相关论文: Enhancing AmBC Systems with Deep Learning for Join…

Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error…

Ambient backscatter communication (AmBC) enables battery-free connectivity by letting passive tags modulate existing RF signals, but reliable detection of multiple tags is challenging due to strong direct link interference, very weak…

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate the requirement of channel estimation and to improve the…

Ambient backscatter communication (AmBC) leverages the existing ambient radio frequency (RF) environment to implement communication with battery-free devices. One critical challenge of AmBC systems is signal recovery because the transmitted…

Recently, deep learning (DL) has been emerging as a promising approach for channel estimation and signal detection in wireless communications. The majority of the existing studies investigating the use of DL techniques in this domain focus…

Deep neural networks (DNNs) have become a popular approach for wireless localization based on channel state information (CSI). A common practice is to use the raw CSI in the input and allow the network to learn relevant channel…

Ambient backscatter communication (AmBC) has emerged as a highly attractive paradigm for energy-efficient communication. Full-duplex multi-tag AmBC systems provide the scalability and efficient spectrum utilization essential for next…

Distant speech recognition is a challenge, particularly due to the corruption of speech signals by reverberation caused by large distances between the speaker and microphone. In order to cope with a wide range of reverberations in…

Integrated sensing and communication (ISAC) and intelligent reflecting surface (IRS) are viewed as promising technologies for future generations of wireless networks. This paper investigates the channel estimation problem in an IRS-assisted…

We consider an ambient backscatter communication (AmBC) system aided by an intelligent reflecting surface (IRS). The optimization of the IRS to assist AmBC is extremely difficult when there is no prior channel knowledge, for which no design…

In this article, we develop an end-to-end wireless communication system using deep neural networks (DNNs), in which DNNs are employed to perform several key functions, including encoding, decoding, modulation, and demodulation. However, an…

Ambient backscatter communication (AmBC) enables ultra-low-power connectivity by allowing passive backscatter devices (BDs) to convey information through reflection of ambient signals. However, the cascaded AmBC channel suffers from severe…

In ambient backscatter communication (AmBC), strong direct interference from the ambient source poses a major challenge to reliable symbol detection. Although previous studies have shown that employing a low-noise amplifier (LNA) in…

This paper proposes to use a deep neural network (DNN)-based symbol detector for mmWave systems such that CSI acquisition can be bypassed. In particular, we consider a sliding bidirectional recurrent neural network (BRNN) architecture that…

Ambient backscatter communication (AmBC) enables a passive backscatter device to transmit information to a reader using ambient RF signals, and has emerged as a promising solution to green Internet-of-Things (IoT). Conventional AmBC…

In this paper, we propose a novel deep learning based approach for joint channel estimation and signal detection in orthogonal frequency division multiplexing (OFDM) systems by exploring the time and frequency correlation of wireless fading…

In this article, deep learning is applied to estimate the uplink channels for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where a portion of antennas are equipped with high-resolution…

This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using WiFi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver…

Multiple-antenna backscatter is emerging as a promising approach to offer high communication performance for the data-intensive applications of ambient backscatter communications (AmBC). Although much has been understood about…

This letter presents a pioneering method that employs deep learning within a probabilistic framework for the joint estimation of both direct and cascaded channels in an ambient backscatter (AmBC) network comprising multiple tags. In…