相关论文: An Efficient Wireless Channel Estimation Model for…
This paper aims to predict radio channel variations over time by deep learning from channel observations without knowledge of the underlying channel dynamics. In next-generation wideband cellular systems, multicarrier transmission for…
This article presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM). OFDM has been widely adopted in wireless broadband communications to combat…
A new wave of wireless services, including virtual reality, autonomous driving and internet of things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive number of connected devices and…
Propagation modeling is a crucial tool for successful wireless deployments and spectrum planning with the demand for high modeling accuracy continuing to grow. Recognizing that detailed knowledge of the physical environment (terrain and…
Integrated sensing and communications (ISAC), radar, and beamforming require real-time, high-resolution estimation algorithms to determine delay-Doppler values of specular paths within the wireless propagation channel. Our contribution is…
The fast motion of Low Earth Orbit (LEO) satellites causes the propagation channel to vary rapidly, and its behavior is strongly shaped by the surrounding environment, especially at low elevation angles where signals are highly susceptible…
The use of terahertz (THz) communications with massive multiple input multiple output (MIMO) systems in 6G can potentially provide high data rates and low latency communications. However, accurate channel estimation in THz frequencies…
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…
Accurate channel estimation is crucial for the improvement of signal processing performance in wireless communications. However, traditional model-based methods frequently experience difficulties in dynamic environments. Similarly,…
Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especially in massive multiple-input multiple-output (MIMO) systems…
In this work, we propose a deep learning (DL)-based approach that integrates a state-of-the-art algorithm with a time-frequency (TF) learning framework to minimize overall latency. Meeting the stringent latency requirements of 6G orthogonal…
The identification of channel scenarios in wireless systems plays a crucial role in channel modeling, radio fingerprint positioning, and transceiver design. Traditional methods to classify channel scenarios are based on typical statistical…
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 generative models offer a powerful alternative to conventional channel estimation by learning the complex prior distribution of wireless channels. Capitalizing on this potential, this paper proposes a novel channel estimation algorithm…
Multiple wireless sensing tasks, e.g., radar detection for driver safety, involve estimating the "channel" or relationship between signal transmitted and received. In this work, we focus on a certain channel model known as the delay-doppler…
How to reduce the pilot overhead required for channel estimation? How to deal with the channel dynamic changes and error propagation in channel prediction? To jointly address these two critical issues in next-generation transceiver design,…
Channel estimation is a critical task in digital communications that greatly impacts end-to-end system performance. In this work, we introduce a novel approach for multiple-input multiple-output (MIMO) channel estimation using score-based…
In this work, the uplink channel estimation problem is considered for a millimeter wave (mmWave) multi-input multi-output (MIMO) system. It is well known that pilot overhead and computation complexity in estimating the channel increases…
Future mobile ad hoc networks will share spectrum between many users. Channels will be assigned on the fly to guarantee signal and interference power requirements for requested links. Channel losses must be re-estimated between many pairs…
In this work, the problem of communication and radar sensing in orthogonal time frequency space (OTFS) with reduced cyclic prefix (RCP) is addressed. A monostatic integrated sensing and communications (ISAC) system is developed and, it is…