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

云风的 BLOG
云风的 BLOG
博客园 - 三生石上(FineUI控件)
WordPress大学
WordPress大学
F
Fortinet All Blogs
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 叶小钗
爱范儿
爱范儿
美团技术团队
H
Hackread – Cybersecurity News, Data Breaches, AI and More
有赞技术团队
有赞技术团队
博客园_首页
T
The Blog of Author Tim Ferriss
T
Tailwind CSS Blog
V
Visual Studio Blog
Jina AI
Jina AI
博客园 - Franky
量子位
MongoDB | Blog
MongoDB | Blog
L
LangChain Blog
Apple Machine Learning Research
Apple Machine Learning Research
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
U
Unit 42
aimingoo的专栏
aimingoo的专栏
M
MIT News - Artificial intelligence

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
Achievable Rates and Training Optimization for Uplink Mul...
Songtao Lu, Zhengdao Wang · 2014-10-28 · via cs.IT updates on arXiv.org

We study the performance of uplink transmission in a large-scale (massive) MIMO system, where all the transmitters have single antennas and the base station has a large number of antennas. Specifically, we first derive the rates that are possible through minimum mean-squared error (MMSE) channel estimation and three linear receivers: maximum ratio combining (MRC), zero-forcing (ZF), and MMSE. Based on the derived rates, we quantify the amount of energy savings that are possible through increased number of base-station antennas or increased coherence interval. We also analyze achievable total degrees of freedom (DoF) of such a system without assuming channel state information at the receiver, which is shown to be the same as that of a point-to-point MIMO channel. Linear receiver is sufficient to achieve total DoF when the number of users is less than the number of antennas. When the number of users is equal to or larger than the number of antennas, nonlinear processing is necessary to achieve the full degrees of freedom. Finally, the training period and optimal training energy allocation under the average and peak power constraints are optimized jointly to maximize the achievable sum rate when either MRC or ZF receiver is adopted at the receiver.