based end-to-end channel model, derive coupling-aware waveguide-spacing principles and achievable-rate bounds, and develop joint waveguide deployment, PA placement, and digital precoding algorithms for multi-hotspot communications. Numerical results demonstrate substantial sum-rate improvements over conventional fixed-deployment and optimized minimum-spacing PASS benchmarks. These findings establish inter-waveguide evanescent coupling as a new physical degree of freedom for PASS channel reconfiguration.

eess.SP↗

arXiv2026

Embodied Movable Antennas

This paper introduces embodied movable antennas (MAs), a new MA architecture that rethinks the source of antenna mobility. Unlike conventional MAs that rely on dedicated antenna actuators, embodied MAs harness the mechanical degrees of freedom already present in physical agents to reconfigure antenna position and orientation. We distinguish two modes of embodied MAs: task-coupled and task-preserving. Focusing on the latter, we show how wireless channels can be reconfigured by exploiting task-equivalent host configurations without altering the prescribed physical task. Using a redundant robotic manipulator as a canonical realization, we characterize the intrinsically coupled antenna translation and orientation induced by task-preserving self-motion and derive an exact Fourier representation of the resulting multipath channel. Our analysis reveals that the wireless value of embodied mobility depends not merely on how far an antenna can move, but on which configurations remain feasible under the physical task and how their coupled motion interacts with polarization and multipath propagation. Building on this insight, we develop a cross-task antenna mounting design that enables wireless adaptation with a fixed physical installation. Numerical results demonstrate substantial communication gains through task-preserving motion. These findings establish mechanical redundancy as a new, quantifiable resource for wireless channel reconfiguration.

eess.SP↗

arXiv2026

From Partitioning to Provisioning: Co-Designing DNN Deployment and Infrastructure Capacity

Deep neural network (DNN) partitioning enables distributed inference by dividing model computation across interconnected devices. Existing approaches have largely focused on optimizing DNN deployment over infrastructure with fixed computing and communication capacities. Yet when these resources cannot adequately support inference demands, optimizing deployment alone is insufficient: the supporting infrastructure must also be provisioned. Crucially, deployment and provisioning are inherently coupled. How a DNN is partitioned determines where capacity is needed, while capacity provisioning can fundamentally change the preferred deployment. Motivated by this interdependence, we investigate the co-design of DNN deployment and infrastructure capacity for pipelined inference. Despite the coupling between these decisions, we reveal an efficiently solvable structure: on acyclic networks, the problem admits an exact shortest-path reformulation, enabling globally optimal joint design in polynomial time. Numerical evaluations demonstrate the benefits of co-design over treating deployment and provisioning separately, highlighting the potential of inference-driven infrastructure planning.

cs.NI↗

arXiv2026

Networked Embodied Communication: From Collective Distinguishability to Communication Reliability

Embodied agents need to convey information to surrounding infrastructure, but their active communication interfaces may be unavailable, constrained, or intentionally inactive. Their ability to manipulate physical states offers a complementary path: messages can be encoded in deliberately selected configurations and recovered through infrastructure sensing. This principle underlies embodied communication. Yet physical differences do not guarantee distinguishable messages: a single sensing viewpoint may leave ambiguities that repeated sensing cannot resolve. This paper develops networked embodied communication, where distributed access points (APs) jointly observe message-bearing scatterer positions under fixed illumination. Under a correlated Gaussian sensing model, we characterize the additional distinguishability supplied by receive APs, establish exact redundancy conditions, and reveal how distinctions absent from individual observations can emerge through cross-AP statistical relationships. We then establish the exact asymptotic optimal maximum-error behavior of a finite alphabet under repeated independent sensing. The largest group of indistinguishable messages determines the error floor; once all messages are distinguishable, the minimum pairwise Chernoff information determines the error exponent. For a given alphabet, receiver cooperation can therefore eliminate an error floor that repetition at any individual AP cannot overcome. Building on these results, we derive finite-budget reliability conditions and jointly design the receive AP set and message-bearing positions. Numerical results show that the proposed search closely approaches exact benchmarks on reduced instances with substantially fewer candidate evaluations than exhaustive enumeration, while receiver cooperation reduces the sensing intervals needed to guarantee reliable decoding.

eess.SP↗

arXiv2026

Delay-Doppler Domain Channel Estimation: What if Sparsity is Unknown?

Sparsity in the delay-Doppler (DD) domain enables efficient channel estimation, but the realization-wise sparsity level is rarely known in advance, and it fluctuates. What if we could estimate the channel without ever knowing how many delays or Dopplers are active? This paper answers that question. We propose a sparsity-agnostic structured estimator that requires no prior knowledge of delay or Doppler sparsity budgets. The key idea is to exploit the Cartesian-product structure of DD support (active delays share a common Doppler set) and to select the support dimensions directly from the data via the Bayesian information criterion. We instantiate the framework on an affine frequency division multiplexing system, where the observation model naturally admits an on-grid DD representation. Numerical results demonstrate that it recovers the exact support with high probability and achieves near-oracle channel reconstruction accuracy, consistently outperforming fixed-budget baselines and sparse Bayesian learning. The approach is waveform-agnostic and offers a practical, adaptive solution for DD-domain channel estimation under unknown and time-varying sparsity.

cs.IT↗

arXiv2026

Resolving the Discontinuity of Continuous-Time AFDM Waveforms

Continuous-time affine frequency division multiplexing (AFDM) waveforms, constructed via frequency wrapping and phase correction, are known to be sample-wise equivalent to the widely adopted discrete AFDM framework. In this paper, we uncover a fundamental and previously overlooked flaw in this construction: its complex envelope is inherently discontinuous for generic chirp parameters. We show that these discontinuities are the direct cause of the high out-of-band emission (OOBE). To resolve this issue, we propose a fundamentally different continuous-time waveform, termed stepped frequency division multiplexing (SFDM). Unlike conventional approaches that allow continuous frequency variation, SFDM freezes the instantaneous frequency at the midpoint of the underlying chirp trajectory within each Nyquist sampling interval. This design yields a complex envelope that is strictly continuous over the entire symbol duration while preserving exact sample-wise equivalence with discrete AFDM. A unified spectral analysis reveals that the superior OOBE performance of SFDM stems from the absence of internal jump discontinuities, which otherwise dominate the far-out spectral roll-off. Numerical results confirm that SFDM consistently achieves significantly lower OOBE across a wide range of chirp rates.

eess.SP↗

arXiv2026

AirTF: Over-the-Air Token Fusion for Task-Oriented Multi-Modal Token Communications

In the Internet of Vehicles (IoV), transmitting high-dimensional multi-modal sensory data to edge servers for time-sensitive tasks faces severe spectrum bottlenecks. To address this, we propose a foundation model-driven over-the-air token fusion (AirTF) framework for task-oriented multi-modal token communications. Unlike existing schemes for segmentation that rely on convolutional neural networks (CNNs) with limited local receptive fields, AirTF leverages vision transformer (ViT) encoders to extract globally contextualized semantic tokens from distributed heterogeneous sensors. By concurrently transmitting these spatially aligned tokens over a shared wireless channel, our framework exploits the superposition property of the multiple access channel to inherently fuse complementary multi-modal semantics (e.g., RGB and infrared) directly over the air. This mechanism significantly enhances spectral efficiency compared to orthogonal transmission. Furthermore, the integration of a pre-trained foundation model provides critical visual priors, effectively addressing the data-hungry nature of ViTs on limited, scenario-specific semantic segmentation datasets. Experiments demonstrate that AirTF consistently outperforms orthogonal transmission and CNN-based fusion baselines across AWGN and fading channels. Additional evaluations under a three-user setting, residual synchronization errors, and imperfect channel state information estimation further confirm its robustness. The source code will be made publicly available upon acceptance.

eess.IV↗

arXiv2026

Sensing-Induced Embodied Communication in the Near Field

Integrated sensing and communication is turning the cellular infrastructure into an active observer of the physical world. When such infrastructure interacts with embodied agents capable of deliberately changing their states and surroundings, the physical world itself can become a communication medium. This paper studies the fundamental communication limits of this sensing-induced embodied communication paradigm in the near field. We consider an agent that maps messages to the positions of a controllable scatterer within a bounded three-dimensional (3D) region, while a base station decodes the selected position from multi-snapshot monostatic sensing echoes. Near-field spherical wavefronts resolve both angle and range, expanding the embodied-symbol space from a 2D plane to a 3D volume. This gain, however, comes with a position-dependent and anisotropic reliability geometry. We characterize this geometry through the pairwise Bhattacharyya distance and derive a local ellipsoidal representation of the resulting 3D confusability regions, whose principal axes quantify directional sensing resolution. The ellipsoid further degenerates into the 2D transverse ellipse in the far-field limit, unifying the two regimes. We then formulate the finite-snapshot $ε$-capacity and translate reliable codebook design into a 3D packing problem. A face-centered cubic construction provides an achievable rate, while a geometric converse yields a complementary upper bound. Numerical results validate the proposed geometry and demonstrate the capacity gain of near-field volumetric packing over far-field planar packing. These results establish a unified geometric and information-theoretic framework for communication through deliberately configured physical states.

cs.IT↗

arXiv2026

Alpha-Beta HMM: Interpretable Low-Parameter Hidden Markov Filtering in Dynamic Environments

Practical online inference in dynamic environments requires a lightweight filtering mechanism that remains adaptive to state changes while retaining reliable information from past noisy observations. To address this challenge, we propose the $αβ$-HMM, an interpretable low-parameter hidden Markov filtering framework that replaces the full transition matrix with an equal-exit surrogate governed by an exit-probability parameter $α$, and introduces a step-size parameter $β$ through a generalized measurement update to regulate the influence of observational evidence. A central feature of the proposed method is that it preserves the nonlinear log-belief-ratio dynamics of HMM-type filtering, which turn out to be critical for strong performance. To analyze this nonlinear recursion, we develop a dynamical-systems framework and a deterministic reference system, through which we characterize adaptation capability, learning performance, and practical guidance for selecting the two proposed parameters. In parallel, we study the approximation error induced by the equal-exit surrogate and show that the resulting low-parameter filter remains competitive with the oracle HMM across a broad range of environments. These results reveal an explicit learning-adaptation trade-off induced by the two proposed parameters, provide principled guidance for parameter tuning, and show that strong filtering performance can be achieved within a tractable and interpretable low-parameter framework.

eess.SY↗

arXiv2026

How should covariates be handled in randomized trials? Empirical evidence from 50 trials and recommendations for practice

Background and Objective: Covariate adjustment can improve precision and power in randomized clinical trials and is recommended by major regulatory agencies. However, there is limited empirical evidence on how different adjustment strategies perform across diverse real-world trials, leaving uncertainty about which methods and covariates should be prespecified in statistical analysis plans. We aim to address this gap and provide practical recommendations. Methods: We conducted a large-scale empirical study using individual-level data from 50 publicly available randomized trials (29,094 participants; 574 treatment-outcome comparisons). We compared commonly used covariate-adjusted estimators, including analysis of covariance, inverse-probability weighting, g-computation, and machine-learning-based approaches, combined with three covariate-selection strategies. Performance was evaluated using precision gains, changes in point estimates, computational reliability, and the probability that covariate adjustment altered statistical significance relative to an unadjusted analysis. Results: Covariate adjustment improved precision in most settings, with a median variance reduction of 13.3\% for continuous outcomes and 4.6\% for binary outcomes. Parsimonious regression approaches using a small prespecified set of prognostic covariates performed as well as or better than more complex methods, particularly in small to medium samples. Machine-learning-based estimators did not provide additional precision and were more prone to computational failure for binary outcomes. Conclusions: Across trials, parsimonious covariate adjustment provided consistent efficiency gains without introducing systematic bias. These findings support routine covariate adjustment in primary trial analyses. All curated datasets and analysis code are openly released to support future clinical research.

stat.AP↗

arXiv2026

Stepped Frequency Division Multiplexing: A Jump-Free Continuous-Time AFDM Waveform

Affine frequency division multiplexing (AFDM) has emerged as a promising modulation scheme for doubly selective channels, but its canonical continuous-time realization, referred to herein as piecewise continuous AFDM (PC-AFDM), has been observed to exhibit high out-of-band emission (OOBE) whose mechanism has not been analytically characterized. This paper shows that the underlying cause is frequency wrapping, which introduces internal envelope jumps between AFDM sampling instants and generates a high-frequency spectral tail distinct from ordinary block truncation. To eliminate these discontinuities without altering the inverse discrete affine Fourier transform (IDAFT) output sequence, we propose stepped frequency division multiplexing (SFDM). In SFDM, the instantaneous frequency is kept constant at the midpoint of the wrapped chirp within each sampling interval, while the phase is continuously accumulated across interval boundaries. We prove that, under continuous phase accumulation and without additional phase correction, the midpoint choice is the unique sample-preserving choice for arbitrary chirp-rate parameter. The resulting waveform is continuous within each AFDM block, reduces OOBE, and preserves the standard AFDM modulation matrix, guard-interval structure, and receiver processing. Moreover, under fractional-delay propagation, SFDM mitigates the receiver sensitivity that arises when delayed sampling points fall near wrapping-induced discontinuities in PC-AFDM. Numerical results verify the theoretical tail coefficients, demonstrate OOBE reduction, and show improved receiver robustness in the high-percentile and worst-case regimes. These findings establish SFDM as a spectrally cleaner and more reliable physical layer for AFDM systems.

eess.SP↗

arXiv2026

Embodied Communication: Sensing-Induced Reliability Fields and Capacity Bounds

This paper introduces embodied communication, a new wireless communication modality in which information is imprinted onto environmental states and recovered by the receiver through sensing. No dedicated communication transmitter is activated, and no additional communication spectrum is occupied; instead, the sensed environment itself becomes the carrier of information. The key insight is that sensing must be reinterpreted for communication. Rather than asking how accurately an unknown physical state can be estimated, embodied communication asks how reliably two states can be distinguished. We formalize this idea through a multi-snapshot radio frequency (RF) sensing model and derive a sensing-induced reliability field that quantifies the distinguishability between physical states. This field turns embodied symbol design into a geometric packing problem shaped by the sensing resolution of the infrastructure. For this embodied channel, we characterize the finite-snapshot $ε$-capacity through achievable designs and converses. We develop lattice-based codebooks, obtain a closed-form hexagonal design under a main-lobe approximation, and establish information-theoretic and geometric upper bounds. We further reveal an intrinsic sensing-duration tradeoff: more sensing snapshots improve reliability, but also lengthen each embodied symbol, leading to a finite optimal sensing time. These results expose a latent communication pathway in sensing-enabled infrastructure and show how the environment can be transformed from a passive backdrop into an active information carrier.

cs.IT↗

arXiv2026

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks

Deploying large language models (LLMs) at the network edge is hindered by their enormous cost, yet the reasoning quality they provide remains indispensable. Heterogeneous collaboration between edge small models and a server LLM has emerged as a promising direction, but existing methods fail under the dynamic conditions of multi-user contention, autoregressive generation, and time-varying resources. This paper puts forward a process reward model (PRM)-aided two-stage decoupled acceleration (PRADA) framework, which is built on a fundamental change of perspective: instead of querying a PRM online, which cripples multi-user systems with prohibitive latency, we use the PRM solely as an offline teacher. Its reasoning-quality intuition is fully distilled into a lightweight policy that screen each step locally, without any context upload, while a Lagrangian scheduler at the server resolves resource contention through a threshold-structured policy. Across diverse reasoning benchmarks, PRADA retains the vast majority of the LLM's accuracy while substantially reducing end-to-end latency. The results further reveal threshold effects for both server parallel capacity and total bandwidth: performance saturates beyond critical resource levels, after which the system bottleneck shifts from queuing to computation or from communication to contention. These structural findings provide actionable guidance for joint provisioning of computation and communication resources without requiring per-benchmark tuning.

cs.NI↗

arXiv2026

AI Tool Discovery at Scale: All You Need is DNS

The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of tools, yet existing solutions buckle under O(N) complexity and centralized governance. Instead of building another fragile overlay, we propose ToolDNS, a radical framework that retrofits semantic tool discovery onto the Internet's most resilient substrate: the Domain Name System (DNS). By embedding functional intent and organizational trust into a hierarchical namespace, ToolDNS transforms an expensive semantic search into a series of lightweight, O(log N) name resolutions. We introduce three protocol-compliant enhancements to enable decentralized governance and semantic pruning: partially unfolded names, EDNS0 intent payloads, and logical subdomains. To rigorously evaluate this approach across the fragmented tooling landscape, we construct and release a large-scale heterogeneous benchmark comprising 33,688 real-world tools spanning MCP, A2A, RESTful, and Skill protocols. On this dataset, ToolDNS slashes the per-query search space by 95.26% while matching state-of-the-art retrieval accuracy. Furthermore, its UDP-native design reduces discovery latency by orders of magnitude compared to HTTP-based registries. Our work demonstrates that scalable AI interoperability requires not more middleware, but a smarter utilization of the infrastructure already beneath our feet.

cs.AI↗

arXiv2026

Cayley Graph Optimization for Scalable Multi-Agent Communication Topologies

Large-scale multi-agent communication has long faced a scalability bottleneck: fully connected networks require quadratic complexity, yet existing sparse topologies rely on hand-crafted rules. This paper treats the communication graph itself as a design variable and proposes CayleyTopo, a family of circulant Cayley graphs whose generator sets are optimized to minimize diameter, directly targeting worst-case information propagation speed. To navigate the enormous search space of possible generator sets, we develop a lightweight reinforcement learning framework that injects a number-theoretic prior to favor structurally rich generators, alongside a message-propagation score that provides dense connectivity feedback during construction. The resulting CayleyTopo consistently outperforms existing hand-crafted topologies, achieving faster information dissemination, greater resilience to link failures, and lower communication load, all while approaching the theoretical Moore bound. Our study opens the door to scalable, robust, and efficient communication foundations for future multi-agent systems, where the graph itself becomes optimizable rather than a fixed constraint.

cs.NI↗

arXiv2026

Multi-Mode Pinching-Antenna Systems: Polarization-Aware Full-Wave Modeling and Optimization

Millimeter-wave and terahertz communications face a fundamental challenge: overcoming severe path loss without sacrificing spectral efficiency. Pinching antenna systems (PASS) address this by bringing radiators physically close to users, yet existing frameworks treat the waveguide as a mere transmission line, overlooking its inherent multi-mode capabilities and the critical role of polarization. This paper develops the first polarization-aware, full-wave electromagnetic model for multi-mode PASS (MMPASS), capturing spatial radiation patterns, modal polarization states, and polarization matching efficiency from first principles. Leveraging this physically grounded model, we reveal fundamental trade-offs among waveguide attenuation, atmospheric absorption, and geometric spreading, yielding closed-form solutions for optimal PA placement and orientation in single-user scenarios. Extending to multi-user settings, we propose a modular optimization framework that integrates fractional programming with closed-form polarization updates, scaling gracefully to arbitrary numbers of waveguides, PAs, and users. Numerical results show that MMPASS achieves up to a 167% increase in spectral efficiency compared with single-mode PASS. Moreover, when comparing MMPASS with its polarization-ignorant counterpart, polarization awareness alone improves the sum rate by up to 23%. By bridging rigorous electromagnetic theory with scalable optimization, MMPASS establishes a physically complete and practically viable foundation for future high-frequency wireless networks.

cs.IT↗