Optimal Covariance Matrix Research Articles

SINR Enhancement in Colocated MIMO Radar Using Transmit Covariance Matrix Optimization

In this letter, we focus on the signal-to-interference-plus-noise ratio (SINR) enhancement using transmitting waveform covariance matrix optimization in colocated MIMO radars. In our proposed algorithm, transmitted waveform covariance matrix has been optimized to focus the transmit beampattern into the target direction and in the receiver, we try to reject maximum number of interfering objects. In the proposed algorithm, transmitted waveform covariance matrix is able to be synthesized with binary phase shift keying waveforms in closed form. Simulation results show that our proposed method has better SINR performance in comparison with methods in which transmitted covariance matrix is designed independently from the interferences locations.

Robust Transceiver Design in Multiuser MIMO System with Energy Harvesting Constraints

In this paper, considering the simultaneous wireless information and power transfer scheme, we investigate the robust transceiver design in the multiuser multiple-input–multiple-output system with the energy harvesting constraints where the channel uncertainties are modeled by the worst-case model. We formulate the robust transceiver design problem as a worst-case mean-square-error (MSE) minimization problem where the transmit covariance matrix at the transmitter and the preprocessing matrices at the information-decoding (ID) receivers are alternatively optimized. With the known preprocessing matrices at the ID receivers, the transmit covariance matrix optimization problem is transformed into a semidefinite programming (SDP) by employing the \({\mathcal {S}}\)-procedure. With the known transmit covariance matrix at the transmitter, the optimization problem is transformed into another SDP by converting the MSE minimization constraint into a linear matrix inequality. Simulation results have shown that our proposed robust transceiver design outperforms the non-robust one.

AstroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

Given the complexity of modern cosmological parameter inference where we are faced with non-Gaussian data and noise, correlated systematics and multi-probe correlated data sets, the Approximate Bayesian Computation (ABC) method is a promising alternative to traditional Markov Chain Monte Carlo approaches in the case where the Likelihood is intractable or unknown. The ABC method is called "Likelihood free" as it avoids explicit evaluation of the Likelihood by using a forward model simulation of the data which can include systematics. We introduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler for parameter estimation. A key challenge in astrophysics is the efficient use of large multi-probe datasets to constrain high dimensional, possibly correlated parameter spaces. With this in mind astroABC allows for massive parallelization using MPI, a framework that handles spawning of jobs across multiple nodes. A key new feature of astroABC is the ability to create MPI groups with different communicators, one for the sampler and several others for the forward model simulation, which speeds up sampling time considerably. For smaller jobs the Python multiprocessing option is also available. Other key features include: a Sequential Monte Carlo sampler, a method for iteratively adapting tolerance levels, local covariance estimate using scikit-learn's KDTree, modules for specifying optimal covariance matrix for a component-wise or multivariate normal perturbation kernel, output and restart files are backed up every iteration, user defined metric and simulation methods, a module for specifying heterogeneous parameter priors including non-standard prior PDFs, a module for specifying a constant, linear, log or exponential tolerance level, well-documented examples and sample scripts. This code is hosted online at https://github.com/EliseJ/astroABC

Joint energy efficiency optimization with nonlinear precoding in multi-cell broadcast systems

In this paper, we focus on maximizing weighted sum energy efficiency (EE) for a multi-cell multi-user channel. In order to solve this non-convex problem, we first decompose the original problem into a sequence of parallel subproblems which can optimized separately. For each subproblem, a base station employs dirty paper coding to maximize the EE for users within a cell while regulating interference induced to other cells. Since each sub-problem can be transformed to a convex multiple-access channel problem, the proposed method provides a closed-form solution for power allocation. Then, based on the derived optimal covariance matrix for each subproblem, a local optimal solution is obtained to maximize the sum EE. Finally, simulation results show that our algorithm based on non-linear precoding achieves about 20 percent performance gains over the conventional linear precoding method.

Optimal Signaling for Secure Communications Over Gaussian MIMO Wiretap Channels

Optimal signaling over the Gaussian multiple-input multiple-output wire-tap channel is studied under the total transmit power constraint. A closed-form solution for an optimal transmit covariance matrix is obtained when the channel is strictly degraded. In combination with the rank-1 solution, this provides the complete characterization of the optimal covariance for the case of two transmit antennas. The cases of weak eavesdropper and high SNR are considered. It is shown that the optimal covariance does not converge to a scaled identity in the high-SNR regime. Necessary optimality conditions and a tight upper bound on the rank of an optimal covariance matrix are established for the general case, along with a lower bound to the secrecy capacity, which is tight in a number of scenarios.

배열 안테나의 상관성 신호에서 원하는 신호 추정 방법에 대한 연구

In this paper, we studied for modified MUSIC algorithm of direction of arrival (DOA)estimation. Modified MUSIC algorithm search optimal covariance matrix using singular value decomposition and Bayes method, and desired signals are estimated by updating weight. In order to estimation of desired signals, we used sub spatial method of MUSIC algorithm. General MUSIC algorithm can estimate a desired signal in case of non-correlation signal. But, general MUSIC algorithm in case of correlation signal can not estimate a desired signals and resolution is decreased. Though simulation in case of correlation signal, we analyze to compare proposed MUSIC algorithm with general MUSIC algorithm.

Low Complexity User Scheduling, Ordering and Transmit Covariance Matrix Optimization Algorithms for Successive Zero-Forcing Precoding

In this paper we consider user scheduling, ordering and transmit covariance matrix optimization problems under successive zero-forcing (SZF) precoding for multiuser multiple-input multiple-output downlink. We propose a heuristic user scheduling metric and an intermediate user grouping technique to develop a low complexity greedy scheduling algorithm. A suboptimal user ordering technique is also proposed for transmit covariance matrix optimization under SZF. Proposed algorithm is of low complexity, but performs closely to the highly complex exhaustive search algorithm. For transmit covariance optimization under SZF, a dirty paper coding based algorithm has been previously proposed, which is computationally very complex. In this paper, we propose a suboptimal but much simplified algorithm, which employs an iterative procedure similar to a known multiple access channel (MAC) covariance optimization algorithm, but does not involve multiple levels of covariance matrix transformations. With the proposed suboptimal user ordering the exhaustive search through all possible user orders is avoided during transmit covariance matrix optimization resulting in a significant complexity reduction, and without a significant performance penalty. Simulation results show that the proposed algorithm performs very close to the known algorithm in the low SNR region.

On Secrecy Capacity of Fast Fading MIMOME Wiretap Channels with Statistical CSIT

In this paper, we consider secure transmissions in ergodic Rayleigh fast-faded multiple-input multiple-output multiple-antenna-eavesdropper (MIMOME) wiretap channels with only statistical channel state information at the transmitter (CSIT). When the legitimate receiver has more (or equal) antennas than the eavesdropper, we prove the first MIMOME secrecy capacity with partial CSIT by establishing a new secrecy capacity upper-bound. The key step is to form an MIMOME degraded channel by dividing the legitimate receiver's channel matrix into two submatrices, and setting one of the submatrices to be the same as the eavesdropper's channel matrix. Next, under the total power constraint over all transmit antennas, we analytically solve the channel-input covariance matrix optimization problem to fully characterize the MIMOME secrecy capacity. Typically, the MIMOME optimization problems are non-concave. However, thank to the proposed degraded channel, we can transform the stochastic MIMOME optimization problem to be a Schur-concave one and then find its solution. Besides total power constraint, we also investigate the secrecy capacity when the transmitter is subject to the practical per-antenna power constraint. The corresponding optimization problem is even more difficult since it is not Schuar-concave. Under the two power constraints considered, the corresponding MIMOME secrecy capacities can both scale with the signal-to-noise ratios (SNR) when the difference between numbers of antennas at legitimate receiver and eavesdropper are large enough. However, when the legitimate receiver and eavesdropper have a single antenna each, such SNR scalings do not exist for both cases.

Knowledge‐aided robust adaptive beamforming with small snapshots

A robust adaptive beamforming method is proposed to combat arbitrary array steering vector (SV) mismatches and small sample size. First, with the imprecise knowledge of the array manifold and angular sectors of the interferences, an a priori covariance matrix can be acquired. Secondly, a theoretically optimal covariance matrix can be obtained by combining an a priori covariance matrix with the sample covariance matrix. Finally, the mismatch vector between the actual and presumed SV can be estimated by solving a quadratic convex optimisation problem.

혼합모형을 이용한 체지방 반복측정자료에 대한 통계적 분석

체지방 감량에 대한 효과를 분석하고자 실험에 참가한 체지방율이 30% 이상인 42명의 여대생을 대상으로 일반폰을 사용하는 그룹과 스마트폰을 사용하는 그룹으로 나누어서 측정자료를 2주 간격으로 정리하여 8주간에 걸친 체지방 및 관련자료를 얻었다. 이 실험자료를 바탕으로 혼합모형을 이용하여 분석한 결과 AR(1)의 공분산행렬이 가장 적합한 모형으로 선택되었으며, 시점 간의 상관계수는 0.838로 상당히 밀접한 관련을 보여주었다. AR(1)의 공분산행렬을 설정하여 분석한 결과 처리간의 차이에서 스마트폰의 사용자가 일반폰의 사용자보다 0.654kg 정도의 체지방 감량 효과를 보여주었으며, 시간이 지날수록 체지방 감소효과가 있음을 알 수 있다. 그러나 처리와 시간과의 교호작용은 존재하지 않는다. 또한 실험실시 전의 체지방값과 총콜레스테롤은 유의하게 나타났으며, 섭취하는 칼로리는 약간 관련이 있으나, 허리엉덩이비율은 유의하지 않는 것으로 판명되었다. Forty two female students whose fat mass ratio was over 30% were participated in the experiment of fat mass loss of two treatments for 8 weeks. They kept diary for foods they ate every day, took a picture of the foods, transmitted the picture to the experimenter by the camera phone. Among those, 28 students took the picture by regular camera phone (Treatment A), and the other students used smart phone (Treatment B). Fat mass weight and its related variables had been measured repeatedly four times at an interval of two weeks during 8 weeks. It was shown from mixed model analysis of repeated measurements data that AR(1) covariance matrix was selected as the optimal covariance matrix pattern. The correlation between two successive times is highly correlated as 0.838. Based upon the AR(1) covariance matrix structure, the students using smart phones were somewhat more effective in losing fat mass weight than the students using regular camera phones. The time effect was highly significant, but the treatment-time interaction effect was insignificant. The baseline effect and total cholesterol were found to be significant, but the calories with taking foods were somewhat significant, but the waist to hip ratio was found to be insignificant.

Optimization of MIMO Systems Capacity Using Large Random Matrix Methods

This paper provides a comprehensive introduction of large random matrix methods for input covariance matrix optimization of mutual information of MIMO systems. It is first recalled informally how large system approximations of mutual information can be derived. Then, the optimization of the approximations is discussed, and important methodological points that are not necessarily covered by the existing literature are addressed, including the strict concavity of the approximation, the structure of the argument of its maximum, the accuracy of the large system approach with regard to the number of antennas, or the justification of iterative water-filling optimization algorithms. While the existing papers have developed methods adapted to a specific model, this contribution tries to provide a unified view of the large system approximation approach.

독립요소분석을 이용한 태아심전도 추출을 위한 시변 칼만 평활기의 개발 : 예비연구

태아심박의 모니터링은 태아의 안녕을 평가하기 위해 중요한 정보이다. 비관혈적인 태아심전도는 산모의 복부 신호로부터 추출될 수 있다. 따라서 산모 복부의 혼합 신호로부터 태아심전도를 추출하기 위해 많은 유망한 신호처리 방법들이 개발되어 왔다. 그러나 비관혈적인 태아심전도는 안정적인 신호 측정법이 부족하고 신호처리 방법의 어려움으로 여전히 임상에서 널리 적용되지 못하고 있다. 태아심전도를 추출하는 신호처리 결과는 가우시안 백색잡음에 의해 신호대잡음비가 낮아질 수 있다. 본 논문에서는 태아심전도에서 시간에 따라 변하는 백색잡음 신호를 제거하여 신호대잡음비를 높이기 위한 방법으로 시변 칼만 평활기를 제안하였고 그 가능성을 확인하였다. Wiener 과정을 시스템 모델로 설정하고 백색잡음 크기에 따라 공분산 행렬을 수정하였으며, 이를 통해 칼만 이득을 변화시켜 평활화 정도를 가변시킬 수 있도록 설계하였다. 5채널 태아심전도 모델을 이용하여 백색잡음의 크기에 따른 최적 공분산 행렬 값을 구하였고, 모의실험을 통해 제안된 방법의 가능성을 확인할 수 있었다.

Robust Transmitter Design for Amplify-and-Forward MIMO Relay Systems Exploiting Only Channel Statistics

In this paper, we address statistically robust transmit design problems that maximize the average mutual information of amplify-and-forward (AF) multiple-input multiple-output (MIMO) two-hop relay channels having perfect channel state information at the receiver (CSIR) and statistical channel state information at the transmitter (CSIT). In the selected scenario, the source, relay, and destination terminals are equipped with correlated antennas where a direct link between the source and the destination terminals can be found. Moreover, the statistical CSIT consists of the channel means and covariance matrices of various links. The design problem is taken into account for regimes in large systems because there are no closed-form expressions of the average mutual information in the MIMO relay channel. The contribution of this study includes the derivation of asymptotic mutual information expressions for the MIMO relay channel in the large system limit. Additionally, an efficient optimization algorithm based on the asymptotic mutual information is proposed in order to find the asymptotically optimal source covariance matrix and the relay amplifying matrix. Numerical simulation results show that the new approach achieves indistinguishable performance compared to those of the maximization approaches in finite-dimensional systems, even for a small number of antennas at each link. The impact of the CSI of various links on the throughput of the MIMO relay channel is studied using the new approach.

Uncertain Volatility Model: A Monte-Carlo Approach

The uncertain volatility model has long ago attracted the attention of practitioners as it provides worst-case pricing scenario for the sell-side. The valuation of a financial derivative based on this model requires solving a fully non-linear PDE. One can rely on finite difference schemes only when the number of variables (that is, underlyings and path-dependent variables) is small - in practice no more than three. In all other cases, numerical valuation seems out of reach. In this paper, we outline two accurate, easy-to-implement Monte-Carlo-like methods which hardly depend on dimensionality. The first method requires a parameterization of the optimal covariance matrix and consists in a series of backward low-dimensional optimizations. The second method relies heavily on a recently established connection between second-order backward stochastic differential equations and non-linear second-order parabolic PDEs. Both methods are illustrated by numerical experiments.

Dynamic Robust Games in MIMO Systems

In this paper, we study dynamic robust power-allocation games in multiple-input-multiple-output systems under the imperfectness of the channel-state information at the transmitters. Using a robust pseudopotential-game approach, we show the existence of robust solutions in both discrete and continuous action spaces under suitable conditions. Considering the imperfectness in terms of the payoff measurement at the transmitters, we propose a COmbined fully DIstributed Payoff and Strategy Reinforcement Learning (CODIPAS-RL) in which each transmitter learns its payoff function, as well as the associated optimal covariance matrix strategies. Under the heterogeneous CODIPAS-RL, the transmitters can use different learning patterns (heterogeneous learning) and different learning rates. We provide sufficient conditions for the almost-sure convergence of the heterogeneous learning to ordinary differential equations. Extensions of the CODIPAS-RL to Itô's stochastic differential equations are discussed.

MIMO Gaussian Bidirectional Broadcast Channels with Common Messages

In this work, the MIMO Gaussian bidirectional broadcast channel (BBC) with common messages is studied. The problem is motivated by the concept of bidirectional relaying in a three-node network, where a half-duplex relay node establishes a bidirectional communication between two other nodes using a decode-and-forward protocol and thereby adds an own multicast message to the communication. The capacity region of the broadcast phase is derived and the corresponding transmit covariance matrix optimization problem is analyzed in detail. Thereby, it is shown that the transmit covariance optimization problem is strongly connected to the corresponding one of the MIMO Gaussian BBC without common messages. In particular, this knowledge can be exploited to transfer results such as optimal transmit strategies from one scenario to the other one.

Uncertain Volatility Model: A Monte-Carlo Approach

The uncertain volatility model has long ago attracted the attention of practitioners as it provides worst-case pricing scenario for the sell-side. The valuation of a financial derivative based on this model requires solving a fully non-linear PDE. One can rely on finite difference schemes only when the number of variables (that is, underlyings and path-dependent variables) is small - in practice no more than three. In all other cases, numerical valuation seems out of reach. In this paper, we outline two accurate, easy-to-implement Monte-Carlo-like methods which hardly depend on dimensionality. The first method requires a parameterization of the optimal covariance matrix and consists in a series of backward low-dimensional optimizations. The second method relies heavily on a recently established connection between second-order backward stochastic differential equations and non-linear second-order parabolic PDEs. Both methods are illustrated by numerical experiments.

On the optimal transmit strategy for the MIMO bidirectional broadcast channel

In this work the transmit covariance matrix optimization problem for the discrete memoryless MIMO Gaussian bidirectional broadcast channel is studied. A half-duplex relay node establishes bidirectional communication between two nodes using a decode-and-forward protocol. In the initial multiple access phase both nodes transmit their messages to the relay node. In the succeeding phase the relay broadcasts an optimal re-encoded message so that both nodes can decode the other's message using their own message as side information. The capacity region of the bidirectional broadcast channel is completely characterized by a weighted rate sum maximization problem, which can be solved by a simple iterative fixed point algorithm. If an efficient transmit covariance matrix is invariant with respect to the joint subspace spanned by the channels, then different combinations of the part transmitted on the orthogonal subspaces result in equivalent transmit strategies with different ranks. A closed-form procedure to obtain the optimal transmit covariance is derived for the case where the rank of the channels is equal to the number of antennas at the relay node and a full-rank transmission is optimal. It shows the complicated structure of the optimal eigenspace, which depends on the weights and the mean transmit power constraint. For parallel channels the optimal solution is completely characterized and discussed, which also solves the optimal power allocation problem for a single-antenna OFDM system.

Population Monte Carlo Algorithm in High Dimensions

The population Monte Carlo algorithm is an iterative importance sampling scheme for solving static problems. We examine the population Monte Carlo algorithm in a simplified setting, a single step of the general algorithm, and study a fundamental problem that occurs in applying importance sampling to high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of estimate under conditions on the importance function. We demonstrate the exponential growth of the asymptotic variance with the dimension and show that the optimal covariance matrix for the importance function can be estimated in special cases.

Ergodic Channel Capacities for the Amplify-and-Forward Half-Duplex Cooperative Systems

In this paper, the ergodic channel capacities are established for the amplify-and-forward (AF) half-duplex cooperative systems, which consist of a source node, a destination, and multiple-relay nodes. The relay nodes assist the transmission from the source node to the destination. Since the channel matrices for the cooperative systems involve product of Gaussian random variables, which are no longer Gaussian, the approach in obtaining the ergodic channel capacity for conventional multiple-input-multiple-output (MIMO) Gaussian channels (Telatar, 1999) is not applicable. By using a novel approach, we have arrived at the following conclusions about the ergodic channel capacities for the AF cooperative systems. For the single antenna AF relay systems in which all nodes are equipped with one antenna, the optimal covariance matrix of the input signals to achieve the ergodic channel capacity is diagonal, and the diagonal elements are obtained by solving optimization problems of multidimensional integrals. These diagonal entries are not all equal even if all the channel gains in the cooperative systems are independent and identically distributed (i.i.d.) Gaussian with unit variance. Therefore, a white input signal for the AF cooperative system may not achieve the ergodic channel capacity of the system. This is in direct contrast to the case of conventional multiple-input-single-output (MISO) systems having i.i.d. Gaussian channel gains of unit variance, in which case, the ergodic capacity is achieved if the input covariance matrix is a scaled identity matrix. For the MIMO relay system in which the nodes have multiple antennas, the input covariance matrix to achieve the ergodic capacity is block diagonal and each block diagonalizes the autocorrelation of channel matrix from the source to the destination. This is different from the case of conventional MIMO systems, where the input covariance matrix to achieve the ergodic channel capacity diagonalizes the channel autocorrelation matrix of the MIMO system (Tulina, Lozano, and Verdu, 2006). If the channel gains in conventional MIMO systems are correlated Gaussian random variables, the input covariance matrix is a full matrix, not block diagonal; and if the channel gains are i.i.d. Gaussians, the optimal input covariance matrix is a scaled identity. The observations obtained in this paper reveal useful insights of how the AF cooperative systems ldquomimicrdquo the conventional MISO and MIMO systems from the ergodic channel capacity perspective.