Search Results for author:
Found 9 papers, 1 papers with code
Transformer-based Joint Source Channel Coding for Textual Semantic Communication
The Space-Air-Ground-Sea integrated network calls for more robust and secure transmission techniques against jamming.
Hybrid Knowledge-Data Driven Channel Semantic Acquisition and Beamforming for Cell-Free Massive MIMO
Moreover, based on the acquired channel semantic, we further propose a knowledge-driven deep-unfolding multi-user beamformer, which is capable of achieving good spectral efficiency with robustness to imperfect CSI in outdoor XR scenarios.
LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains?
The rapid development of communication technologies in the past decades has provided immense vertical opportunities for individuals and enterprises.
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces
Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput.
Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search
However, how to learn deep representations that strongly preserve similarities between data pairs and can be accurately quantized via vector quantization remains a challenging task.
Accelerated Distance Computation with Encoding Tree for High Dimensional Data
We propose a novel distance to calculate distance between high dimensional vector pairs, utilizing vector quantization generated encodings.
Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search
In this paper, we propose an improved residual vector quantization (IRVQ) method, our IRVQ learns codebook with a hybrid method of subspace clustering and warm-started k-means on each stage to prevent performance gain from dropping, and uses a multi-path encoding scheme to encode a vector with lower distortion.
HCLAE: High Capacity Locally Aggregating Encodings for Approximate Nearest Neighbor Search
Further, we propose Aggregating-Tree (A-Tree), a non-exhaustive search method using HCLAE to perform efficient ANN-Search.
Learning Better Encoding for Approximate Nearest Neighbor Search with Dictionary Annealing
Jointly used with residual vector quantization, our optimized dictionaries lead to a better approximate nearest neighbor search performance compared to the state-of-the-art methods.
