Deep Mismatch Channel Estimation in IRS based 6G Communication

We propose a channel estimation protocol to determine the uplink channel state information (CSI) at the base station for an intelligent reflecting surface (IRS) based wireless communication. More specifically, we develop a channel estimation scheme in a multi-user system with high estimation accuracy and low computational complexity. One of the state-of-the-art approaches to channel estimation is the deep learning-based approach. However, the data-driven model often experiences high computational complexity and, thus, is slow to channel estimation. Inspired by the success of utilizing domain knowledge to build effective data-driven models, the proposed scheme uses the high channel correlation property to train a shallow deep learning model. More specifically, utilizing the one coherent channel estimation, the model predicts the subsequent channel coherence CSI. We evaluate the performance of the proposed scheme in terms of normalized mean square error (NMSE) and spectral efficiency (SE) via simulation. The proposed scheme can estimate the CSI with reasonable success of lower NMSE, higher SE, and lower estimation time than existing schemes.

Beyond Traditional Beamforming: Singular Vector Projection Techniques for MU-MIMO Interference Management

This paper introduces low-complexity beamforming algorithms for multi-user multiple-input multiple-output (MU-MIMO) systems to minimize inter-user interference and enhance spectral efficiency (SE). A Singular-Vector Beamspace Search (SVBS) algorithm is initially presented, wherein all the singular vectors are assessed to determine the most effective beamforming scheme. We then establish a mathematical proof demonstrating that the total inter-user interference of a MU-MIMO beamforming system can be efficiently calculated from the mutual projections of orthonormal singular vectors. Capitalizing on this, we present an Interference-optimized Singular Vector Beamforming (IOSVB) algorithm for optimal singular vector selection. For further reducing the computational burden, we propose a Dimensionality-reduced IOSVB (DR-IOSVB) algorithm by integrating the principal component analysis (PCA). The numerical results demonstrate the superiority of the SVBS algorithm over the existing algorithms, with the IOSVB offering near-identical SE and the DR-IOSVB balancing the performance and computational efficiency. This work establishes a new benchmark for high-performance and low-complexity beamforming in MU-MIMO wireless communication systems.

Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning

Seismic intensity prediction in a geographical area from early or initial seismic waves received by a few seismic stations is a critical component of an effective Earthquake Early Warning (EEW) system. State-of-the-art deep learning-based techniques for this task suffer from limited accuracy in the prediction and, more importantly, require input waveforms of a large time window from a handful number of seismic stations, which is not practical for EEW systems. To overcome the above limitations, in this paper, we propose a novel deep learning approach, Seismic Contrastive Graph Neural Network (SC-GNN) for highly accurate seismic intensity prediction using a small portion of initial seismic waveforms received by a few seismic stations. The SC-GNN comprises two key components: (i) a graph neural network (GNN) to propagate spatiotemporal information through the nodes of a graph-like structure of seismic station distribution and wave propagation, and (ii) a self-supervised contrastive learning component to train the model with larger time windows and make predictions using shorter initial waveforms. The efficacy of our proposed model is thoroughly evaluated through experiments on three real-world seismic datasets, showing superior performance over existing state-of-the-art techniques. In particular, the SC-GNN model demonstrates a substantial reduction in mean squared error (MSE) and the lowest standard deviation of the error, indicating its robustness, reliability, and a strong positive relationship between predicted and actual values. More importantly, the model maintains superior performance even with 5s input waveforms, making it particularly efficient for EEW systems.