Article
Chemistry, Analytical
Jingqi Wang, Pingping Wang, Ruoyu Zhang, Wen Wu
Summary: This paper proposes a novel intercarrier interference (ICI)-free parameter estimation method for orthogonal frequency division multiplexing (OFDM) radar to address the performance degradation issue in range-velocity estimation in high mobility scenarios. By utilizing the scale discrete Fresnel transform (SDFnT), the OFDM radar signals are converted to the scale Fresnel domain, recovering the orthogonality of subcarriers with the optimal scale factor. The proposed method demonstrates low computational complexity and high feasibility for OFDM radar implementation due to the compatibility of SDFnT and discrete Fourier Transform (DFT). Simulation results show that the proposed SDFnT-based scheme effectively eliminates ICI effect and achieves accurate delay-Doppler estimation for OFDM radar systems in high velocity and low SNR scenarios with consistency and robustness.
Article
Engineering, Electrical & Electronic
Xinwei Du, Tianyu Song, Yan Li, Ming-Wei Wu, Pooi-Yuen Kam
Summary: Coherent orthogonal frequency-division multiplexing (OFDM) is a key technique for wireless and optical communications, with accurate synchronization being the main challenge. A joint maximum likelihood (ML) estimator is proposed for coherent optical OFDM (CO-OFDM) to address this issue, utilizing replica computation and matched-filtering in the frequency domain, as well as a simpler sequential approach.
JOURNAL OF LIGHTWAVE TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Rongxin Zhang, Yiyin Wang, Xiaoli Ma
Summary: This letter studies the channel estimation problem in a cyclic-prefix OCDM system. The carrier frequency offset is compensated using the cyclic prefix, and a channel estimator based on superimposed pilot subchirps is proposed. The research results show that the proposed approach achieves superior performance compared to the state-of-the-art methods.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Tsui-Tsai Lin, Fuh-Hsin Hwang
Summary: This letter studies the design of blind estimation technique for carrier frequency offset (CFO) in a universal filtered multicarrier (UFMC) system. The proposed methods provide performance comparable to non-blind least square-based scheme with lower computational complexity. Simulation results demonstrate the superiority of blind CFO estimation for UFMC.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Suvra Sekhar Das, Vivek Rangamgari, Shashank Tiwari, Subhas Chandra Mondal
Summary: This work introduces a time domain channel estimation method for OTFS to combat the impact of residual synchronization errors in high mobility scenarios. By analyzing the effects of residual synchronization errors, it is shown that OTFS exhibits better sparsity in time domain channel representation. In addition, utilizing low complexity MMSE equalization and SIC receiver can enhance system performance and mitigate against residual synchronization errors.
Article
Telecommunications
Muhammad Shahmeer Omar, Xiaoli Ma
Summary: This paper proposes several frequency-domain pilot multiplexing techniques to achieve independent channel estimation and detection in frequency-selective channels. The analysis shows that these techniques perform well in channel estimation and have higher spectral efficiency.
IEEE COMMUNICATIONS LETTERS
(2022)
Proceedings Paper
Computer Science, Hardware & Architecture
Rahmat Mulyawan, Reza Averly, Infall Syafalni, Nana Sutisna, Trio Adiono
Summary: The paper proposed a dynamic pilot symbol assisted (PSA) channel estimation technique which provides independent estimation of the channel at each of the subcarriers in each individual OFDM symbol. This approach enables high spectral efficiency multi-carrier communication in mobile environments.
2021 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC)
(2021)
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
Telecommunications
Mateus L. de Filomeno, Lucas Giroto de Oliveira, Andrei Camponogara, Axel Diewald, Thomas Zwick, Marcello L. R. de Campos, Moises Ribeiro
Summary: This letter proposes a joint channel estimation and synchronization method for the orthogonal chirp-division multiplexing (OCDM) scheme. By using a Fresnel-domain pilot symbol, the method enables channel estimation and synchronization, reducing overhead and increasing data rates.
IEEE COMMUNICATIONS LETTERS
(2022)
Article
Engineering, Aerospace
Xiaohu Liang, Hehao Niu, Aijun Liu, Zhixiang Gao, Yunyang Zhang
Summary: An algorithm for joint symbol timing offset and Doppler frequency offset estimation is proposed for spectrally efficient frequency division multiplexing system in the downlink of low-earth-orbit satellite communications. The algorithm is derived by maximizing likelihood function and simplifying the estimator to reduce computation complexity. Simulation results demonstrate the algorithm's robustness and superiority over traditional estimation methods for different packing factors of SEFDM.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Kaiyao Wang, Yongjun Liu, Zhiyong Hong, Zhiqiang Zeng
Summary: A training symbol-aided symbol timing synchronization method for ACO-OFDM is proposed, and simulation results show that it outperforms existing methods in terms of synchronization accuracy and system BER penalty.
JOURNAL OF LIGHTWAVE TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Wei Shi, Chunming Zhao, Wei Xu
Summary: In this study, a novel detection method called RSML is proposed to improve the performance of OFDM systems with insufficient cyclic prefix. By jointly detecting nearby subcarriers, RSML achieves significant performance enhancement under various channel models and cyclic prefix lengths. The detection performance of RSML is even better than conventional methods in certain channel conditions with insufficient cyclic prefix.
PHYSICAL COMMUNICATION
(2022)
Article
Telecommunications
Yangfan Xu, Xinwei Du, Changyuan Yu
Summary: This letter proposes two carrier frequency offset (CFO) estimators for coherent optical orthogonal frequency-division multiplexing (CO-OFDM) systems using the likelihood ratio test (LRT). The estimators detect the zero-information in real and imaginary parts of the effective data subcarriers, and reduce complexity by approximating the cost function as a cosine function. The proposed algorithms show accuracy, flexibility and efficiency in simulation results. The LRT-SFE achieves a significant SNR gain of up to 6.5 dB compared to other blind estimators at an MSE of 10-4.
IEEE COMMUNICATIONS LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Wallace Alves Martins, Symeon Chatzinotas, Bjorn Ottersten
Summary: This study addresses the interference issue caused by co-channel users in downlink multi-antenna multicarrier systems with frequency-packed faster-than-Nyquist (FTN) signaling, proposing a symbol-level precoder to mitigate interblock interference. The research shows that spectral efficiency is affected by the frequency packing and FTN acceleration factors.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Telecommunications
Soheil Salari, Francois Chan
Summary: In this article, the problem of joint carrier frequency offset (CFO) and sparse channel estimation in OFDM communication systems is studied using the sparse Bayesian learning (SBL) framework. A novel SBL-based scheme is designed to iteratively estimate the CFO, channel impulse response (CIR), and noise variance jointly, outperforming existing methods with lower computational cost as confirmed by theoretical analysis and simulation results.
IEEE COMMUNICATIONS LETTERS
(2021)
Article
Telecommunications
Farhad Mirkarimi, Chintha Tellambura, Geoffrey Ye Li
Summary: This letter proposes the development of minimum mean-squared error (MMSE) estimators based on deep neural networks for data detection. To overcome the performance degradation caused by linear MMSE approximations, a near-optimal estimator is developed using the Donsker-Varadhan representation of mutual information (MI) and the derivative relationship between MI and MMSE. This near-optimal MMSE estimator can be computed using a deep neural network, which is trained using mini-batches of input and output samples. Several examples are provided to demonstrate the effectiveness of the proposed estimator, and its application in an end-to-end communication system shows promising performance compared to conventional techniques.
IEEE COMMUNICATIONS LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Peiwen Jiang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li
Summary: Video conferencing is a popular mode of meeting, but it consumes considerable communication resources. Conventional video compression reduces resolution under limited bandwidth. Semantic video conferencing (SVC) maintains high resolution by transmitting keypoints to represent motions. However, the study on transmission errors' influence on keypoints is limited. In this paper, an SVC network based on keypoint transmission is established to reduce transmission resources and prevent detailed expression loss.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Bowen Zhang, Houssem Sifaou, Geoffrey Ye Li
Summary: This paper presents a new localization system that utilizes a novel attention-augmented residual convolutional neural network for indoor positioning and a denoising task for tracking. The proposed methods outperform existing approaches in performance and generality improvement.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Telecommunications
Yixuan Huang, Su Hu, Gang Wu
Summary: This paper focuses on the multiuser implementation of fusion of radar and communication (RadCom) in internet-of-vehicles (IoV) scenarios. The traditional time-division multiple access (TDMA) technology degrades the velocity detection performance of orthogonal frequency-division multiplexing (OFDM)-based RadCom systems. A new TDMA approach for OFDM-based RadCom systems is proposed, where multiuser communication and radar detection are completed synchronously. A continuous-wave TDMA OFDM structure is considered, and numerical evaluation shows wireless communication and radar detection performance over the continuous-wave TDMA OFDM-based RadCom approach.
CHINA COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Chonghao Zhao, Gang Wu
Summary: The increasing demand for high data rates and connection densities in the vehicle communication network has driven the research on cellular vehicle-to-everything (C-V2X) communication. However, the use of Wi-Fi and other wireless technologies in the 5.9 GHz band has also grown significantly. Interference from both co-channel and adjacent channel sources can affect C-V2X users' dedicated band on the 5.9 GHz spectrum, particularly in urban scenarios.
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
(2023)
Article
Telecommunications
Ouya Wang, Jiabao Gao, Geoffrey Ye Li
Summary: In recent years, deep learning has been widely used in communications and achieved remarkable performance improvement. Most existing works rely on data-driven deep learning, which requires a large amount of training data and computing resources. This paper introduces few-shot learning to reduce the training data requirement for new environments by leveraging the learning experience from known environments. By embedding an attention network into the deep learning-based communication model, different environments can be learned together, allowing the communication model to perform well in new environments with only a few pilot blocks. Through an example of deep-learning-based channel estimation, this novel design method achieves better performance than the existing data-driven approach for few-shot learning.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2023)
Review
Food Science & Technology
Ying Huang, Donghui Feng, Xu Li, Wang Li, Jiali Ren, Haiyan Zhong
Summary: Food safety incidents pose a threat to human health and life safety. Enhancing the rapid and sensitive detection of food contaminants using emerging porous materials, such as COFs, is an effective method to prevent and control food safety events. COFs, with their highly ordered pore structure and large specific surface area, play versatile roles in food safety analysis and have broad application prospects.
CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION
(2023)
Article
Engineering, Electrical & Electronic
Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li
Summary: This paper introduces a deep learning-based semantic communication system with memory, called Mem-DeepSC, to mimic human-like communication. By using a universal Transformer-based transceiver to extract semantic information and a memory module to process context information, the reliability and efficiency of communication are improved. Experimental results show that Mem-DeepSC outperforms benchmarks in terms of answer accuracy and transmission efficiency.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Junchao Shi, An-An Lu, Wen Zhong, Xiqi Gao, Geoffrey Ye Li
Summary: In this paper, the authors investigate the downlink robust precoding for massive MIMO communications with imperfect CSI. They propose a robust WMMSE precoder that maximizes the ergodic sum rate. The precoding vectors are characterized by low-dimensional parameters learned from available CSI through a neural network. The deep learning design significantly reduces computational complexity while achieving near-optimal performance.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shenglong Zhou, Geoffrey Ye Li
Summary: The researchers have developed an efficient optimization algorithm for federated learning, which is capable of combating the stragglers' effect, is computationally and communication-efficient, and has good convergence under mild conditions. Moreover, it outperforms several state-of-the-art algorithms in terms of numerical performance.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Xianglong Yu, Xiqi Gao, An-An Lu, Jinlin Zhang, Hebing Wu, Geoffrey Ye Li
Summary: This paper investigates the robust precoding for high frequency skywave massive MIMO communications with imperfect channel state information (CSI). It is proven that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust precoder (BDRP) without any loss of optimality. Furthermore, a low-complexity BDRP design with an ergodic sum-rate upper bound is developed, simplifying the iterative algorithm.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Optics
Ranjeet Kumar Pathak, Sumita Mishra, Preeta Sharan
Summary: A key role of the World Health Organisation is to improve early cancer diagnosis. Advances in computer engineering and optical field communication have inspired scholars to use computational algorithms to analyze illness prognosis. This article discusses the development of a 2D-photonic crystal biosensor to detect changes in refractive index of healthy and cancer cells, showing promising results for detecting various types of cancer. The accuracy of skin cancer detection is 81%, while blood cancer detection has the highest sensitivity at 89%.
JOURNAL OF OPTICS-INDIA
(2023)
Article
Engineering, Electrical & Electronic
Shenglong Zhou, Geoffrey Ye Li
Summary: In this paper, a hybrid federated learning algorithm (FedGiA) that combines gradient descent and the inexact alternating direction method of multipliers was proposed to address the challenges of saving communication resources, reducing computational costs, and achieving convergence. The proposed algorithm was proven to be theoretically and numerically more efficient in terms of communication and computation than other state-of-the-art algorithms, and it also achieves global convergence under mild conditions.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Article
Computer Science, Information Systems
Pallavi Asthana, Sumita Mishra, Nishu Gupta, Mohammad Derawi, Anil Kumar
Summary: Advanced ML methods accurately predict student's performance and provide metrics for improvement. Linear regression model has the highest accuracy of 97% compared to other models.
Proceedings Paper
Computer Science, Artificial Intelligence
Purnima Awasthi, Sumita Mishra, Nishu Gupta
Summary: The agriculture industry has undergone tremendous changes in recent years, facing obstacles such as climate change, pollution, and limited land and resources. To improve crop productivity, smarter technologies need to be adopted in agricultural practices. In this study, a machine learning framework is proposed to predict the yield of corn in 46 districts of Uttar Pradesh, India over a period of 37 years. By combining weather data, climatic data, soil data, and corn yield data, farmers can predict the annual corn production in their district. Evaluation of multiple models shows that the ensemble Bagging Extreme Gradient Boosting (XGBoost) regression model outperforms others, with an accuracy of 93.8% and RMSE= 9.1.
ADVANCED NETWORK TECHNOLOGIES AND INTELLIGENT COMPUTING, ANTIC 2022, PT II
(2023)
