arXiv subjects

Ji He

Publications and source records attributed to Ji He.

29 records · Page 2Linked to original sources

arXiv2024

Covert Multicast in UAV-Enabled Wireless Communication Systems With One-hop and Two-hop Strategies

This paper delves into the time-efficient covert multicast in a wireless communication system facilitated by Unmanned Aerial Vehicle (UAV), in which the UAV aims to disseminate a common covert information to multiple ground users (GUs) while suffering from the risk of detection by a ground warden (Willie). We propose one hop (OH) and two hop (TH) transmission schemes, first develop a theoretical framework for performance modeling of both the detection error probability at Willie and the transmission time at UAV. The optimization problems subject to the covertness constraint for the two transmission schemes are then formulated to gain insights into the system settings of the UAV's prior transmit probability, transmit power and horizontal location that affect the minimum transmission time. The optimization problems are non-convex and challenging to give numerical results. We thus explore the optimal setting of the transmit power and the prior transmit probability for the UAV separately under specific parameters with two schemes. We further propose a particle swarm optimization (PSO) based algorithm and an exhaustive algorithm to provide the joint solutions for the optimization problem with the OH transmission scheme and TH scheme, respectively. Finally, the efficiency of the proposed PSO-based algorithm is substantiated through extensive numerical results.

cs.CR↗

arXiv2023

Covert Communication in Hybrid Microwave/mmWave A2G Systems with Transmission Mode Selection

This paper investigates the covert communication in an air-to-ground (A2G) system, where a UAV (Alice) can adopt the omnidirectional microwave (OM) or directional mmWave (DM) transmission mode to transmit covert data to a ground user (Bob) while suffering from the detection of an adversary (Willie). For both the OM and DM modes, we first conduct theoretical analysis to reveal the inherent relationship between the transmit rate/transmit power and basic covert performance metrics in terms of detection error probability (DEP), effective covert rate (ECR), and covert Shannon capacity (CSC). To facilitate the transmission mode selection at Alice, we then explore the optimization of transmit rate and transmit power for ECR/CSC maximization under the OM and DM modes, and further propose a hybrid OM/DM transmission mode which allows the UAV to adaptively select between the OM and DM modes to achieve the maximum ECR and CSC at a given location of UAV. Finally, extensive numerical results are provided to illustrate the covert performances of the concerned A2G system under different transmission modes, and demonstrate that the hybrid OM/DM transmission mode outperforms the pure OM or DM mode in terms of covert performance.

cs.IT↗

arXiv2022

Opportunistic Wiretapping/Jamming: A New Attack Model in Millimeter-Wave Wireless Networks

While the millimeter-wave (mmWave) communication is robust against the conventional wiretapping attack due to its short transmission range and directivity, this paper proposes a new opportunistic wiretapping and jamming (OWJ) attack model in mmWave wireless networks. With OWJ, an eavesdropper can opportunistically conduct wiretapping or jamming to initiate a more hazardous attack based on the instantaneous costs of wiretapping and jamming. We also provide three realizations of the OWJ attack, which are mainly determined by the cost models relevant to distance, path loss and received power, respectively. To understand the impact of the new attack on mmWave network security, we first develop novel approximation techniques to characterize the irregular distributions of wiretappers, jammers and interferers under three OWJ realizations. With the help of the results of node distributions, we then derive analytical expressions for the secrecy transmission capacity to depict the network security performance under OWJ. Finally, we provide extensive numerical results to illustrate the effect of OWJ and to demonstrate that the new attack can more significantly degrade the network security performance than the pure wiretapping or jamming attack.

cs.CR↗

arXiv2018

Radon Inversion via Deep Learning

Radon transform is widely used in physical and life sciences and one of its major applications is the X-ray computed tomography (X-ray CT), which is significant in modern health examination. The Radon inversion or image reconstruction is challenging due to the potentially defective radon projections. Conventionally, the reconstruction process contains several ad hoc stages to approximate the corresponding Radon inversion. Each of the stages is highly dependent on the results of the previous stage. In this paper, we propose a novel unified framework for Radon inversion via deep learning (DL). The Radon inversion can be approximated by the proposed framework with an end-to-end fashion instead of processing step-by-step with multiple stages. For simplicity, the proposed framework is short as iRadonMap (inverse Radon transform approximation). Specifically, we implement the iRadonMap as an appropriative neural network, of which the architecture can be divided into two segments. In the first segment, a learnable fully-connected filtering layer is used to filter the radon projections along the view-angle direction, which is followed by a learnable sinusoidal back-projection layer to transfer the filtered radon projections into an image. The second segment is a common neural network architecture to further improve the reconstruction performance in the image domain. The iRadonMap is overall optimized by training a large number of generic images from ImageNet database. To evaluate the performance of the iRadonMap, clinical patient data is used. Qualitative results show promising reconstruction performance of the iRadonMap.

cs.CV↗

arXiv2018

Link Selection for Secure Cooperative Networks with Buffer-Aided Relaying

This paper investigates the secure communication in a two-hop cooperative wireless network, where a buffer-aided relay is utilized to forward data from the source to destination, and a passive eavesdropper attempts to intercept data transmission from both the source and relay. Depending on the availability of instantaneous channel state information of the source, two cases of transmission mechanisms, i.e., adaptive-rate transmission and fixed-rate transmission are considered. To enhance the security of the system, novel link selection policies are proposed for both cases to select source-to-relay, relay-to-destination, or no link transmission based on the channels qualities. Closed-form expressions are derived for the end-to-end secrecy outage probability (SOP), secrecy outage capacity (SOC), and exact secrecy throughput (EST), respectively. Furthermore, we prove the condition that EST reaches its maximum, and explore how to minimize the SOP and maximize the SOC by optimizing the link selection parameters. Finally, simulations are conducted to demonstrate the validity of our theoretical performance evaluation, and extensive numerical results are provided to illustrate the efficiency of the proposed link selection polices for the secure communication in two-hop cooperative networks.

cs.IT↗

arXiv2017

Pseudo Dual Energy CT Imaging using Deep Learning Based Framework: Initial Study

Dual energy computed tomography (DECT) has become of particular interest in clinic recent years. The DECT scan comprises two images, corresponding to two photon attenuation coefficients maps of the objects. Meanwhile, the DECT images are less accessible sometimes, compared to the conventional single energy CT (SECT). This motivates us to simulate pseudo DECT (pDECT) images from the SECT images. Inspired by recent advances in deep learning, we present a deep learning based framework to yield pDECT images from SECT images, utilizing the intrinsic characteristics underlying DECT images, i.e., global correlation and high similarity. To demonstrate the performance of the deep learning based framework, a cascade deep ConvNet (CD-ConvNet) approach is specifically presented in the deep learning framework. In the training step, the CD-ConvNet is designed to learn the non-linear mapping from the measured energy-specific (i.e., low-energy) CT images to the desired energy-specific (i.e., high-energy) CT images. In the testing step, the trained CD-ConvNet can be used to yield desired high-energy CT images from the low-energy CT images, and then produce accurate basic material maps. Clinical patient data were employed to validate and evaluate the presented CD-ConvNet approach performance. Both visual and quantitative results demonstrate the presented CD-ConvNet approach can yield high quality pDECT images and basic material maps.

physics.med-ph↗

arXiv2017

Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads

This paper addresses the problem of predicting popularity of comments in an online discussion forum using reinforcement learning, particularly addressing two challenges that arise from having natural language state and action spaces. First, the state representation, which characterizes the history of comments tracked in a discussion at a particular point, is augmented to incorporate the global context represented by discussions on world events available in an external knowledge source. Second, a two-stage Q-learning framework is introduced, making it feasible to search the combinatorial action space while also accounting for redundancy among sub-actions. We experiment with five Reddit communities, showing that the two methods improve over previous reported results on this task.

cs.CL↗

arXiv2016

Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads

We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space. A specified number of discussion threads predicted to be popular are recommended, chosen from a fixed window of recent comments to track. Novel deep reinforcement learning architectures are studied for effective modeling of the value function associated with actions comprised of interdependent sub-actions. The proposed model, which represents dependence between sub-actions through a bi-directional LSTM, gives the best performance across different experimental configurations and domains, and it also generalizes well with varying numbers of recommendation requests.

cs.CL↗

arXiv2016

Deep Reinforcement Learning with a Natural Language Action Space

This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games. Termed a deep reinforcement relevance network (DRRN), the architecture represents action and state spaces with separate embedding vectors, which are combined with an interaction function to approximate the Q-function in reinforcement learning. We evaluate the DRRN on two popular text games, showing superior performance over other deep Q-learning architectures. Experiments with paraphrased action descriptions show that the model is extracting meaning rather than simply memorizing strings of text.

cs.AI↗

arXiv2015

Recurrent Reinforcement Learning: A Hybrid Approach

Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states. It is in general very challenging to construct and infer hidden states as they often depend on the agent's entire interaction history and may require substantial domain knowledge. In this work, we investigate a deep-learning approach to learning the representation of states in partially observable tasks, with minimal prior knowledge of the domain. In particular, we propose a new family of hybrid models that combines the strength of both supervised learning (SL) and reinforcement learning (RL), trained in a joint fashion: The SL component can be a recurrent neural networks (RNN) or its long short-term memory (LSTM) version, which is equipped with the desired property of being able to capture long-term dependency on history, thus providing an effective way of learning the representation of hidden states. The RL component is a deep Q-network (DQN) that learns to optimize the control for maximizing long-term rewards. Extensive experiments in a direct mailing campaign problem demonstrate the effectiveness and advantages of the proposed approach, which performs the best among a set of previous state-of-the-art methods.

cs.LG↗

arXiv2015

End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i.e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document. Different from traditional variational learning or Gibbs sampling approaches, the proposed learning method applies (i) the mirror descent algorithm for maximum a posterior inference and (ii) back propagation over a deep architecture together with stochastic gradient/mirror descent for model parameter estimation, leading to scalable and end-to-end discriminative learning of the model. As a byproduct, we also apply this technique to develop a new learning method for the traditional unsupervised LDA model (i.e., BP-LDA). Experimental results on three real-world regression and classification tasks show that the proposed methods significantly outperform the previous supervised topic models, neural networks, and is on par with deep neural networks.

cs.LG↗