arXiv subjects

Jun Fang

Publications and source records attributed to Jun Fang.

At least 127 records · Page 7Linked to original sources

arXiv2018

Fast Beam Alignment for Millimeter Wave Communications: A Sparse Encoding and Phaseless Decoding Approach

In this paper, we studied the problem of beam alignment for millimeter wave (mmWave) communications, in which we assume a hybrid analog and digital beamforming structure is employed at the transmitter (i.e. base station), and an omni-directional antenna or an antenna array is used at the receiver (i.e. user). By exploiting the sparse scattering nature of mmWave channels, the beam alignment problem is formulated as a sparse encoding and phaseless decoding problem. More specifically, the problem of interest involves finding a sparse sensing matrix and an efficient recovery algorithm to recover the support and magnitude of the sparse signal from compressive phaseless measurements. A sparse bipartite graph coding (SBG-Coding) algorithm is developed for sparse encoding and phaseless decoding. Our theoretical analysis shows that, in the noiseless case, our proposed algorithm can perfectly recover the support and magnitude of the sparse signal with probability exceeding a pre-specified value from $\mathcal{O}(K^2)$ measurements, where $K$ is the number of nonzero entries of the sparse signal. The proposed algorithm has a simple decoding procedure which is computationally efficient and noise-robust. Simulation results show that our proposed method renders a reliable beam alignment in the low and moderate signal-to-noise ratio (SNR) regimes and presents a clear performance advantage over existing methods.

cs.IT↗

arXiv2018

Low-Rank Phase Retrieval via Variational Bayesian Learning

In this paper, we consider the problem of low-rank phase retrieval whose objective is to estimate a complex low-rank matrix from magnitude-only measurements. We propose a hierarchical prior model for low-rank phase retrieval, in which a Gaussian-Wishart hierarchical prior is placed on the underlying low-rank matrix to promote the low-rankness of the matrix. Based on the proposed hierarchical model, a variational expectation-maximization (EM) algorithm is developed. The proposed method is less sensitive to the choice of the initialization point and works well with random initialization. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.

stat.ML↗

arXiv2018

Proximal-Free ADMM for Decentralized Composite Optimization via Graph Simplification

We consider the problem of decentralized composite optimization over a symmetric connected graph, in which each node holds its own agent-specific private convex functions, and communications are only allowed between nodes with direct links. A variety of algorithms have been proposed to solve such a problem in an alternating direction method of multiplier (ADMM) framework. Many of these algorithms, however, need to include some proximal term in the augmented Lagrangian function such that the resulting algorithm can be implemented in a decentralized manner. The use of the proximal term slows down the convergence speed because it forces the current solution to stay close to the solution obtained in the previous iteration. To address this issue, in this paper, we first introduce the notion of simplest bipartite graph, which is defined as a bipartite graph that has a minimum number of edges to keep the graph connected. A simple two-step message passing-based procedure is proposed to find a simplest bipartite graph associated with the original graph. We show that the simplest bipartite graph has some interesting properties. By utilizing these properties, an ADMM without involving any proximal terms can be developed to perform decentralized composite optimization over the simplest bipartite graph. Simulation results show that our proposed method achieves a much faster convergence speed than existing state-of-the-art decentralized algorithms.

math.OC↗

arXiv2018

An explanation for the peculiar periphery of supernova remnant G309.2-0.6

Supernova remnant (SNR) G309.2-0.6 has a peculiar radio morphology with two bright ears to the southwest and northeast, although the main shell outside the ears is roughly circular. Based on an earlier proposal that the supernova ejecta has a jet component with extra energy, the dynamical evolution of the remnant is solved using 3D hydrodynamical (HD) simulation to investigate the formation of the periphery of the remnant. Assuming the ejecta with a kinetic energy of $10^{51}$\,erg and a mass of $3~\mathrm{M}_{\odot}$ evolved in a uniform ambient medium for a time of $\sim 4000$\,yr and the jet component has cylindrical symmetry with a half open angle of $10^\circ$, the result indicates that the energy contained in the jet is about $10-15\%$ of the kinetic energy of the entire ejecta to reproduce the detected profile. This study supports that the remnant originated from a jet-driven core-collapse supernova.

astro-ph.HE↗

arXiv2018

Super-resolution channel estimation for mmWave massive MIMO with hybrid precoding

Channel estimation is challenging for millimeter-wave (mmWave) massive MIMO with hybrid precoding, since the number of radio frequency (RF) chains is much smaller than that of antennas. Conventional compressive sensing based channel estimation schemes suffer from severe resolution loss due to the channel angle quantization. To improve the channel estimation accuracy, we propose an iterative reweight (IR)-based super-resolution channel estimation scheme in this paper. By optimizing an objective function through the gradient descent method, the proposed scheme can iteratively move the estimated angle of arrivals/departures (AoAs/AoDs) towards the optimal solutions, and finally realize the super-resolution channel estimation. In the optimization, a weight parameter is used to control the tradeoff between the sparsity and the data fitting error. In addition, a singular value decomposition (SVD)-based preconditioning is developed to reduce the computational complexity of the proposed scheme. Simulation results verify the better performance of the proposed scheme than conventional solutions.

cs.IT↗

arXiv2018

Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach

We consider the problem of spectrum sharing in a cognitive radio system consisting of a primary user and a secondary user. The primary user and the secondary user work in a non-cooperative manner. Specifically, the primary user is assumed to update its transmit power based on a pre-defined power control policy. The secondary user does not have any knowledge about the primary user's transmit power, or its power control strategy. The objective of this paper is to develop a learning-based power control method for the secondary user in order to share the common spectrum with the primary user. To assist the secondary user, a set of sensor nodes are spatially deployed to collect the received signal strength information at different locations in the wireless environment. We develop a deep reinforcement learning-based method, which the secondary user can use to intelligently adjust its transmit power such that after a few rounds of interaction with the primary user, both users can transmit their own data successfully with required qualities of service. Our experimental results show that the secondary user can interact with the primary user efficiently to reach a goal state (defined as a state in which both users can successfully transmit their data) from any initial states within a few number of steps.

cs.IT↗

arXiv2018

Physical Layer Service Integration in 5G: Potentials and Challenges

High transmission rate and secure communication have been identified as the key targets that need to be effectively addressed by fifth generation (5G) wireless systems. In this context, the concept of physical-layer security becomes attractive, as it can establish perfect security using only the characteristics of wireless medium. Nonetheless, to further increase the spectral efficiency, an emerging concept, termed physical-layer service integration (PHY-SI), has been recognized as an effective means. Its basic idea is to combine multiple coexisting services, i.e., multicast/broadcast service and confidential service, into one integral service for one-time transmission at the transmitter side. This article first provides a tutorial on typical PHY-SI models. Furthermore, we propose some state-of-the-art solutions to improve the overall performance of PHY-SI in certain important communication scenarios. In particular, we highlight the extension of several concepts borrowed from conventional single-service communications, such as artificial noise (AN), eigenmode transmission etc., to the scenario of PHY-SI. These techniques are shown to be effective in the design of reliable and robust PHY-SI schemes. Finally, several potential research directions are identified for future work.

cs.IT↗

arXiv2017

A hybrid approach to solve the high-frequency Helmholtz equation with source singularity in smooth heterogeneous media

We propose a hybrid approach to solve the high-frequency Helmholtz equation with point source terms in smooth heterogeneous media. The method is based on the ray-based finite element method (ray-FEM), whose original version can not handle the singularity close to point sources accurately. This pitfall is addressed by combining the ray-FEM, which is used to compute the smooth far-field of the solution accurately, with a high-order asymptotic expansion close to the point source, which is used to properly capture the singularity of the solution in the near-field. The method requires a fixed number of grid points per wavelength to accurately represent the wave field with an asymptotic convergence rate of $\mathcal{O}(ω^{-1/2})$, where $ω$ is the frequency parameter in the Helmholtz equation. In addition, a fast sweeping-type preconditioner is used to solve the resulting linear system. We present numerical examples in 2D to show both accuracy and efficiency of our method as the frequency increases. In particular, we provide numerical evidence of the convergence rate, and we show empirically that the overall complexity is $\mathcal{O}(ω^2)$ up to a poly-logarithmic factor.

math.NA↗

arXiv2017

Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning

In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method for recovery of common row sparsity MMV signals, where a pattern-coupled hierarchical Gaussian prior model is introduced to characterize both the block-sparsity of the coefficients and the statistical dependency between neighboring coefficients of the common row sparsity MMV signals. Unlike many other methods, the proposed method is able to automatically capture the block sparse structure of the unknown signal. Our method is developed using an expectation-maximization (EM) framework. Simulation results show that our proposed method offers competitive performance in recovering block-sparse common row sparsity pattern MMV signals.

cs.LG↗

arXiv2017

An explanation of the formation of the peculiar periphery of Tycho's supernova remnant

Tycho's supernova remnant (SNR) has a periphery which clearly deviates from spherical shape bash on the X-ray and radio observations. The forward shock from the southeast to the north of the remnant has a relatively deformed outline with a depression in the east, although in the west it is generally round and smooth. Moreover, at some locations of the shell, the supernova ejecta distributes closely to the forward shock to produce protrusions. Using 3D hydrodynamical simulation, the dynamical evolution of the supernova ejecta in an inhomogeneous medium and the formation process of the profile of the forward shock can be studied. To reproduce the peculiar periphery of the remnant, we propose a model in which the supernova ejecta has evolved in a cavity blown by a latitude-dependent outflow. The results indicate that, with the assumptions of the wind bubble driven by the anisotropic wind with a mass loss rate of $\sim10^{-7} {\rm M_{\odot}}$~yr$^{-1}$, a pole velocity of $\sim 100$~km s$^{-1}$, a duration of $\sim10^5$~yr just before the supernova explosion, and a spatial velocity of $\sim 30$~km s$^{-1}$ of the progenitor with respect to the circumstellar medium, the depression to the east and the protrusion to the southeast on the observed periphery of the remnant can be generally reproduced. In conclusion, an explanation to the peculiar shape of the periphery of Tycho's SNR is the supernova ejecta evolved in the cavity driven by the latitude-dependent wind.

astro-ph.HE↗

arXiv2017

Phased Array-Based Sub-Nyquist Sampling for Joint Wideband Spectrum Sensing and Direction-of-Arrival Estimation

In this paper, we study the problem of joint wideband spectrum sensing and direction-of-arrival (DoA) estimation in a sub-Nyquist sampling framework. Specifically, considering a scenario where a few uncorrelated narrowband signals spread over a wide (say, several GHz) frequency band, our objective is to estimate the carrier frequencies and the DoAs associated with the narrowband sources, as well as reconstruct the power spectra of these narrowband signals. To overcome the sampling rate bottleneck for wideband spectrum sensing, we propose a new phased-array based sub-Nyquist sampling architecture with variable time delays, where a uniform linear array (ULA) is employed and the received signal at each antenna is delayed by a variable amount of time and then sampled by a synchronized low-rate analog-digital converter (ADC). Based on the collected sub-Nyquist samples, we calculate a set of cross-correlation matrices with different time lags, and develop a CANDECOMP/PARAFAC (CP) decomposition-based method for joint DoA, carrier frequency and power spectrum recovery. Perfect recovery conditions for the associated parameters and the power spectrum are analyzed. Our analysis reveals that our proposed method does not require to place any sparse constraint on the wideband spectrum, only needs the sampling rate to be greater than the bandwidth of the narrowband source signal with the largest bandwidth among all sources. Simulation results show that our proposed method can achieve an estimation accuracy close to the associated Cramér-Rao bounds (CRBs) using only a small number of data samples.

eess.SP↗

arXiv2017

Channel Estimation for TDD/FDD Massive MIMO Systems with Channel Covariance Computing

In this paper, we propose a new channel estimation scheme for TDD/FDD massive MIMO systems by reconstructing uplink/downlink channel covariance matrices (CCMs) with the aid of array signal processing techniques. Specifically, the angle information and power angular spectrum (PAS) of each multi-path channel is extracted from the instantaneous uplink channel state information (CSI). Then, the uplink CCM is reconstructed and can be used to improve the uplink channel estimation without any additional training cost. In virtue of angle reciprocity as well as PAS reciprocity between uplink and downlink channels, the downlink CCM could also be inferred with a similar approach even for FDD massive MIMO systems. Then, the downlink instantaneous CSI can be obtained by training towards the dominant eigen-directions of each user. The proposed strategy is applicable for any kind of PAS distributions and array geometries. Numerical results are provided to demonstrate the superiority of the proposed methods over the existing ones.

cs.IT↗

arXiv2017

Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models

The problem of low rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and a Wishart distribution is specified as a hyperprior over the precision matrix. We show that such a hierarchical Gaussian prior has the potential to encourage a low-rank solution. Based on the proposed hierarchical prior model, a variational Bayesian method is developed for matrix completion, where the generalized approximate massage passing (GAMP) technique is embedded into the variational Bayesian inference in order to circumvent cumbersome matrix inverse operations. Simulation results show that our proposed method demonstrates superiority over existing state-of-the-art matrix completion methods.

cs.LG↗

arXiv2017

Bayesian Compressive Sensing Using Normal Product Priors

In this paper, we introduce a new sparsity-promoting prior, namely, the "normal product" prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework. The normal product distribution is the distribution of a product of two normally distributed variables with zero means and possibly different variances. Like other sparsity-encouraging distributions such as the Student's $t$-distribution, the normal product distribution has a sharp peak at origin, which makes it a suitable prior to encourage sparse solutions. A two-stage normal product-based hierarchical model is proposed. We resort to the variational Bayesian (VB) method to perform the inference. Simulations are conducted to illustrate the effectiveness of our proposed algorithm as compared with other state-of-the-art compressed sensing algorithms.

stat.ML↗

arXiv2017

Artificial Noise-Aided Biobjective Transmitter Optimization for Service Integration in Multi-User MIMO Gaussian Broadcast Channel

This paper considers an artificial noise (AN)-aided transmit design for multi-user MIMO systems with integrated services. Specifically, two sorts of service messages are combined and served simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver and required to be perfectly secure from other unauthorized receivers. Our interest lies in the joint design of input covariances of the multicast message, confidential message and artificial noise (AN), such that the achievable secrecy rate and multicast rate are simultaneously maximized. This problem is identified as a secrecy rate region maximization (SRRM) problem in the context of physical-layer service integration. Since this bi-objective optimization problem is inherently complex to solve, we put forward two different scalarization methods to convert it into a scalar optimization problem. First, we propose to prefix the multicast rate as a constant, and accordingly, the primal biobjective problem is converted into a secrecy rate maximization (SRM) problem with quality of multicast service (QoMS) constraint. By varying the constant, we can obtain different Pareto optimal points. The resulting SRM problem can be iteratively solved via a provably convergent difference-of-concave (DC) algorithm. In the second method, we aim to maximize the weighted sum of the secrecy rate and the multicast rate. Through varying the weighted vector, one can also obtain different Pareto optimal points. We show that this weighted sum rate maximization (WSRM) problem can be recast into a primal decomposable form, which is amenable to alternating optimization (AO). Then we compare these two scalarization methods in terms of their overall performance and computational complexity via theoretical analysis as well as numerical simulation, based on which new insights can be drawn.

cs.IT↗

arXiv2017

The optimal particle-mesh interpolation basis

The fast Ewald methods are widely used to compute the point-charge electrostatic interactions in molecular simulations. The key step that introduces errors in the computation is the particle-mesh interpolation. In this work, the optimal interpolation basis is derived by minimizing the estimated error of the fast Ewald method. The basis can be either general or model specific, depending on whether or not the charge correlation has been taken into account. By using the TIP3P water as an example system, we demonstrate that the general optimal basis is always more accurate than the B-spline basis in the investigated parameter range, while the computational cost is at most 5\% more expensive. In some cases, the optimal basis is found to be two orders of magnitude more accurate. The model specific optimal basis further improves the accuracy of the general optimal basis, but requires more computational effort in the optimization, and may not be transferable to systems with different charge correlations. Therefore, the choice between the general and model specific optimal bases is a trade-off between the generality and the accuracy.

physics.chem-ph↗

arXiv2017

Millimeter Wave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures

We consider the problem of channel estimation for millimeter wave (mmWave) systems, where, to minimize the hardware complexity and power consumption, an analog transmit beamforming and receive combining structure with only one radio frequency (RF) chain at the base station (BS) and mobile station (MS) is employed. Most existing works for mmWave channel estimation exploit sparse scattering characteristics of the channel. In addition to sparsity, mmWave channels may exhibit angular spreads over the angle of arrival (AoA), angle of departure (AoD), and elevation domains. In this paper, we show that angular spreads give rise to a useful low-rank structure that, along with the sparsity, can be simultaneously utilized to reduce the sample complexity, i.e. the number of samples needed to successfully recover the mmWave channel. Specifically, to effectively leverage the joint sparse and low-rank structure, we develop a two-stage compressed sensing method for mmWave channel estimation, where the sparse and low-rank properties are respectively utilized in two consecutive stages, namely, a matrix completion stage and a sparse recovery stage. Our theoretical analysis reveals that the proposed two-stage scheme can achieve a lower sample complexity than a direct compressed sensing method that exploits only the sparse structure of the mmWave channel. Simulation results are provided to corroborate our theoretical results and to show the superiority of the proposed two-stage method.

cs.IT↗

arXiv2017

New zirconium hydrides predicted by structure search method based on first principles calculations

The formation of precipitated zirconium (Zr) hydrides is closely related to the hydrogen embrittlement problem for the cladding materials of pressured water reactors (PWR). In this work, we systematically investigated the crystal structures of zirconium hydride (ZrHx) with different hydrogen concentrations (x = 0~2, atomic ratio) by combining the basin hopping algorithm with first principles calculations. We conclude that the P3m1 ζ-ZrH0.5 is dynamically unstable, while a novel dynamically stable P3m1 ZrH0.5 structure was discovered in the structure search. The stability of bistable P42/nnm ZrH1.5 structures and I4/mmm ZrH2 structures are also revisited. We find that the P42/nnm (c/a > 1) ZrH1.5 is dynamically unstable, while the I4/mmm (c/a = 1.57) ZrH2 is dynamically stable.The P42/nnm (c/a < 1) ZrH1.5 might be a key intermediate phase for the transition of γ->δ->ε phases. Additionally, by using the thermal dynamic simulations, we find that δ-ZrH1.5 is the most stable structure at high temperature while ZrH2 is the most stable hydride at low temperature. Slow cooling process will promote the formation of δ-ZrH1.5, and fast cooling process will promote the formation of γ-ZrH. These results may help to understand the phase transitions of zirconium hydrides.

cond-mat.mtrl-sci↗