arXiv subjects

Jun Fang

Publications and source records attributed to Jun Fang.

At least 145 records · Page 8Linked to original sources

arXiv2017

Quantization Design and Channel Estimation for Massive MIMO Systems with One-Bit ADCs

We consider the problem of channel estimation for uplink multiuser massive MIMO systems, where, in order to significantly reduce the hardware cost and power consumption, one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. Channel estimation for one-bit massive MIMO systems is challenging due to the severe distortion caused by the coarse quantization. It was shown in previous studies that an extremely long training sequence is required to attain an acceptable performance. In this paper, we study the problem of optimal one-bit quantization design for channel estimation in one-bit massive MIMO systems. Our analysis reveals that, if the quantization thresholds are optimally devised, using one-bit ADCs can achieve an estimation error close to (with an increase by a factor of $π/2$) that of an ideal estimator which has access to the unquantized data. The optimal quantization thresholds, however, are dependent on the unknown channel parameters. To cope with this difficulty, we propose an adaptive quantization (AQ) approach in which the thresholds are adaptively adjusted in a way such that the thresholds converge to the optimal thresholds, and a random quantization (RQ) scheme which randomly generate a set of nonidentical thresholds based on some statistical prior knowledge of the channel. Simulation results show that, our proposed AQ and RQ schemes, owing to their wisely devised thresholds, present a significant performance improvement over the conventional fixed quantization scheme that uses a fixed (typically zero) threshold, and meanwhile achieve a substantial training overhead reduction for channel estimation. In particular, even with a moderate number of pilot symbols (about 5 times the number of users), the AQ scheme can provide an achievable rate close to that of the perfect channel state information (CSI) case.

cs.IT↗

arXiv2017

On Artificial-Noise Aided Transmit Design for Multi-User MISO Systems with Integrated Services

This paper considers artificial noise (AN)-aided transmit designs for multi-user MISO systems in the eyes of service integration. Specifically, we combine two sorts of services, and serve them simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. Our goal is to jointly design the optimal input covariances for the multicast message, confidential message and AN, such that the achievable secrecy rate region is maximized subject to the sum power constraint. This secrecy rate region maximization (SRRM) problem is a nonconvex vector maximization problem. To handle it, we reformulate the SRRM problem into a provably equivalent scalar optimization problem and propose a searching method to find all of its Pareto optimal points. The equivalent scalar optimization problem is identified as a secrecy rate maximization (SRM) problem with the quality of multicast service (QoMS) constraints. Further, we show that this equivalent QoMS-constrained SRM problem, albeit nonconvex, can be efficiently handled based on a two-stage optimization approach, including solving a sequence of semidefinite programs. Moreover, we also extend the SRRM problem to an imperfect channel state information (CSI) case where a worst-case robust formulation is considered. In particular, while transmit beamforming is generally a suboptimal technique to the SRRM problem, we prove that it is optimal for the confidential message transmission whether in the perfect CSI scenario or in the imperfect CSI scenario. Finally, numerical results demonstrate that the AN-aided transmit designs are effective in expanding the achievable secrecy rate regions.

cs.IT↗

arXiv2017

Kaiser-Bessel Basis for the Particle-Mesh Interpolation

In this work, we introduce the Kaiser-Bessel interpolation basis for the particle-mesh interpolation in the fast Ewald method. A reliable a priori error estimate is developed to measure the accuracy of the force computation in correlated charge systems, and is shown to be effective in optimizing the shape parameter of the Kaiser-Bessel basis in terms of accuracy. By comparing the optimized Kaiser-Bessel basis with the traditional B-spline basis, we demonstrate that the former is more accurate than the latter in part of the working parameter space, saying a relatively small real space cutoff, a relatively small reciprocal space mesh and a relatively large truncation of basis. In some cases, the Kaiser-Bessel basis is found to be more than one order of magnitude more accurate.

physics.chem-ph↗

arXiv2017

Greedy Sparse Signal Reconstruction Using Matching Pursuit Based on Hope-tree

The reconstruction of sparse signals requires the solution of an $\ell_0$-norm minimization problem in Compressed Sensing. Previous research has focused on the investigation of a single candidate to identify the support (index of nonzero elements) of a sparse signal. To ensure that the optimal candidate can be obtained in each iteration, we propose here an iterative greedy reconstruction algorithm (GSRA). First, the intersection of the support sets estimated by the Orthogonal Matching Pursuit (OMP) and Subspace Pursuit (SP) is set as the initial support set. Then, a hope-tree is built to expand the set. Finally, a developed decreasing subspace pursuit method is used to rectify the candidate set. Detailed simulation results demonstrate that GSRA is more accurate than other typical methods in recovering Gaussian signals, 0--1 sparse signals, and synthetic signals.

cs.IT↗

arXiv2017

Large-Scale Antenna-Assisted Grant-free Non-Orthogonal Multiple Access via Compressed Sensing

Support massive connectivity is an important requirement in 5G wireless communication system. For massive Machine Type Communication (MTC) scenario, since the network is expected to accommodate a massive number of MTC devices with sparse short message, the multiple access scheme like current LTE uplink would not be suitable. In order to reduce the signaling overhead, we consider an grant-free multiple access system, which requires the receiver facilitate activity detection, channel estimation, and data decoding in "one shot" and without the knowledge of active user's pilots. However, most of the "one shot" communication research haven't considered the massive MIMO scenario. In this work we propose a Multiple Measurement Model (MMV) model based Massive MIMO and exploit the covariance matrix of the measurements to confirm a high activity detection rate.

cs.IT↗

arXiv2016

GSVD-Based Precoding in MIMO Systems With Integrated Services

This letter considers a two-receiver multiple-input multiple-output (MIMO) Gaussian broadcast channel model with integrated services. Specifically, we combine two sorts of service messages, and serve them simultaneously: one multicast message intended for both receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from the unauthorized receiver. Due to the coupling of service messages, it is intractable to seek capacity-achieving transmit covariance matrices. Accordingly, we propose a suboptimal precoding scheme based on the generalized singular value decomposition (GSVD). The GSVD produces several virtual orthogonal subchannels between the transmitter and the receivers. Subchannel allocation and power allocation between multicast message and confidential message are jointly optimized to maximize the secrecy rate in this letter, subject to the quality of multicast service (QoMS) constraints. Since this problem is inherently complex, a difference-of-concave (DC) algorithm, together with an exhaustive search, is exploited to handle the power allocation and subchannel allocation, respectively. Numerical results are presented to illustrate the efficacy of our proposed strategies.

cs.IT↗

arXiv2016

Linear Precoder Design for a MIMO Gaussian Wiretap Channel with Full-Duplex Source and Destination Nodes

We consider linear precoder design for a multiple-input multiple-output (MIMO) Gaussian wiretap channel, which comprises two legitimate nodes, i.e., Alice and Bob, operating in Full-Duplex (FD) mode and exchanging confidential messages in the presence of a passive eavesdropper. Using the sum secrecy degrees of freedoms (sum S.D.o.F.) as reliability measure, we formulate an optimization problem with respect to the precoding matrices. In order to solve this problem, we first propose a cooperative secrecy transmission scheme, and prove that its feasible set is sufficient to achieve the maximum sum S.D.o.F.. Based on that feasible set, we then determine the maximum achievable sum S.D.o.F. in closed form, and provide a method for constructing the precoding matrix pair which achieves the maximum sum S.D.o.F.. Results show that, the FD based network provides an attractive secrecy transmission rate performance.

cs.IT↗

arXiv2016

Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems

We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large antenna arrays for directional precoding/beamforming. Hybrid analog and digital beamforming structures are employed in order to offer a compromise between hardware complexity and system performance. Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for channel parameter estimation (including angles of arrival/departure, time delays, and fading coefficients). In our proposed method, the received signal at the BS is expressed as a third-order tensor. We show that the tensor has the form of a low-rank CP decomposition, and the channel parameters can be estimated from the associated factor matrices. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. We also develop Cramer-Rao bound (CRB) results for channel parameters, and compare our proposed method with a compressed sensing-based method. Simulation results show that the proposed method attains mean square errors that are very close to their associated CRBs, and presents a clear advantage over the compressed sensing-based method in terms of both estimation accuracy and computational complexity.

cs.IT↗

arXiv2016

Robust Bayesian Compressed sensing

We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers. A new sparse Bayesian learning method is developed for robust compressed sensing. The basic idea of the proposed method is to identify and remove the outliers from sparse signal recovery. To automatically identify the outliers, we employ a set of binary indicator hyperparameters to indicate which observations are outliers. These indicator hyperparameters are treated as random variables and assigned a beta process prior such that their values are confined to be binary. In addition, a Gaussian-inverse Gamma prior is imposed on the sparse signal to promote sparsity. Based on this hierarchical prior model, we develop a variational Bayesian method to estimate the indicator hyperparameters as well as the sparse signal. Simulation results show that the proposed method achieves a substantial performance improvement over existing robust compressed sensing techniques.

stat.ML↗

arXiv2016

Learning Dominant Wave Directions For Plane Wave Methods For High-Frequency Helmholtz Equations

We present a ray-based finite element method (ray-FEM) by learning basis adaptive to the underlying high-frequency Helmholtz equation in smooth media. Based on the geometric optics ansatz of the wave field, we learn local dominant ray directions by probing the medium using low-frequency waves with the same source. Once local ray directions are extracted, they are incorporated into the finite element basis to solve the high-frequency Helmholtz equation. This process can be continued to further improve approximations for both local ray directions and the high frequency wave field iteratively. The method requires a fixed number of grid points per wavelength to represent the wave field and achieves an asymptotic convergence as the frequency $ω\rightarrow \infty$ without the pollution effect. A fast solver is developed for the resulting linear system with an empirical complexity $\mathcal{O}(ω^d)$ up to a poly-logarithmic factor. Numerical examples in 2D are presented to corroborate the claims.

math.NA↗

arXiv2016

Low-Rank Covariance-Assisted Downlink Training and Channel Estimation for FDD Massive MIMO Systems

We consider the problem of downlink training and channel estimation in frequency division duplex (FDD) massive MIMO systems, where the base station (BS) equipped with a large number of antennas serves a number of single-antenna users simultaneously. To obtain the channel state information (CSI) at the BS in FDD systems, the downlink channel has to be estimated by users via downlink training and then fed back to the BS. For FDD large-scale MIMO systems, the overhead for downlink training and CSI uplink feedback could be prohibitively high, which presents a significant challenge. In this paper, we study the behavior of the minimum mean-squared error (MMSE) estimator when the channel covariance matrix has a low-rank or an approximate low-rank structure. Our theoretical analysis reveals that the amount of training overhead can be substantially reduced by exploiting the low-rank property of the channel covariance matrix. In particular, we show that the MMSE estimator is able to achieve exact channel recovery in the asymptotic low-noise regime, provided that the number of pilot symbols in time is no less than the rank of the channel covariance matrix. We also present an optimal pilot design for the single-user case, and an asymptotic optimal pilot design for the multi-user scenario. Lastly, we develop a simple model-based scheme to estimate the channel covariance matrix, based on which the MMSE estimator can be employed to estimate the channel. The proposed scheme does not need any additional training overhead. Simulation results are provided to verify our theoretical results and illustrate the effectiveness of the proposed estimated covariance-assisted MMSE estimator.

cs.IT↗

arXiv2016

Multiple Staggered Mesh Ewald: Boosting the Accuracy of the Smooth Particle Mesh Ewald Method

The smooth particle mesh Ewald (SPME) method is the standard method for computing the electrostatic interactions in the molecular simulations. In this work, the multiple staggered mesh Ewald (MSME) method is proposed to boost the accuracy of the SPME method. Unlike the SPME that achieves higher accuracy by refining the mesh, the MSME improves the accuracy by averaging the standard SPME forces computed on, e.g. $M$, staggered meshes. We prove, from theoretical perspective, that the MSME is as accurate as the SPME, but uses $M^2$ times less mesh points in a certain parameter range. In the complementary parameter range, the MSME is as accurate as the SPME with twice of the interpolation order. The theoretical conclusions are numerically validated both by a uniform and uncorrelated charge system, and by a three-point-charge water system that is widely used as solvent for the bio-macromolecules.

physics.comp-ph↗

arXiv2016

Perturbation-Assisted PAPR Reduction for Large-Scale MIMO-OFDM Systems via ADMM

We consider the problem of peak-to-average power ratio (PAPR) reduction for orthogonal frequency-division multiplexing (OFDM) based large-scale multiple-input multipleoutput (MIMO) systems. A novel perturbation-assisted scheme is developed to reduce the PAPRs of the transmitted signals by exploiting the redundant degrees-of-freedom (DoFs) inherent in the large-scale antenna array. Specifically, we introduce artificial perturbation signals to the frequency-domain precoded signals, with the aim of reducing the PAPRs of their time-domain counterpart signals. Meanwhile, the additive perturbation signal associated with each tone is constrained to lie in the null-space of its associated channel matrix, such that it does not cause any multi-user inference or out-of-band radiations. Such a problem is formulated as a convex optimization problem, and an efficient algorithm is developed by resorting to the variable splitting and alterative direction method of multipliers (ADMM) techniques. Simulation results show that the proposed method has a fast convergence rate and achieves substantial PAPR reduction within only tens of iterations. In addition, unlike other precoding-based PAPR reduction methods, our proposed method which introduces perturbation signals to the precoded signals is independent of the precoding stage and thus could be more suitable for practical systems.

cs.IT↗

arXiv2016

On the existence of the optimal order for wavefunction extrapolation in Born-Oppenheimer molecular dynamics

Wavefunction extrapolation greatly reduces the number of self-consistent field (SCF) iterations and thus the overall computational cost of Born-Oppenheimer molecular dynamics (BOMD) that is based on the Kohn-Sham density functional theory. Going against the intuition that the higher order of extrapolation possesses a better accuracy, we demonstrate, from both theoretical and numerical perspectives, that the extrapolation accuracy firstly increases and then decreases with respect to the order, and an optimal extrapolation order in terms of minimal number of SCF iterations always exists. We also prove that the optimal order tends to be larger when using larger MD time steps or more strict SCF convergence criteria. By example BOMD simulations of a solid copper system, we show that the optimal extrapolation order covers a broad range when varying the MD time step or the SCF convergence criterion. Therefore, we suggest the necessity for BOMD simulation packages to open the user interface and to provide more choices on the extrapolation order. Another factor that may influence the extrapolation accuracy is the alignment scheme that eliminates the discontinuity in the wavefunctions with respect to the atomic or cell variables. We prove the equivalence between the two existing schemes, thus the implementation of either of them does not lead to essential difference in the extrapolation accuracy.

physics.comp-ph↗

arXiv2016

Sampling the isothermal-isobaric ensemble by Langevin dynamics

We present a new method of conducting molecular dynamics simulation in isothermal-isobaric ensemble based on Langevin equations of motion. The stochastic coupling to all particle and cell degrees of freedoms is introduced in a correct way, in the sense that the stationary configurational distribution is proved to be in consistent with that of the isothermal-isobaric ensemble. In order to apply the proposed method in computer simulations, a second order symmetric numerical integration scheme is developed by Trotter's splitting of the single-step propagator. Moreover, a practical guide of choosing working parameters is suggested for user specified thermo- and baro-coupling time-scales. The method and software implementation are carefully validated by a numerical example.

cond-mat.stat-mech↗

arXiv2016

Channel Estimation for Millimeter Wave Multiuser MIMO Systems via PARAFAC Decomposition

We consider the problem of uplink channel estimation for millimeter wave (mmWave) systems, where the base station (BS) and mobile stations (MSs) are equipped with large antenna arrays to provide sufficient beamforming gain for outdoor wireless communications. Hybrid analog and digital beamforming structures are employed by both the BS and the MS due to hardware constraints. We propose a layered pilot transmission scheme and a CANDECOMP/PARAFAC (CP) decomposition-based method for joint estimation of the channels from multiple users (i.e. MSs) to the BS. The proposed method exploits the sparse scattering nature of the mmWave channel and the intrinsic multi-dimensional structure of the multiway data collected from multiple modes. The uniqueness of the CP decomposition is studied and sufficient conditions for essential uniqueness are obtained. The conditions shed light on the design of the beamforming matrix, the combining matrix and the pilot sequences, and meanwhile provide general guidelines for choosing system parameters. Our analysis reveals that our proposed method can achieve a substantial training overhead reduction by employing the layered pilot transmission scheme. Simulation results show that the proposed method presents a clear advantage over a compressed sensing-based method in terms of both estimation accuracy and computational complexity.

cs.IT↗

arXiv2016

Improving Wireless Physical Layer Security via Exploiting Co-Channel Interference

This paper considers a scenario in which a source-destination pair needs to establish a confidential connection against an external eavesdropper, aided by the interference generated by another source-destination pair that exchanges public messages. The goal is to compute the maximum achievable secrecy degrees of freedom (S.D.o.F) region of a MIMO two-user wiretap network. First, a cooperative secrecy transmission scheme is proposed, whose feasible set is shown to achieve all S.D.o.F. pairs on the S.D.o.F. region boundary. In this way, the determination of the S.D.o.F. region is reduced to a problem of maximizing the S.D.o.F. pair over the proposed transmission scheme. The maximum achievable S.D.o.F. region boundary points are obtained in closed form, and the construction of the precoding matrices achieving the maximum S.D.o.F. region boundary is provided. The obtained analytical expressions clearly show the relation between the maximum achievable S.D.o.F. region and the number of antennas at each terminal.

cs.IT↗

arXiv2016

Dominant itinerant ferromagnetism in Eu0.5Sr0.5CoO3: evidences from both critical analysis and Rhodes-Wohlfarth's criterion

The critical behaviors of ferromagnet Eu0.5Sr0.5CoO3 arround TC=140.5K have been comprehensively investigated by analyzing a series of isothermal magnetization M(H) curves. Both Modified Arrott plot and Kouvel-Fisher methods give nearly the same critical exponents, which scale nicely the M(H) curves into two different branches below and above TC. The exponents γ=1.044 and δ=3.06 demonstrate the relevance of mean-field characters for this material. The conclusion of mean-field behavior proves a dominant itinerant ferromagnetism (FM) due to a long range exchange interaction in the system. Meanwhile, by using Rhodes-Wohlfarth's criterion [P. Rhodes and E. P. Wohlfarth, Proc. R. Soc. Lond. A 273, 247 (1963)], it is further confirmed that the itinerant FM dominates in the system.

cond-mat.str-el↗