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

Y
Y Combinator Blog
B
Blog
S
SegmentFault 最新的问题
Vercel News
Vercel News
博客园 - 聂微东
宝玉的分享
宝玉的分享
C
Check Point Blog
有赞技术团队
有赞技术团队
IT之家
IT之家
V
V2EX
爱范儿
爱范儿
GbyAI
GbyAI
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Azure Blog
Microsoft Azure Blog
P
Proofpoint News Feed
博客园 - 司徒正美
博客园_首页
Last Week in AI
Last Week in AI
博客园 - 叶小钗
量子位
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
F
Fortinet All Blogs
腾讯CDC
J
Java Code Geeks

cs.IT updates on arXiv.org

Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
STAR-RIS Assisted SWIPT Systems: Active or Passive?
Guangyu Zhu, Xidong Mu, Li Guo, Ao Huang, Shibiao Xu · 2024-12-23 · via cs.IT updates on arXiv.org

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is investigated. Both active and passive STAR-RISs are considered. Passive STAR-RISs can be cost-efficiently fabricated to large aperture sizes with significant near-field regions, but the design flexibility is limited by the coupled phase-shifts. Active STAR-RISs can further amplify signals and have independent phase-shifts, but their aperture sizes are relatively small due to the high cost. To characterize and compare their performance, a power consumption minimization problem is formulated by jointly designing the beamforming at the access point (AP) and the STAR-RIS, subject to both the power and information quality-of-service requirements. To solve the resulting highly-coupled non-convex problem, the original problem is first decomposed into simpler subproblems and then an alternating optimization framework is proposed. For the passive STAR-RIS, the coupled phase-shift constraint is tackled by employing a vector-driven weight penalty method. While for the active STAR-RIS, the independent phase-shift is optimized with AP beamforming via matrix-driven semidefinite programming, and the amplitude matrix is updated using convex optimization techniques in each iteration. Numerical results show that: 1) given the same aperture sizes, the active STAR-RIS exhibits superior performance over the passive one when the aperture size is small, but the performance gap decreases with the increase in aperture size; and 2) given identical power budgets, the passive STAR-RIS is generally preferred, whereas the active STAR-RIS typically suffers performance loss for balancing between the hardware power and the amplification power.