Sequential MAP Parametric OFDM Channel Estimation for Joint Sensing and Communication
Uplink sensing is still a relatively unexplored scenario in integrated sensing and communication which can be used to improve positioning and sensing estimates. We introduce a pilot-based maximum likelihood, and a maximum a posteriori parametric channel estimation procedure using an orthogonal frequency division multiplexing (OFDM) waveform in uplink sensing. The algorithm is capable of estimating the multipath components of the channel, such as the angles of arrival, departure, path coefficient, and the delay and Doppler terms. As an advantage, when compared to other existing methods, the proposed procedure presents expressions for exact alternating coordinate updates, which can be further improved to achieve a competitive multipath channel estimation tool.
Code (0)
등록된 구현이 없습니다.
Tasks
Integrated sensing and communicationSimilar Papers 제목 키워드 기반
One-Bit OFDM Receivers via Deep Learning
This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quan…
DecoderDeep LearningQuantizationOFDM-Based Massive Connectivity for LEO Satellite Internet of Things
Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in orthogonal frequ…
Action DetectionActivity DetectionDoppler-Robust Maximum Likelihood Parametric Channel Estimation for Multiuser MIMO-OFDM
The high directionality and intense Doppler effects of millimeter wave (mmWave) and sub-terahertz (subTHz) channels demand accurate localization of the users and a new paradigm of channel estimation. For orthogonal frequ…
Machine Learning-based Methods for Joint {Detection-Channel Estimation} in OFDM Systems
In this work, two machine learning (ML)-based structures for joint detection-channel estimation in OFDM systems are proposed and extensively characterized. Both ML architectures, namely Deep Neural Network (DNN) and Extr…
Low-Complexity Joint CFO and Channel Estimation for RIS-aided OFDM Systems
Accurate channel estimation is essential for achieving the performance gains offered by reconfigurable intelligent surface (RIS)-aided wireless communications. A variety of channel estimation methods have been proposed f…