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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
Delay-aware Multiple Access Design for Intelligent Reflec...
Piao Zeng, Guangji Chen, Qingqing Wu, Deli Qiao, Abbas Jamalipou · 2022-06-19 · via eess.SP updates on arXiv.org

In this paper, we develop a hybrid multiple access (MA) protocol for an intelligent reflecting surface (IRS) aided uplink transmission network by incorporating the IRS-aided time-division MA (I-TDMA) protocol and the IRS-aided non-orthogonal MA (I-NOMA) protocol as special cases. Two typical communication scenarios, namely the transmit power limited case and the transmit energy limited case are considered, where the device's rearranged order, time and power allocation, as well as dynamic IRS beamforming patterns over time are jointly optimized to minimize the sum transmission delay. To shed light on the superiority of the proposed IRS-aided hybrid MA (I-HMA) protocol over conventional protocols, the conditions under which I-HMA outperforms I-TDMA and I-NOMA are revealed by characterizing their corresponding optimal solution. Then, a computationally efficient algorithm is proposed to obtain the high-quality solution to the corresponding optimization problems. Simulation results validate our theoretical findings, demonstrate the superiority of the proposed design, and draw some useful insights. Specifically, it is found that the proposed protocol can significantly reduce the sum transmission delay by combining the additional gain of dynamic IRS beamforming with the high spectral efficiency of NOMA, which thus reveals that integrating IRS into the proposed HMA protocol is an effective solution for delay-aware optimization. Furthermore, it reveals that the proposed design reduces the time consumption not only from the system-centric view, but also from the device-centric view.