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

Stack Overflow Blog
Stack Overflow Blog
量子位
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
美团技术团队
小众软件
小众软件
aimingoo的专栏
aimingoo的专栏
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
Microsoft Security Blog
Microsoft Security Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
酷 壳 – CoolShell
酷 壳 – CoolShell
J
Java Code Geeks
V
V2EX
大猫的无限游戏
大猫的无限游戏
D
DataBreaches.Net
博客园 - Franky
爱范儿
爱范儿
T
Tailwind CSS Blog
A
About on SuperTechFans
Google DeepMind News
Google DeepMind News
博客园_首页
B
Blog RSS Feed
博客园 - 司徒正美
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

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
Low-Complexity Monitoring and Compensation of Transceiver...
Yukun Zhang, Xiaoxue Gong, Xu Zhang, Lei Guo · 2025-12-15 · via cs.IT updates on arXiv.org

In this paper, a low-complexity multi-dimensional architecture for IQ imbalance compensation is proposed, which reduces the effects of in-phase (I) and quadrature (Q) imbalance. The architecture use a transceiver IQ skew estimation structure to compensate for IQ skew, and then use a low-complexity MIMO equalizer to compensate for IQ amplitude/phase imbalance. In the transceiver IQ skew estimation structure, the receiver(RX) IQ skew is estimated by Gardner's phase detector, and the transmitter TX skew is estimated by finding the value that yields the lowest equalizer error. The low-complexity MIMO equalizer consists of a complex-valued MIMO (CV-MIMO) and a two-layer multimodulus algorithm real-valued MIMO (TMMA-RV-MIMO), which employ a butterfly and a non-butterfly structure, respectively. The CV-MIMO is used to perform polarization demultiplexing and the TMMA-RV-MIMO equalizes each of the two polarizations. In addition, the TMMA-RV-MIMO can recovery the carrier phase. A 100 km transmission simulation and experiment with 36 Gbaud dual-polarization 16 quadrature amplitude modulation (DP-16QAM) signals showed that, with the TX/RX IQ skew estimation, the estimation error is less than 0.9/0.25 ps. The low-complexity MIMO equalizer can tolerate 0.1 TX IQ amplitude imbalance and 5 degrees at a 0.3 dB Q-factor penalty. The number of real multiplications is reduced by 55% compared with conventional cases in total.