Pang-An Ting

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.

Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO With Low-Precision ADCs

This paper considers a multiple-input multiple-output (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be relatively long to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design.

Interference management of femtocell in macro-cellular networks

This paper investigates the impact of femtocell interference on existing macrocells with fractional frequency reuse (FFR). Instead of using complicated transmission techniques by coordinating multiple base stations (BSs), this paper adaptively configures FFR pattern to avoids interference caused by femtocells, depending on density and locations of femtocells. Simulation results show that the proposed femtocell BS deployment can effectively reduce downlink interference to macro-cellular networks.

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.

BER analysis of the optimum multiuser detection with channel mismatch in MC-CDMA systems

In this paper, we analyze the bit-error-rate (BER) performance of the optimum multiuser detection (MUD) with channel mismatch in multicarrier code-division-multiple-access (MC-CDMA) systems. The BER performance of the optimum MUD without channel mismatch in MC-CDMA systems has been recently derived using the replica method. However, it is left unjustified, since the replica method is not a rigorous approach. In addition, it is NP-hard to implement an optimum MUD algorithm. To justify the BER performance and to make the optimum MUD feasible, based on Pearls belief propagation (BP) scheme, we put together a low-complexity iterative MUD algorithm for MC-CDMA systems. Furthermore, channel mismatch is introduced into the BP-based MUD algorithm to make the scenario general. With channel mismatch, the analytical results of the BP-based MUD algorithm conform perfectly to, and the simulation results of the BP-based MUD algorithm conform very closely to the BER performance of the optimum MUD derived using the replica method, which is a nontrivial extension of the existing replica approach mentioned above. Without channel mismatch, the problem becomes a special case of our contribution.

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.

A Novel Message Passing Based MIMO-OFDM Data Detector with a Progressive Parallel ICI Canceller

A joint design of message passing MIMO data detector/decoder with progressive parallel inter-carrier interference canceller (PPIC) based on factor graph for OFDM-based wireless communication systems is proposed. By exchanging messages both in space domain and frequency domain, the proposed algorithm can suppress inter-antenna interferences and cancel inter-carrier interferences iteratively and progressively. With a proper designed message passing schedule and random interleaver, the short cycle problem is solved. Computer simulations show that the performance of the proposed message passing MIMO detector outperforms MMSE-SIC MIMO detector. The performances of PPIC, both in perfect channel estimation and imperfect channel estimation cases, are compared with the standard PIC architecture and the ICI self-canceller. The proposed PPIC is superior to PIC both in computational complexity and system architecture. The parallel structure of PPIC is similar to a systolic array. The proposed algorithm potentially leads to a very-high-speed detector/decoder. It is very suitable for VLSI implementation and it is a potential candidate for data detection/decoding in future high data rate, high mobility, wireless MIMO-OFDM communication systems.

Iterative Interference Cancellation for STBC-OFDM Systems in Fast Fading Channels

The performance of a space-time block coded orthogonal frequency-division multiplexing (STBC-OFDM) system often relies on the assumption of quasi-static channels. For the time-varying multipath channel, co-channel interference (CCI) and inter-carrier interference (ICI) occur and performance degrades seriously. In this paper, we propose an iterative interference cancellation scheme to reduce both CCI and ICI jointly. In particular, a list successive interference cancellation (List-SIC) algorithm is presented to obtain a candidate list to obtain soft information used in the CCI cancellation. Furthermore, an ICI cancellation algorithm that uses prior List-SIC information from the preceding iteration is proposed. The simulation results show that the proposed scheme achieves near maximum likelihood (ML) error performance within only two or three iterations in fast fading channels.

Message Passing Algorithm for Distributed Downlink Regularized Zero-Forcing Beamforming with Cooperative Base Stations

Base station (BS) cooperation can turn unwanted interference to useful signal energy for enhancing system performance. In the cooperative downlink, zero-forcing beamforming (ZFBF) with a simple scheduler is well known to obtain nearly the performance of the capacity-achieving dirty-paper coding. However, the centralized ZFBF approach is prohibitively complex as the network size grows. In this paper, we devise message passing algorithms for realizing the regularized ZFBF (RZFBF) in a distributed manner using belief propagation. In the proposed methods, the overall computational cost is decomposed into many smaller computation tasks carried out by groups of neighboring BSs and communication is only required between neighboring BSs. More importantly, some exchanged messages can be computed based on channel statistics rather than instantaneous channel state information, leading to significant reduction in computational complexity. Simulation results demonstrate that the proposed algorithms converge quickly to the exact RZFBF and much faster compared to conventional methods.