Article
Chemistry, Analytical
Shuaijun Wang, Mingliu Liu, Deshi Li
Summary: Orthogonal frequency division multiplexing (OFDM) is widely used in underwater acoustic (UWA) communication for its anti-multipath performance and high spectral efficiency. Channel state information (CSI) is crucial for UWA-OFDM systems, and a Bayesian learning-based channel estimation architecture has been proposed to accurately estimate the time-varying UWA channel. The algorithms designed in this study improve channel estimation accuracy and decrease the bit error rate (BER).
Article
Telecommunications
Zhengqiang Yan, Xinghai Yang, Lijun Sun, Jingjing Wang
Summary: This paper proposes a fast orthogonal matching pursuit algorithm OIP-FOMP for sparse time-varying underwater acoustic channel estimation, which can reduce computational complexity by about 1/4 compared with the traditional OMP algorithm without loss of accuracy. By precomputing the candidate path signature Hermitian inner product matrix and using efficient QR decomposition, the algorithm successfully avoids problems such as reconstruction failure caused by inaccurate delay selection in the OMP algorithm.
CHINA COMMUNICATIONS
(2021)
Article
Astronomy & Astrophysics
Yunlong Du, Junlong He, Deshuang Zhao
Summary: In this paper, a mathematical model of power combination based on the time reversal (TR) technique in slowly time-varying atmospheric channels is proposed. The relationship between power combination efficiency and the permittivity of the time-varying atmospheric channel is analyzed through theoretical analysis and Monte Carlo simulations. The results show that the power combination efficiency of the TR technique is lower than that of completely reciprocal channels if the atmospheric channel changes slowly.
Article
Computer Science, Information Systems
Kunwar Pritiraj Rajput, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo
Summary: This paper investigates the robust linear decentralized tracking of a time-varying sparse parameter in a multiple-input-multiple-output (MIMO) wireless sensor network (WSN) under channel state information (CSI) uncertainty. A novel sparse Bayesian learning-based Kalman filtering (SBL-KF) framework is developed to track the time-varying sparse parameter, and an optimization problem is formulated to minimize the mean-square error (MSE) in each time slot (TS). The proposed technique requires only a single iteration per TS to obtain the transmit precoder (TPC) matrices for all the sensor nodes (SNs) and the receiver combiner (RC) matrix for the fusion center (FC) in an online fashion.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Acoustics
Weihua Jiang, Feng Tong
Summary: In this paper, a Kalman Filtered Compressed Sensing (KF-CS) channel estimation algorithm for time reversal communication is proposed under the framework of dynamic compressed sensing (DCS). By coupling the KF-CS estimator driven time reversal processor with a single channel decision-feedback equalizer (TR-DCS-DFE) in the receiver structure, effective handling of multipath and time variations in underwater acoustic communication is demonstrated through shallow water experimental results with field data.
Article
Engineering, Electrical & Electronic
Gilad Avrashi, Alon Amar, Israel Cohen
Summary: This study focuses on carrier frequency offset estimation in OFDM underwater acoustic communication, proposing a simple estimator by transmitting equi-power and equi-spaced pilot tones. By designing the phases of pilot tones, the peak to average power can be kept low while maintaining a sufficient pilot to data ratio. Modifications are made for time-varying underwater acoustic channels, showing superior performance compared to state-of-the-art techniques.
Article
Engineering, Electrical & Electronic
Alihan Kaplan, Volker Pohl, Dae Gwan Lee
Summary: Many traditional data transmission schemes rely on the availability of channel state information, but for time-varying channels it is difficult to determine the accuracy of previous channel estimates. This paper proposes two data transmission schemes that perform both data transmission and channel estimation simultaneously, considering both single antenna and antenna array receivers, and relying on sparsity assumptions.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Weihua Jiang, Feng Tong, Zhengliang Zhu
Summary: The paper proposes a sequential adaptive observation length orthogonal matching pursuit (SAOLOMP) approach to explore rapidly time-varying sparsity in underwater acoustic (UWA) channels. The method separates static and dynamic components to adapt to different time variations and achieves better performance than existing methods.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Chemistry, Analytical
Yi Cen, Mingliu Liu, Deshi Li, Kaitao Meng, Huihui Xu
Summary: The paper proposes a method to control the energy efficiency of data transmission in underwater acoustic sensor networks by adjusting modulation methods, coding rates, and transmission power based on the dynamic nature of the communication channel. They introduce a double-scale adaptive transmission mechanism that predicts channel states and develops an energy-efficient transmission algorithm to optimize modulation and coding in the long term. A quantitative model is constructed to analyze the relationship between data transmission and buffer thresholds under different channel states or data arrival rates.
Article
Engineering, Electrical & Electronic
Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love, James V. Krogmeier
Summary: 6G operators may use mmWave and sub-THz bands to meet wireless access demand, but sub-THz communication faces new challenges due to wider bandwidths and harsher propagation conditions. This paper proposes a compressed training framework for estimating time-varying sub-THz MIMO-OFDM channels.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Erfan Khordad, Iain B. Collings, Stephen V. Hanly, Giuseppe Caire
Summary: This paper investigates the implementation of compressive sensing (CS) approaches for beam alignment (BA) in multiuser millimeter wave (mmWave) MIMO systems. The study focuses on wideband time-varying channels in the low SNR regime and compares different time scales for beam-switching and running the CS algorithm. An overarching trial-based protocol and a new deterministic construction for designing the CS sensing matrix (SM) are proposed. The results demonstrate that the proposed SM and trial-based protocol outperform other approaches, while running the CS algorithm every block (CS-EB) has higher complexity and overhead than CS-EE.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Maciej Niedzwiecki, Artur Gancza, Lu Shen, Yuriy Zakharov
Summary: This paper investigates the identification problem of sparse linear systems with a mix of static and time-varying parameters. Such systems are common in underwater acoustics, particularly in applications that require identifying the acoustic channel for underwater communication, navigation, and continuous-wave sonar. The paper improves the performance of the fast local basis function (fLBF) algorithm by exploiting properties of the system. Specifically, adaptive time-invariance testing, regularization techniques, and debiasing methods are proposed to enhance the algorithm's performance.
Article
Computer Science, Information Systems
Guangyao Han, Sining Wang, Shuai Chang, Xiaomei Fu
Summary: This article proposes a novel sparse NOFDM scheme based on TR-CNN for underwater communication. The scheme reduces distortion caused by multipath propagation and Doppler spread by using nonorthogonal subcarriers and improves the adaptability of the underwater acoustic channel using TR-CNN. Experimental results demonstrate that the proposed scheme outperforms the traditional scheme in signal detection and recovery, and exhibits robustness in different underwater acoustic channel environments.
IEEE SYSTEMS JOURNAL
(2023)
Article
Computer Science, Information Systems
Jing Xu, Chuandong Li, Xing He, Hongsong Wen, Xiaoyu Zhang
Summary: In this paper, a novel neurodynamic network is proposed to solve the l(1)-minimization problem. The time-varying fixed-time converging neurodynamic network (TFxNN) is designed by introducing time-varying coefficients in the framework of the fixed-time converging neurodynamic network (FxNN). It is proven that the proposed TFxNN is fixed-time stable via Lyapunov stability conditions. Furthermore, it is shown that the proposed TFxNN quickly converges to the unique equilibrium solution from any initial points. An important feature of the proposed TFxNN is its flexibility to choose time-varying coefficients to accelerate convergence. Simulation results based on signal and image reconstruction validate the feasibility and effectiveness of the proposed neurodynamic network.
INFORMATION SCIENCES
(2024)
Article
Engineering, Electrical & Electronic
Irina Merin Baby, Kumar Appaiah, Ribhu Chopra
Summary: The use of massive MIMO technology improves the efficiency and capacity of wireless communication systems. In frequency division duplexed systems, reducing the need for training and feedback can lead to higher data rates.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2022)
