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Ming Zeng

Publications and source records attributed to Ming Zeng.

At least 145 records · Page 8Linked to original sources
arXiv2019

Principle Study of Image Reconstruction Algorithms in Muon Tomography

Muon tomography is a relatively new method of radiography that utilizes muons from cosmic rays and their multiple Coulomb scattering property to distinguish materials. Researchers around the world have been developing various detection systems and image reconstruction algorithms for muon tomography applications, such as nuclear reactor monitoring and cargo inspection for contraband. This paper studies the principle in image reconstruction of muon tomography. Implementation and comparison of some popular algorithms with our simulation dataset will be presented as well as some ideas of future improvements for better image qualities and material discrimination performances.

physics.ins-det↗
arXiv2019

Dynamic Spectrum Sharing for Load Balancing in Multi-Cell Mobile Edge Computing

Large-scale mobile edge computing (MEC) systems require scalable solutions to allocate communication and computing resources to the users. In this letter we address this challenge by applying dynamic spectrum sharing among the base stations (BSs), together with local resource allocation in the cells. We show that the network-wide resource allocation can be transformed into a convex optimization problem, and propose a distributed, hierarchical solution with limited information exchange among the BSs. Numerical results demonstrate that the proposed solution is superior to other baseline algorithms, when wireless and computing resource allocation is not jointly optimized, or the wireless resources allocated to the BSs are fixed.

cs.IT↗
arXiv2019

Codebook-Based Max-Min Energy-Efficient Resource Allocation for Uplink mmWave MIMO-NOMA Systems

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bisection method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes

cs.IT↗
arXiv2019

Non-Orthogonal Multiple Access (NOMA): How It Meets 5G and Beyond

Due to massive connectivity and increasing demands of various services and data-hungry applications, a full-scale implementation of the fifth generation (5G) wireless systems requires more effective radio access techniques. In this regard, non-orthogonal multiple access (NOMA) has recently gained ever-growing attention from both academia and industry. Compared to orthogonal multiple access (OMA) techniques, NOMA is superior in terms of spectral efficiency and is thus appropriate for 5G and Beyond. In this article, we provide an overview of NOMA principles and applications. Specifically, the article discusses the fundamentals of power-domain NOMA with single and multiple antennas in both uplink and downlink settings. In addition, the basic principles of code-domain NOMA are elaborated. Further, the article explains various resource allocation techniques such as user pairing and power allocation for NOMA systems; discusses the basic form of cooperative NOMA and its variants; and addresses several opportunities and challenges associated with the compatibility of NOMA with other advanced communication paradigms such as heterogeneous networks and millimeter wave communications.

cs.IT↗
arXiv2019

Face Parsing with RoI Tanh-Warping

Face parsing computes pixel-wise label maps for different semantic components (e.g., hair, mouth, eyes) from face images. Existing face parsing literature have illustrated significant advantages by focusing on individual regions of interest (RoIs) for faces and facial components. However, the traditional crop-and-resize focusing mechanism ignores all contextual area outside the RoIs, and thus is not suitable when the component area is unpredictable, e.g. hair. Inspired by the physiological vision system of human, we propose a novel RoI Tanh-warping operator that combines the central vision and the peripheral vision together. It addresses the dilemma between a limited sized RoI for focusing and an unpredictable area of surrounding context for peripheral information. To this end, we propose a novel hybrid convolutional neural network for face parsing. It uses hierarchical local based method for inner facial components and global methods for outer facial components. The whole framework is simple and principled, and can be trained end-to-end. To facilitate future research of face parsing, we also manually relabel the training data of the HELEN dataset and will make it public. Experiments on both HELEN and LFW-PL benchmarks demonstrate that our method surpasses state-of-the-art methods.

cs.CV↗
arXiv2019

Energy-Efficient Joint User-RB Association and Power Allocation for Uplink Hybrid NOMA-OMA

In this paper, energy efficient resource allocation is considered for an uplink hybrid system, where non-orthogonal multiple access (NOMA) is integrated into orthogonal multiple access (OMA). To ensure the quality of service for the users, a minimum rate requirement is pre-defined for each user. We formulate an energy efficiency (EE) maximization problem by jointly optimizing the user clustering, channel assignment and power allocation. To address this hard problem, a many-to-one bipartite graph is first constructed considering the users and resource blocks (RBs) as the two sets of nodes. Based on swap matching, a joint user-RB association and power allocation scheme is proposed, which converges within a limited number of iterations. Moreover, for the power allocation under a given user-RB association, we first derive the feasibility condition. If feasible, a low-complexity algorithm is proposed, which obtains optimal EE under any successive interference cancellation (SIC) order and an arbitrary number of users. In addition, for the special case of two users per cluster, analytical solutions are provided for the two SIC orders, respectively. These solutions shed light on how the power is allocated for each user to maximize the EE. Numerical results are presented, which show that the proposed joint user-RB association and power allocation algorithm outperforms other hybrid multiple access based and OMA-based schemes.

cs.IT↗
arXiv2019

Securing Downlink Massive MIMO-NOMA Networks with Artificial Noise

In this paper, we focus on securing the confidential information of massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks by exploiting artificial noise (AN). An uplink training scheme is first proposed with minimum mean squared error estimation at the base station. Based on the estimated channel state information, the base station precodes the confidential information and injects the AN. Following this, the ergodic secrecy rate is derived for downlink transmission. An asymptotic secrecy performance analysis is also carried out for a large number of transmit antennas and high transmit power at the base station, respectively, to highlight the effects of key parameters on the secrecy performance of the considered system. Based on the derived ergodic secrecy rate, we propose the joint power allocation of the uplink training phase and downlink transmission phase to maximize the sum secrecy rates of the system. Besides, from the perspective of security, another optimization algorithm is proposed to maximize the energy efficiency. The results show that the combination of massive MIMO technique and AN greatly benefits NOMA networks in term of the secrecy performance. In addition, the effects of the uplink training phase and clustering process on the secrecy performance are revealed. Besides, the proposed optimization algorithms are compared with other baseline algorithms through simulations, and their superiority is validated. Finally, it is shown that the proposed system outperforms the conventional massive MIMO orthogonal multiple access in terms of the secrecy performance.

cs.IT↗
arXiv2019

Energy-Efficient Power Allocation in Uplink mmWave Massive MIMO with NOMA

In this paper, we study the energy efficiency (EE) maximization problem for an uplink millimeter wave massive multiple-input multiple-output system with non-orthogonal multiple access (NOMA). Multiple two-user clusters are formed according to their channel correlation and gain difference, and NOMA is applied within each cluster. Then, a hybrid analog-digital beamforming scheme is designed to lower the number of radio frequency chains at the base station (BS). On this basis, we formulate a power allocation (PA) problem to maximize the EE under users' quality of service requirements. An iterative algorithm is proposed to obtain the PA. Moreover, an enhanced NOMA scheme is also proposed, by exploiting the global information at the BS. Numerical results show that the proposed NOMA schemes achieve superior EE when compared with the conventional orthogonal multiple access scheme.

cs.IT↗
arXiv2019

PolarLight: a CubeSat X-ray Polarimeter based on the Gas Pixel Detector

The gas pixel detector (GPD) is designed and developed for high-sensitivity astronomical X-ray polarimetry, which is a new window about to open in a few years. Due to the small mass, low power, and compact geometry of the GPD, we propose a CubeSat mission Polarimeter Light (PolarLight) to demonstrate and test the technology directly in space. There is no optics but a collimator to constrain the field of view to 2.3 degrees. Filled with pure dimethyl ether (DME) at 0.8 atm and sealed by a beryllium window of 100 micron thick, with a sensitive area of about 1.4 mm by 1.4 mm, PolarLight allows us to observe the brightest X-ray sources on the sky, with a count rate of, e.g., ~0.2 counts/s from the Crab nebula. The PolarLight is 1U in size and mounted in a 6U CubeSat, which was launched into a low Earth Sun-synchronous orbit on October 29, 2018, and is currently under test. More launches with improved designs are planned in 2019. These tests will help increase the technology readiness for future missions such as the enhanced X-ray Timing and Polarimetry (eXTP), better understand the orbital background, and may help constrain the physics with observations of the brightest objects.

astro-ph.IM↗
arXiv2019

Plasma Lenses for Relativistic Laser Beams in Laser Wakefield Accelerators

Focusing petawatt-level laser beams to a variety of spot sizes for different applications is expensive in cost, labor and space. In this paper, we propose a plasma lens to flexibly resize the laser beam by utilizing the laser self-focusing effect. Using a fixed conventional focusing system to focus the laser a short distance in front of the plasma, we can adjust the effective laser beam waist within a certain range, as if a variety of focusing systems were used with the plasma lens acting as an adjustable eyepiece in a telescope. Such a setup is a powerful tool for laser wakefield accelerator experiments in state-of-art petawatt laser projects and allows for scanning focal spot parameters.

physics.plasm-ph↗
arXiv2018

Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention

Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean F1 score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.

cs.LG↗
arXiv2018

Semi-Supervised Convolutional Neural Networks for Human Activity Recognition

Labeled data used for training activity recognition classifiers are usually limited in terms of size and diversity. Thus, the learned model may not generalize well when used in real-world use cases. Semi-supervised learning augments labeled examples with unlabeled examples, often resulting in improved performance. However, the semi-supervised methods studied in the activity recognition literatures assume that feature engineering is already done. In this paper, we lift this assumption and present two semi-supervised methods based on convolutional neural networks (CNNs) to learn discriminative hidden features. Our semi-supervised CNNs learn from both labeled and unlabeled data while also performing feature learning on raw sensor data. In experiments on three real world datasets, we show that our CNNs outperform supervised methods and traditional semi-supervised learning methods by up to 18% in mean F1-score (Fm).

cs.LG↗
arXiv2017

Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues

This article presents advances in resource allocation (RA) for downlink non-orthogonal multiple access (NOMA) systems, focusing on user pairing (UP) and power allocation (PA) algorithms. The former pairs the users to obtain the high capacity gain by exploiting the channel gain difference between the users, while the later allocates power to users in each cluster to balance system throughput and user fairness. Additionally, the article introduces the concept of cluster fairness and proposes the divideand- next largest difference-based UP algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner. Furthermore, performance comparison between multiple-input multiple-output NOMA (MIMO-NOMA) and MIMO-OMA is conducted when users have pre-defined quality of service. Simulation results are presented, which validate the advantages of NOMA over OMA. Finally, the article provides avenues for further research on RA for downlink NOMA.

cs.IT↗
arXiv2017

A Fair Individual Rate Comparison between MIMO-NOMA and MIMO-OMA

In this paper, we compare the individual rate of MIMO-NOMA and MIMO-OMA when users are paired into clusters. A power allocation (PA) strategy is proposed, which ensures that MIMO-NOMA achieves a higher individual rate for each user than MIMO-OMA with arbitrary PA and optimal degrees of freedom split. In addition, a special case with equal degrees of freedom and arbitrary PA for OMA is considered, for which the individual rate superiority of NOMA still holds. Moreover, it is shown that NOMA can attain better fairness through appropriate PA. Finally, simulations are carried out, which validate the developed analytical results.

cs.IT↗
arXiv2017

A Two-Phase Power Allocation Scheme for CRNs Employing NOMA

In this paper, we consider the power allocation (PA) problem in cognitive radio networks (CRNs) employing nonorthogonal multiple access (NOMA) technique. Specifically, we aim to maximize the number of admitted secondary users (SUs) and their throughput, without violating the interference tolerance threshold of the primary users (PUs). This problem is divided into a two-phase PA process: a) maximizing the number of admitted SUs; b) maximizing the minimum throughput among the admitted SUs. To address the first phase, we apply a sequential and iterative PA algorithm, which fully exploits the characteristics of the NOMA-based system. Following this, the second phase is shown to be quasiconvex and is optimally solved via the bisection method. Furthermore, we prove the existence of a unique solution for the second phase and propose another PA algorithm, which is also optimal and significantly reduces the complexity in contrast with the bisection method. Simulation results verify the effectiveness of the proposed two-phase PA scheme.

cs.IT↗
arXiv2017

Cooperation in 5G HetNets: Advanced Spectrum Access and D2D Assisted Communications

The evolution of conventional wireless communication networks to the fifth generation (5G) is driven by an explosive increase in the number of wireless mobile devices and services, as well as their demand for all-time and everywhere connectivity, high data rates, low latency, high energy-efficiency and improved quality of service. To address these challenges, 5G relies on key technologies, such as full duplex (FD), device-to-device (D2D) communications, and network densification. In this article, a heterogeneous networking architecture is envisioned, where cells of different sizes and radio access technologies coexist. Specifically, collaboration for spectrum access is explored for both FD- and cognitive-based approaches, and cooperation among devices is discussed in the context of the state-of-the-art D2D assisted communication paradigm. The presented cooperative framework is expected to advance the understandings of the critical technical issues towards dynamic spectrum management for 5G heterogeneous networks.

cs.NI↗
arXiv2017

Energy-Efficient Power Allocation in Millimeter Wave Massive MIMO with Non-Orthogonal Multiple Access

In this letter, we investigate the energy efficiency (EE) problem in a millimeter wave (mmWave) massive MIMO (mMIMO) system with non-orthogonal multiple access (NOMA). Multiple two-user clusters are formulated according to their channel correlation and gain difference. Following this, we propose a hybrid analog/digital precoding scheme for the low radio frequency (RF) chains structure at the base station (BS). On this basis, we formulate a power allocation problem aiming to maximize the EE under users' quality of service (QoS) requirements and per-cluster power constraint. An iterative algorithm is proposed to obtain the optimal power allocation. Simulation results show that the proposed NOMA achieves superior EE performance than that of conventional OMA.

cs.IT↗