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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
Distributed MIMO Precoding with Routing Constraints in Se...
Jale Sadreddini, Omer Haliloglu, Andres Reial · 2024-05-30 · via eess.SP updates on arXiv.org

Distributed Multiple-Input and Multiple-Output (D-MIMO) is envisioned to play a significant role in future wireless communication systems as an effective means to improve coverage and capacity. In this paper, we have studied the impact of a practical two-level data routing scheme on radio performance in a downlink D-MIMO scenario with segmented fronthaul. At the first level, a Distributed Unit (DU) is connected to the Aggregating Radio Units (ARUs) that behave as cluster heads for the selected serving RU groups. At the second level, the selected ARUs connect with the additional serving RUs. At each route discovery level, RUs and/or ARUs share information with each other. The aim of the proposed framework is to efficiently select serving RUs and ARUs so that the practical data routing impact for each User Equipment (UE) connection is minimal. The resulting post-routing Signal-to-Interference plus Noise Ratio (SINR) among all UEs is analyzed after the routing constraints have been applied. The results show that limited fronthaul segment capacity causes connection failures with the serving RUs of individual UEs, especially when long routing path lengths are required. Depending on whether the failures occur at the first or the second routing level, a UE may be dropped or its SINR may be reduced. To minimize the DU-ARU connection failures, the segment capacity of the segments closest to the DU is set as double as the remaining segments. When the number of active co-scheduled UEs is kept low enough, practical segment capacities suffice to achieve a zero UE dropping rate. Besides, the proper choice of maximum path length setting should take into account segment capacity and its utilization due to the relation between the two.