arXiv subjects

Chunbo Luo

Publications and source records attributed to Chunbo Luo.

32 records · Page 2Linked to original sources

arXiv2024

Intelligent Reflecting Surfaces vs. Full-Duplex Relays: A Comparison in the Air

This letter aims to provide a fundamental analytical comparison for the two major types of relaying methods: intelligent reflecting surfaces and full-duplex relays, particularly focusing on unmanned aerial vehicle communication scenarios. Both amplify-and-forward and decode-and-forward relaying schemes are included in the comparison. In addition, optimal 3D UAV deployment and minimum transmit power under the quality of service constraint are derived. Our numerical results show that IRSs of medium size exhibit comparable performance to AF relays, meanwhile outperforming DF relays under extremely large surface size and high data rates.

eess.SP↗

arXiv2024

Towards the THz Networks in the 6G Era

This commentary dedicates to envision what role THz is going to play in the coming human-centric 6G era. Three distinct THz network types including outdoor, indoor, and body area networks are discussed, with an emphasis on their capabilities in human body detection. Synthesizing these networks will unlock a bunch of fascinating applications across industrial, biomedical and entertainment fields, significantly enhancing the quality of human life.

eess.SY↗

arXiv2023

DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes

Most feature-based stereo visual odometry (SVO) approaches estimate the motion of mobile robots by matching and tracking point features along a sequence of stereo images. However, in dynamic scenes mainly comprising moving pedestrians, vehicles, etc., there are insufficient robust static point features to enable accurate motion estimation, causing failures when reconstructing robotic motion. In this paper, we proposed DynPL-SVO, a complete dynamic SVO method that integrated united cost functions containing information between matched point features and re-projection errors perpendicular and parallel to the direction of the line features. Additionally, we introduced a \textit{dynamic} \textit{grid} algorithm to enhance its performance in dynamic scenes. The stereo camera motion was estimated through Levenberg-Marquard minimization of the re-projection errors of both point and line features. Comprehensive experimental results on KITTI and EuRoC MAV datasets showed that accuracy of the DynPL-SVO was improved by over 20\% on average compared to other state-of-the-art SVO systems, especially in dynamic scenes.

cs.CV↗

arXiv2023

MMANet: Margin-aware Distillation and Modality-aware Regularization for Incomplete Multimodal Learning

Multimodal learning has shown great potentials in numerous scenes and attracts increasing interest recently. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practice. To this end, we propose a general framework called MMANet to assist incomplete multimodal learning. It consists of three components: the deployment network used for inference, the teacher network transferring comprehensive multimodal information to the deployment network, and the regularization network guiding the deployment network to balance weak modality combinations. Specifically, we propose a novel margin-aware distillation (MAD) to assist the information transfer by weighing the sample contribution with the classification uncertainty. This encourages the deployment network to focus on the samples near decision boundaries and acquire the refined inter-class margin. Besides, we design a modality-aware regularization (MAR) algorithm to mine the weak modality combinations and guide the regularization network to calculate prediction loss for them. This forces the deployment network to improve its representation ability for the weak modality combinations adaptively. Finally, extensive experiments on multimodal classification and segmentation tasks demonstrate that our MMANet outperforms the state-of-the-art significantly. Code is available at: https://github.com/shicaiwei123/MMANet

cs.CV↗

arXiv2023

Behavioural Reports of Multi-Stage Malware

The extensive damage caused by malware requires anti-malware systems to be constantly improved to prevent new threats. The current trend in malware detection is to employ machine learning models to aid in the classification process. We propose a new dataset with the objective of improving current anti-malware systems. The focus of this dataset is to improve host based intrusion detection systems by providing API call sequences for thousands of malware samples executed in Windows 10 virtual machines. A tutorial on how to create and expand this dataset is provided along with a benchmark demonstrating how to use this dataset to classify malware. The data contains long sequences of API calls for each sample, and in order to create models that can be deployed in resource constrained devices, three feature selection methods were tested. The principal innovation, however, lies in the multi-label classification system in which one sequence of APIs can be tagged with multiple labels describing its malicious behaviours.

cs.CR↗

arXiv2022

Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges

Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is missing. Recent breakthroughs in deep learning (DL) have laid the foundation for developing high-performance DL-AMR approaches for communications systems. Comparing with traditional modulation detection methods, DL-AMR approaches have achieved promising performance including high recognition accuracy and low false alarms due to the strong feature extraction and classification abilities of deep neural networks. Despite the promising potential, DL-AMR approaches also bring concerns to complexity and explainability, which affect the practical deployment in wireless communications systems. This paper aims to present a review of the current DL-AMR research, with a focus on appropriate DL models and benchmark datasets. We further provide comprehensive experiments to compare the state of the art models for single-input-single-output (SISO) systems from both accuracy and complexity perspectives, and propose to apply DL-AMR in the new multiple-input-multiple-output (MIMO) scenario with precoding. Finally, existing challenges and possible future research directions are discussed.

eess.SP↗

arXiv2021

An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation

Automatic modulation recognition (AMR) is a promising technology for intelligent communication receivers to detect signal modulation schemes. Recently, the emerging deep learning (DL) research has facilitated high-performance DL-AMR approaches. However, most DL-AMR models only focus on recognition accuracy, leading to huge model sizes and high computational complexity, while some lightweight and low-complexity models struggle to meet the accuracy requirements. This letter proposes an efficient DL-AMR model based on phase parameter estimation and transformation, with convolutional neural network (CNN) and gated recurrent unit (GRU) as the feature extraction layers, which can achieve high recognition accuracy equivalent to the existing state-of-the-art models but reduces more than a third of the volume of their parameters. Meanwhile, our model is more competitive in training time and test time than the benchmark models with similar recognition accuracy. Moreover, we further propose to compress our model by pruning, which maintains the recognition accuracy higher than 90% while has less than 1/8 of the number of parameters comparing with state-of-the-art models.

eess.SP↗

arXiv2021

Fractional order graph neural network

This paper proposes fractional order graph neural networks (FGNNs), optimized by the approximation strategy to address the challenges of local optimum of classic and fractional graph neural networks which are specialised at aggregating information from the feature and adjacent matrices of connected nodes and their neighbours to solve learning tasks on non-Euclidean data such as graphs. Meanwhile the approximate calculation of fractional order gradients also overcomes the high computational complexity of fractional order derivations. We further prove that such an approximation is feasible and the FGNN is unbiased towards global optimization solution. Extensive experiments on citation networks show that FGNN achieves great advantage over baseline models when selected appropriate fractional order.

cs.LG↗

arXiv2021

The effects of regularisation on RNN models for time series forecasting: Covid-19 as an example

Many research papers that propose models to predict the course of the COVID-19 pandemic either use handcrafted statistical models or large neural networks. Even though large neural networks are more powerful than simpler statistical models, they are especially hard to train on small datasets. This paper not only presents a model with grater flexibility than the other proposed neural networks, but also presents a model that is effective on smaller datasets. To improve performance on small data, six regularisation methods were tested. The results show that the GRU combined with 20% Dropout achieved the lowest RMSE scores. The main finding was that models with less access to data relied more on the regulariser. Applying Dropout to a GRU model trained on only 28 days of data reduced the RMSE by 23%.

cs.LG↗

arXiv2020

A DoA Estimation Based Robust Beam Forming Method for UAV-BS Communication

High data rate communication with Unmanned Aerial Vehicles (UAV) is of growing demand among industrial and commercial applications since the last decade. In this paper, we investigate enhancing beam forming performance based on signal Direction of Arrival (DoA) estimation to support UAV-cellular network communication. We first study UAV fast moving scenario where we found that drone's mobility cause degradation of beam forming algorithm performance. Then, we propose a DoA estimation algorithm and a steering vector adaptive receiving beam forming method. The DoA estimation algorithm is of high precision with low computational complexity. Also it enables a beam former to timely adjust steering vector value in calculating beam forming weight. Simulation results show higher SINR performance and more stability of proposed method than traditional method based on Multiple Signal Classification (MUSIC) DoA estimation algorithm.

eess.SP↗

arXiv2019

A Model-driven and Data-driven Fusion Framework for Accurate Air Quality Prediction

Air quality is closely related to public health. Health issues such as cardiovascular diseases and respiratory diseases, may have connection with long exposure to highly polluted environment. Therefore, accurate air quality forecasts are extremely important to those who are vulnerable. To estimate the variation of several air pollution concentrations, previous researchers used various approaches, such as the Community Multiscale Air Quality model (CMAQ) or neural networks. Although CMAQ model considers a coverage of the historic air pollution data and meteorological variables, extra bias is introduced due to additional adjustment. In this paper, a combination of model-based strategy and data-driven method namely the physical-temporal collection(PTC) model is proposed, aiming to fix the systematic error that traditional models deliver. In the data-driven part, the first components are the temporal pattern and the weather pattern to measure important features that contribute to the prediction performance. The less relevant input variables will be removed to eliminate negative weights in network training. Then, we deploy a long-short-term-memory (LSTM) to fetch the preliminary results, which will be further corrected by a neural network (NN) involving the meteorological index as well as other pollutants concentrations. The data-set we applied for forecasting is from January 1st, 2016 to December 31st, 2016. According to the results, our PTC achieves an excellent performance compared with the baseline model (CMAQ prediction, GRU, DNN and etc.). This joint model-based data-driven method for air quality prediction can be easily deployed on stations without extra adjustment, providing results with high-time-resolution information for vulnerable members to prevent heavy air pollution ahead.

stat.AP↗

arXiv2019

Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework

Intrusion detection systems (IDSs) play an important role in identifying malicious attacks and threats in networking systems. As fundamental tools of IDSs, learning based classification methods have been widely employed. When it comes to detecting network intrusions in small sample sizes (e.g., emerging intrusions), the limited number and imbalanced proportion of training samples usually cause significant challenges in training supervised and semi-supervised classifiers. In this paper, we propose a general network intrusion detection framework to address the challenges of both \emph{data scarcity} and \emph{data imbalance}. The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation. Given a small set of network intrusion samples, it first derives a Poisson-Gamma joint probabilistic generative model to generate synthesised intrusion data using Monte Carlo methods. Those synthesised data are then augmented by deep generative neural networks through adversarial learning. Finally, it adopts the augmented intrusion data to train supervised models for detecting network intrusions. Comprehensive experimental validations on KDD Cup 99 dataset show that the proposed framework outperforms the existing learning based IDSs in terms of improved accuracy, precision, recall, and F1-score.

cs.CR↗

arXiv2017

On Channel State Feedback Model and Overhead in Theoretical and Practical Views

Channel state feedback plays an important role to the improvement of link performance in current wireless communication systems, and even more in the next generation. The feedback information, however, consumes the uplink bandwidth and thus generates overhead. In this paper, we investigate the impact of channel state feedback and propose an improved scheme to reduce the overhead in practical communication systems. Compared with existing schemes, we introduce a more accurate channel model to describe practical wireless channels and obtain the theoretical lower bounds of overhead for the periodical and aperiodical feedback schemes. The obtained theoretical results provide us the guidance to optimise the design of feedback systems, such as the number of bits used for quantizing channel states. We thus propose a practical feedback scheme that achieves low overhead and improved performance over currently widely used schemes such as zero holding. Simulation experiments confirm its advantages and suggest its potentially wide applications in the next generation of wireless systems.

cs.IT↗

arXiv2015

Hybrid Demodulate-Forward Relay Protocol for Two-Way Relay Channels

Two-Way Relay Channel (TWRC) plays an important role in relay networks, and efficient relaying protocols are particularly important for this model. {\color{black} However, existing protocols may not be able to realize the potential of TWRC if the two independent fading channels are not carefully handled}. In this paper, a Hybrid DeModulate-Forward (HDMF) protocol is proposed to address such a problem. {\color{black} We first introduce the two basic components of HDMF - direct and differential DMF, and then propose the key decision criterion for HDMF based on the corresponding log-likelihood ratios. We further enhance the protocol so that it can be applied independently from the modulation schemes. Through extensive mathematical analysis, theoretical performance of the proposed protocol is investigated. By comparing with existing protocols, the proposed HDMF has lower error rate. A novel scheduling scheme for the proposed protocol is introduced, which has lower length than the benchmark method. The results also reveal the protocol's potential to improve spectrum efficiency of relay channels with unbalanced bilateral traffic.}

cs.IT↗