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

Recent Announcements
Recent Announcements
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
MongoDB | Blog
MongoDB | Blog
H
Help Net Security
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
人人都是产品经理
人人都是产品经理
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
The GitHub Blog
The GitHub Blog
V
V2EX
Microsoft Security Blog
Microsoft Security Blog
V
Visual Studio Blog
A
About on SuperTechFans
博客园_首页
L
LangChain Blog
量子位
雷峰网
雷峰网
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Jina AI
Jina AI
月光博客
月光博客
阮一峰的网络日志
阮一峰的网络日志
博客园 - 聂微东
Microsoft Azure Blog
Microsoft Azure Blog
M
MIT News - Artificial intelligence
N
Netflix TechBlog - Medium

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
Link Performance Abstraction for Interference-Aware Commu...
Heunchul Lee, Taeyoon Kim, Wonwoo Park, Jonghan Lim · 2013-10-03 · via cs.IT updates on arXiv.org

Advanced co-channel interference aware signal detection has drawn research attention during the recent development of Long Term Evolution-Advanced (LTE-A) systems and the interference-aware communications (IAC) is currently being studied by 3GPP. This paper investigates link performance abstraction for the IAC systems employing maximum-likelihood detector (MLD). The link performance of MLD can be estimated by combining two performance bounds, namely, linear receiver and genie-aided maximum-likelihood (ML) receiver. It is shown that the conventional static approach based on static parameterization, while working well under moderate and weak interference conditions, fails to generate a well-behaved solution in the strong interference case. Inspired by this observation, we propose a new adaptive approach where the combining parameter is adaptively adjusted according to instantaneous interference-to-signal ratio (ISR). The basic idea is to exploit the probabilistic behavior of the optimal combining ratio over the ISR. The link-level simulation results are provided to verify the prediction accuracy of the proposed link abstraction method. Moreover, we use the proposed link abstraction model as a link-to-system interface mapping in system-level simulations to demonstrate the performance of the IAC receiver in interference-limited LTE systems