Jung-Chieh Chen

Decentralized Plug-in Electric Vehicle Charging Selection Algorithm in Power Systems

This paper uses a charging selection concept for plug-in electric vehicles (PEVs) to maximize user convenience levels while meeting predefined circuit-level demand limits. The optimal PEV-charging selection problem requires an exhaustive search for all possible combinations of PEVs in a power system, which cannot be solved for the practical number of PEVs. Inspired by the efficiency of the convex relaxation optimization tool in finding close-to-optimal results in huge search spaces, this paper proposes the application of the convex relaxation optimization method to solve the PEV-charging selection problem. Compared with the results of the uncontrolled case, the simulated results indicate that the proposed PEV-charging selection algorithm only slightly reduces user convenience levels, but significantly mitigates the impact of the PEV-charging on the power system. We also develop a distributed optimization algorithm to solve the PEV-charging selection problem in a decentralized manner, i.e., the binary charging decisions (charged or not charged) are made locally by each vehicle. Using the proposed distributed optimization algorithm, each vehicle is only required to report its power demand rather than report several of its private user state information, mitigating the security problems inherent in such problem. The proposed decentralized algorithm only requires low-speed communication capability, making it suitable for real-time implementation.

Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning

Pilot contamination posts a fundamental limit on the performance of massive multiple-input-multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To address this problem, we propose estimation of only the channel parameters of the desired links in a target cell, but those of the interference links from adjacent cells. The required estimation is, nonetheless, an underdetermined system. In this paper, we show that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. Our strategy is inspired by the observation that for a cellular network, the channel from user equipment to a base station is composed of only a few clustered paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. As a result, if the signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators, the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination.

Network-side mobile position location using factor graphs

A low-complexity high-accuracy algorithm is proposed to estimate the location of a target MS based on network-side time-of-arrival (TOA) measurements. Under a factor graph framework, the proposed algorithm first constructs a graphical model for the mobile position location problem by dividing the problem into many mutually-interactive local constraints. Each local constraint is enforced by a separate local processing unit. Efficient exchange of soft-information among local processing units in the mobile switching center (MSC) then iteratively purifies the estimate of the MS location. Numerical results show that the proposed algorithm not only enjoys low complexity, suitable for integrated-circuit implementation, but it is also able to achieve performance very close to the optimum achievable solution accuracy, the maximum likelihood (ML) solution accuracy

An Efficient Pilot Design Scheme for Sparse Channel Estimation in OFDM Systems

This paper investigates the pilot placement problem for sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. Prompted by the success of the compressed sensing technique in recovering sparse signals from undersampled measurements, compressed sensing has been successfully applied for pilot-aided sparse channel estimation in OFDM systems to reduce the transmitted overhead. However, the selection of pilot tones significantly affects channel estimation performance. Seeking optimal pilot placement for sparse channel estimation, in the sense of minimum mean-square error of the channel estimation, through an exhaustive search of all possible pilot placements is extremely computationally intensive. To reduce the computational complexity and simultaneously maximize the accuracy of sparse channel estimation, cross-entropy optimization is introduced to determine the optimal pilot placement. Computer simulation results demonstrate that the pilot index sequences obtained using the proposed method performed better compared with those obtained using the conventional equispaced scheme and the random search method.

Efficient Identification Method for Power Line Outages in the Smart Power Grid

This paper considers the use of phasor angle measurements provided by phasor measurement units to identify multiple power line outages. The problem of power line outage identification has traditionally been formulated as a combinatorial optimization problem, the optimal solution of which can be found through an exhaustive search. However, the size of the search space grows exponentially with the number of outages and may thus pose a potential problem for the practical implementation of an exhaustive search, especially when multiple power line outages are considered in a power system. To reduce the complexity while improving outage detection performance, we propose a novel global stochastic optimization technique based on cross-entropy optimization, which has been proven to be a powerful tool for many combinatorial optimization problems, to identify multiple line outages. To validate the effectiveness of the proposed approach, the algorithm is tested using IEEE 118- and 300-bus systems, as well as a Polish 2736-bus system. Simulation results demonstrate that the percentage of correctly identified line outages achieved by the proposed method outperforms those obtained by existing sparse signal recovery algorithms.

Mobile position location using factor graphs

Making use of the time-of-arrival measurements and their stochastic properties, we propose a low-complexity high-accuracy algorithm to estimate the location of a target mobile station (MS). Under a factor graph framework, in the proposed algorithm, soft-information is efficiently exchanged among local processing units to iteratively purify the estimate of the MS location. Numerical results show that the proposed algorithm not only enjoys advantages of low complexity, suitable for integrated-circuit implementation, but it is also able to achieve performance very close to the optimum achievable bound, the maximum-likelihood bound.

Improved Constant Envelope Multiuser Precoding for Massive MIMO Systems

This letter considers the constant envelope precoding method, proposed by Mohammed and Larsson, to minimize the multiuser interference (MUI) in a massive multiple-input multiple-output (MIMO) antenna system. To achieve promising performance for suppressing MUI, nevertheless, constant envelope precoding requires solving the intractable nonlinear least squares (NLS) problem of the transmit phase angles, which is non-convex and has multiple local minima. To tackle this, our approach is to use cross-entropy optimization (CEO) for solving the NLS problem of constant envelope precoding. Simulation results reveal that the CEO-optimized constant envelope precoding significantly outperforms the conventional gradient descent (GD) based constant envelope precoding and zero-forcing.

Spatially Correlated MIMO Multiple-Access Systems With Macrodiversity: Asymptotic Analysis Via Statistical Physics

This paper studies the asymptotic performance of a multiple-input multiple-output (MIMO) multiple-access (MA) wireless network where spatial correlations at both the transmitters (i.e., mobile stations) and the central receiver (i.e., base station) exist, and more than one multiantenna sets are employed at the receiver to provide macrodiversity. The sense of asymptotic behavior we consider is the large-system limit in which the numbers of antennas at each mobile station and antenna set go to infinity with their ratios fixed. Using the replica method, we derive analytical solutions to the asymptotic spectral efficiency (SE) of the MIMO-MA systems for any given input distributions (not necessarily Gaussian) from the mobile transmitters. Our results can be regarded as the generalization of many previously known results for degenerate cases. Another contribution of this paper is an efficient algorithm to determine the asymptotic optimum transmit-signal covariance matrices that can maximize the SE of the MIMO-MA network within the period in which the spatial channel covariance information (CCI) can be considered static, and assuming that only CCI is available at the transmitters

A Deterministic Equivalent for the Analysis of Non-Gaussian Correlated MIMO Multiple Access Channels

Using large-dimensional random matrix theory (RMT), we conduct mutual information analysis of a multiple-input multiple-output (MIMO) multiple access channel (MAC). Our channel model reflects the characteristics in small-cell networks where antenna correlations, line-of-sight components, and general type of fading distributions have to be included. The mutual information expression can be expressed as functionals of the Stieltjes transform through the so-called Shannon transform. Ideally, if the Stieltjes transform is known in the context of the large-dimensional RMT, then the problem is solved. However, it is difficult to derive the Stieltjes transform of the considered channel models directly, especially when the transmit correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian. To overcome this, we use the generalized Lindeberg principle to show that the Stieltjes transforms of this class of random matrices with Gaussian or non-Gaussian independent entries coincide in the large-dimensional regime. This result permits to derive the deterministic equivalents (e.g., the Stieltjes transform and the ergodic mutual information) for non-Gaussian MIMO channels from the known results developed for Gaussian MIMO channels. As an application, we determine the capacity-achieving input covariance matrices for the MIMO-MACs and prove that the capacity-achieving input covariance matrices are asymptotically independent of the fading distribution.

A Novel Cognitive Radio Adaptation for Wireless Multicarrier Systems

This paper deals with the transmission parameter adaptation problem in a dynamic wireless channel environment for multicarrier-based cognitive radio systems. Given the environmental parameters returned by sensors, the cognitive radio will select a set of transmission parameters that can best respond to the new conditions. However, due to many possible values for the transmission parameters, the adaptation of radio parameters to generate optimum transmitted signals according to the changing environment and user needs is rather complex, especially for the multicarrier system with a large number of subcarriers. Inspired by the efficient ability of the cross-entropy (CE) method to find near-optimal solutions in huge search spaces, the application of the CE method to optimize cognitive radio parameters given a set of objectives is proposed. Computer simulation results show that the proposed CE method has significantly faster convergence than the conventional particle swarm optimization (PSO) method. Moreover, the parameters optimized by the proposed CE method have higher fitness values than those optimized by PSO.