惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

G
Google Developers Blog
GbyAI
GbyAI
Y
Y Combinator Blog
The GitHub Blog
The GitHub Blog
B
Blog
博客园 - 叶小钗
V
Visual Studio Blog
小众软件
小众软件
阮一峰的网络日志
阮一峰的网络日志
博客园 - 聂微东
S
SegmentFault 最新的问题
Engineering at Meta
Engineering at Meta
博客园 - Franky
V
V2EX
人人都是产品经理
人人都是产品经理
H
Hackread – Cybersecurity News, Data Breaches, AI and More
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
月光博客
月光博客
IT之家
IT之家
T
The Blog of Author Tim Ferriss
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
C
Check Point Blog
N
Netflix TechBlog - Medium
博客园 - 【当耐特】

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
Generalized Pinching-Antenna Systems: A Radio-Stripe-Base...
[Submitted on 18 Jun 2026] · 2026-06-19 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:This paper investigates radio stripes (RSs) as a practical realization of generalized pinching antennas and proposes an RS-based generalized pinching-antenna (RS-GPA) framework. Unlike dielectric-waveguide-based passive pinching antennas that rely on passive coupling from a guided wave into free space, RSs employ active antenna processing units (APUs) deployed along a shared cable for local transmission, reception, and signal processing. This cable-like active architecture offers flexible installation and broad frequency applicability, while allowing selected APUs to act as discrete and controllable radiation or reception points for location-flexible wireless access. Based on the proposed RS-GPA framework, we establish the system and channel models by accounting for the distance-dependent APU-user channels. For downlink transmission, we formulate a circuit-power-aware sparse APU activation and beamforming problem and develop a reweighted group-sparse beamforming algorithm. To reveal the activation principle, we analyze the single-user downlink case and characterize when an additional APU should be activated by balancing transmit-power saving and circuit-power cost. Inspired by this insight, a geometry-guided low-complexity multiuser algorithm is proposed. For uplink transmission, we formulate a joint APU activation and user power control problem and develop a geometry-guided sparse activation design. Numerical results show that the proposed RS-GPA framework substantially reduces the total consumed power compared with benchmark schemes, while the geometry-guided algorithm achieves near-identical consumed-power performance to the group-sparse design with significantly lower runtime.

Submission history

From: Yanqing Xu [view email]
[v1] Thu, 18 Jun 2026 02:26:47 UTC (1,512 KB)