相关论文: Binary Neural Network Aided CSI Feedback in Massiv…
With the ever-increasing demand for high-speed wireless data transmission, beamforming techniques have been proven to be crucial in improving the data rate and the signal-to-noise ratio (SNR) at the receiver. However, they require feedback…
The AI-enabled autoencoder has demonstrated great potential in channel state information (CSI) feedback in frequency division duplex (FDD) multiple input multiple output (MIMO) systems. However, this method completely changes the existing…
Accurate channel state information (CSI) is critical for realizing the full potential of multiple-antenna wireless communication systems. While deep learning (DL)-based CSI feedback methods have shown promise in reducing feedback overhead,…
Massive MIMO systems can enhance spectral and energy efficiency, but they require accurate channel state information (CSI), which becomes costly as the number of antennas increases. While machine learning (ML) autoencoders show promise for…
Channel state information (CSI) needs to be estimated for reliable and efficient communication, however, location information is hidden inside and can be further exploited. This article presents a detailed description of a Massive…
In this paper, a deep neural network with interpretable motion compensation called CS-MCNet is proposed to realize high-quality and real-time decoding of video compressive sensing. Firstly, explicit multi-hypothesis motion compensation is…
Network binarization is a promising hardware-aware direction for creating efficient deep models. Despite its memory and computational advantages, reducing the accuracy gap between binary models and their real-valued counterparts remains an…
Deep learning (DL)-based channel state information (CSI) feedback has received significant research attention in recent years. However, previous research has overlooked the potential privacy disclosure problem caused by the transmission of…
Deep learning (DL)-based channel state information (CSI) feedback methods compressed the CSI matrix by exploiting its delay and angle features straightforwardly, while the measure in terms of information contained in the CSI matrix has…
In this paper, we consider an extremely large-scale massive multiple-input-multiple-output (XL-MIMO) system. As the scale of antenna arrays increases, the range of near-field communications also expands. In this case, the signals no longer…
Accurate channel state information (CSI) is essential for downlink precoding in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems with orthogonal frequency-division multiplexing (OFDM). However,…
The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even…
Accurate estimation of DL CSI is required to achieve high spectrum and energy efficiency in massive MIMO systems. Previous works have developed learning-based CSI feedback framework within FDD systems for efficient CSI encoding and recovery…
In large scale dynamic wireless networks, the amount of overhead caused by channel estimation (CE) is becoming one of the main performance bottlenecks. This is due to the large number users whose channels should be estimated, the user…
A conventional brain-computer interface (BCI) requires a complete data gathering, training, and calibration phase for each user before it can be used. In recent years, a number of subject-independent (SI) BCIs have been developed. Many of…
In a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, the acquisition of downlink channel state information (CSI) at base station (BS) is a very challenging task due to the overwhelming overheads…
State-of-the-art schemes for performance analysis and optimization of multiple-input multiple-output systems generally experience degradation or even become invalid in dynamic complex scenarios with unknown interference and channel state…
Symbol detection for Massive Multiple-Input Multiple-Output (MIMO) is a challenging problem for which traditional algorithms are either impractical or suffer from performance limitations. Several recently proposed learning-based approaches…
Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications. However, current BCNNs are not able to…
The rapidly decreasing computation and memory cost has recently driven the success of many applications in the field of deep learning. Practical applications of deep learning in resource-limited hardware, such as embedded devices and smart…