Results 231 to 240 of about 5,046,998 (306)

Multiple-symbol differential detection of MPSK

IEEE Transactions on Communications, 1990
A differential detection technique for MPSK (multiple-phase shift keying), which uses a multiple-symbol observation interval, is presented, and its performance is analyzed and simulated. The technique makes use of maximum-likelihood sequence estimation of the transmitted phases rather than symbol-by-symbol detection as in the conventional differential ...
D. Divsalar, M.K. Simon
openaire   +3 more sources

Active Detection for Symbol-Level Jamming

IEEE Transactions on Wireless Communications
Recently, symbol level jamming (SLJ) has been proposed and studied as an effective jamming technique. However, the detection performance estimation and efficient detection schemes for SLJ have not been investigated in the literature.
Yuxin Shi, Xinjin Lu, Yusheng Li, Kang An   +3 more
openaire   +2 more sources

Explainability of Neural Networks for Symbol Detection in Molecular Communication Channels

IEEE Transactions on Molecular Biological and Multi-Scale Communications, 2023
Recent molecular communication (MC) research suggests machine learning (ML) models for symbol detection, avoiding the unfeasibility of end-to-end channel models.
Jorge Torres Gómez, Pit Hofmann, Frank H.P. Fitzek, F. Dressler   +3 more
semanticscholar   +1 more source

Symbol Detection of Ambient Backscatter Communications Under IQ Imbalance

IEEE Transactions on Vehicular Technology, 2023
Ambient backscatter communications (AmBC) have been mainly studied under the assumption of perfect inphase/quadrature-phase (IQ) balance, which is hard to achieve in practical AmBC networks.
Yinghui Ye, Jiajia Zhao, Xiaoli Chu, Sumei Sun, G. Lu   +4 more
semanticscholar   +1 more source

Low Complexity Parallel Symbol Detection for OTFS Modulation

IEEE Transactions on Vehicular Technology, 2023
This article proposes a novel low-complexity parallel message passing (PMP) detection scheme for orthogonal time-frequency space (OTFS) modulation technology. The traditional message passing (MP) scheme is to detect message symbols individually. Due to a
Zongming Yuan, Meng Tang, Jianhua Chen, Taibin Fang, Hao Wang, Jinhong Yuan   +5 more
semanticscholar   +1 more source

Robust Symbol Detection Based on Quaternion Neural Networks in Wireless Polarization-Shift-Keying Communications

IEEE Transactions on Neural Networks and Learning Systems, 2023
Quaternion neural networks (QNNs) form a class of neural networks constructed with quaternion numbers. They are suitable for processing 3-D features with fewer trainable free parameters than real-valued neural networks (RVNNs).
Haotian Chen, R. Natsuaki, A. Hirose
semanticscholar   +1 more source

Deep Learning Based Symbol Detection With Natural Redundancy for Non-Uniform Memoryless Sources

IEEE Communications Letters, 2023
In this letter, we consider improving the performance of symbol detection by utilizing the natural redundancy (NR) that widely exists in the transmission sources.
Zhen-Yu Wang, Hong-yi Yu, Zhixiang Shen, Zhaorui Zhu, Caiyao Shen, Jian-ping Du   +5 more
semanticscholar   +1 more source

Tensor-Based Joint Channel Estimation and Symbol Detection for Time-Varying mmWave Massive MIMO Systems

IEEE Transactions on Signal Processing, 2021
In this paper, a tensor-based joint channel parameter estimation and information symbol detection scheme is developed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems.
Jianhe Du, Meng Han, Yuanzhi Chen, Libiao Jin, F. Gao   +4 more
semanticscholar   +1 more source

Joint Channel Estimation and Symbol Detection in MIMO-OFDM Systems: A Deep Learning Approach using Bi-LSTM

International Conference on Communication Systems and Networks, 2022
Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system is a promising technology that provides high capacity and high data rate transmission in 5G and beyond.
Aswathy K. Nair, Vivek Menon
semanticscholar   +1 more source

Real-time Machine Learning for Symbol Detection in MIMO-OFDM Systems

IEEE Conference on Computer Communications, 2022
Recently, there have been renewed interests in applying machine learning (ML) techniques to wireless systems. Nevertheless, ML-based approaches often require a large amount of data in training, and prior ML-based MIMO symbol detectors usually adopt ...
Yibin Liang, Lianjun Li, Y. Yi, Lingjia Liu   +3 more
semanticscholar   +1 more source