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

J
Java Code Geeks
M
MIT News - Artificial intelligence
D
Docker
S
SegmentFault 最新的问题
B
Blog
Apple Machine Learning Research
Apple Machine Learning Research
博客园_首页
博客园 - 【当耐特】
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
The GitHub Blog
The GitHub Blog
Y
Y Combinator Blog
腾讯CDC
阮一峰的网络日志
阮一峰的网络日志
U
Unit 42
C
Check Point Blog
GbyAI
GbyAI
美团技术团队
Recent Announcements
Recent Announcements
F
Fortinet All Blogs
量子位
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
Visual Studio Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
G
Google Developers Blog

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
Energy-efficient Resource Allocation for Mobile Edge Comp...
Xiang Li, Rongfei Fan, Han Hu, Ning Zhang, Xianfu Chen · 2020-04-08 · via cs.IT updates on arXiv.org

In this paper, we study a mobile edge computing (MEC) system with the mobile device aided by multiple relay nodes for offloading data to an edge server. Specifically, the modes of decode-and-forward (DF) with time-division-multiple-access (TDMA) and frequency-division-multiple-access (FDMA), and the mode of amplify-and-forward (AF) are investigated, which are denoted as DF-TDMA, DF-FDMA, and AF, respectively. Our target is to minimize the total energy consumption of the mobile device and multiple relay nodes through optimizing the allocation of computation and communication resources. Optimization problems under the three considered modes are formulated and shown to be non-convex. For DF-TDMA mode, we transform the original non-convex problem to be a convex one and further develop a low computation complexity yet optimal solution. In DF-FDMA mode, with some transformation on the original problem, we prove the mathematical equivalence between the transformed problem in DF-FDMA mode and the problem under DF-TDMA mode. In AF mode, the associated optimization problem is decomposed into two levels, in which monotonic optimization is utilized in upper level and successive convex approximation (SCA) is adopted to find the convergent solution in the lower level. Numerical results prove the effectiveness of our proposed methods under various working modes.