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
Google DeepMind News
Google DeepMind News
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
小众软件
小众软件
Blog — PlanetScale
Blog — PlanetScale
腾讯CDC
A
About on SuperTechFans
Vercel News
Vercel News
I
InfoQ
阮一峰的网络日志
阮一峰的网络日志
月光博客
月光博客
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
S
SegmentFault 最新的问题
V
Visual Studio Blog
T
Tailwind CSS Blog
大猫的无限游戏
大猫的无限游戏
M
MIT News - Artificial intelligence
博客园 - 【当耐特】
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Microsoft Azure Blog
Microsoft Azure Blog
Apple Machine Learning Research
Apple Machine Learning Research
GbyAI
GbyAI
美团技术团队

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
Novel Time Asynchronous NOMA schemes for Downlink Transmi...
Mehdi Ganji, Hamid Jafarkhani · 2018-08-27 · via cs.IT updates on arXiv.org

In this work, we investigate the effect of time asynchrony in non-orthogonal multiple access (NOMA) schemes for downlink transmissions. First, we analyze the benefit of adding intentional timing offsets to the conventional power domain-NOMA (P-NOMA). This method which is called Asynchronous-Power Domain-NOMA (AP-NOMA) introduces artificial symbol-offsets between packets destined for different users. It reduces the mutual interference which results in enlarging the achievable rate-region of the conventional P-NOMA. Then, we propose a precoding scheme which fully exploits the degrees of freedom provided by the time asynchrony. We call this multiple access scheme T-NOMA which provides higher degrees of freedom for users compared to the conventional P-NOMA or even the modified AP-NOMA. T-NOMA adopts a precoding at the base station and a linear preprocessing scheme at the receiving user which decomposes the broadcast channel into parallel channels circumventing the need for Successive Interference Cancellation (SIC). The numerical results show that T-NOMA outperforms AP-NOMA and both outperform the conventional P-NOMA. We also compare the maximum sum-rate and fairness provided by these methods. Moreover, the impact of pulse shape and symbol offset on the performance of AP-NOMA and T-NOMA schemes are investigated.