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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
Performance Analysis of 5G FR2 (mmWave) Downlink 256QAM o...
Kasidis Arunruangsirilert, Pasapong Wongprasert, Jiro Katto · 2025-02-06 · via eess.SP updates on arXiv.org

The 5G New Radio (NR) standard introduces new frequency bands allocated in Frequency Range 2 (FR2) to support enhanced Mobile Broadband (eMBB) in congested environments and enables new use cases such as Ultra-Reliable Low Latency Communication (URLLC). The 3GPP introduced 256QAM support for FR2 frequency bands to further enhance downlink capacity. However, sustaining 256QAM on FR2 in practical environments is challenging due to strong path loss and susceptibility to distortion. While 256QAM can improve theoretical throughput by 33%, compared to 64QAM, and is widely adopted in FR1, its real-world impact when utilized in FR2 is questionable, given the significant path loss and distortions experienced in the FR2 range. Additionally, using higher modulation correlates to higher BLER, increased instability, and retransmission. Moreover, 256QAM also utilizes a different MCS table defining the modulation and code rate at different Channel Quality Indexes (CQI), affecting the UE's link adaptation behavior. This paper investigates the real-world performance of 256QAM utilization on FR2 bands in two countries, across three RAN manufacturers, and in both NSA (EN-DC) and SA (NR-DC) configurations, under various scenarios, including open-air plazas, city centers, footbridges, train station platforms, and stationary environments. The results show that 256QAM provides a reasonable throughput gain when stationary but marginal improvements when there is UE mobility while increasing the probability of NACK responses, increasing BLER, and the number of retransmissions. Finally, MATLAB simulations are run to validate the findings as well as explore the effect of the recently introduced 1024QAM on FR2.