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

Jina AI
Jina AI
Apple Machine Learning Research
Apple Machine Learning Research
宝玉的分享
宝玉的分享
M
MIT News - Artificial intelligence
S
SegmentFault 最新的问题
博客园 - 叶小钗
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
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - Franky
博客园 - 司徒正美
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理
Hugging Face - Blog
Hugging Face - Blog
V
Visual Studio Blog
阮一峰的网络日志
阮一峰的网络日志
博客园 - 【当耐特】
Google DeepMind News
Google DeepMind News
L
LangChain Blog
Stack Overflow Blog
Stack Overflow Blog
博客园_首页
U
Unit 42
月光博客
月光博客
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC

eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Antenna Selection For Receive Spatial Modulation System E...
Burak Ahmet Ozden, Erdogan Aydin · 2023-11-22 · via eess.SP updates on arXiv.org

Reconfigurable intelligent surface (RIS) enhances signal quality by adjusting the phase of electromagnetic waves in wireless communication. Spatial modulation (SM), a prominent index modulation (IM) technique, provides high spectral efficiency and low energy consumption. In this article, a new wireless communication system is proposed by combining capacity-optimized antenna selection (COAS), antenna correlation antenna selection (ACAS), and Euclidean distance-optimized antenna selection (EDAS)-supported RIS-empowered receive SM (RIS-RSM) system (AS-RIS-RSM) in a single-input multiple-output (SIMO) structure. The proposed AS-RIS-RSM schemes (COAS-RIS-RSM, ACAS-RIS-RSM, and EDAS-RIS-RSM) have superior features such as high spectral efficiency, high energy efficiency, and low error data transmission. Integrating COAS, ACAS, and EDAS techniques into the system enables the selection of the channel with the best conditions, thus increasing the error performance of the proposed system. Also, using RIS increases the error performance of the system by controlling the transmitted signal to a certain extent. The analytical ABER results of the proposed AS-RIS-RSM systems are derived and shown to overlap with simulation results. For the proposed systems, an optimal maximum likelihood (ML) detector and a sub-optimal low-complexity greedy detector (GD) are offered. Also, capacity analyses of the proposed AS-RIS-RSM systems are derived and it is observed that they have higher capacity compared to RIS-QAM/PSK and RIS-RSM systems. Then, computational complexity analyses of the proposed COAS-RIS-RSM, ACAS-RIS-RSM, and EDAS-RIS-RSM systems are presented. The proposed systems have been compared to counterpart wireless communication systems including RIS-RSM, RIS-QAM, and RIS-PSK under equivalent conditions, demonstrating that the proposed systems achieve better error performance.