Janghoon Yang

Characterization of photoreduced PtTiO2 and decomposition of dichloroacetic acid over photoreduced PtTiO2 catalysts

Abstract The effect of photoreduction on the properties of Pt TiO 2 catalysts was investigated. A decrease of the white-line area in the XANES spectra of Pt TiO 2 catalysts at the Pt LIII edge was observed with increasing photoreduction time, which denoted the photoreduction of Pt TiO 2 with UV illumination. From the EXAFS fitting results, the growth of Pt particles with the photoreduction time could be monitored. The XPS spectra of Pt TiO 2 catalysts at the Pt4f 5 2 and Pt4f 7 2 bands showed the presence of Pt0 and Pt2+ states after photoreduction and the fraction of metallic Pt0 was increased with increasing photoreduction time. The optimum photoreduction time for DCA (dichloroacetic acid) decomposition was 6 h. The partially reduced state of Pt seems to be effective to obtain a high quantum yield in DCA decomposition with minimizing the agglomeration of Pt clusters on the TiO2 surface.

Multi-cell uplink-downlink beamforming throughput duality based on Lagrangian duality with per-base station power constraints

Despite significant research efforts in beamforming, the maximum achievable downlink throughput with beamforming in a multi-cell environment is not available due to difficulty in finding optimal downlink beamforming. Thus, to reformulate the problem into a more solvable form, we derive dual uplink throughput optimization problem to multi-cell downlink beam- forming throughput maximization with per-base station (BS) power constraints based on Lagrangian duality. The optimal downlink beamforming is shown to be a minimum mean squared error (MMSE) beamforming in the dual uplink. It is also shown that the dual uplink problem achieves the same optimal throughput as the primal downlink problem.

Minimum MSE Design for Multiuser MIMO Relay

We study a joint optimal design of transceiver and relaying architecture in terms of mean square error (MSE) performance for multi-user multiple-input multiple-output (MIMO) relaying system. We show a duality relationship in the design of MSE-optimal transceiver and relaying architecture. Based on that, we devise an alternating algorithm, which converges and provides a suboptimal joint design of transceiver and relaying. By simulation, we show that the proposed joint design improves on the average MSE performance of the existing transceiver and relaying architectures.

Analysis of Pilot-Aided Channel Estimation with Optimal Leakage Suppression for OFDM Systems

This letter analyzes the effect of the leakage on the mean square error (MSE) performance of the pilot-aided channel estimator using discrete Fourier transform (DFT)-based interpolation in orthogonal frequency division multiplexing (OFDM) systems with virtual carriers. The optimal linear estimator for leakage suppression is derived to minimize the MSE. Numerical results show that the pilot-aided channel estimator with optimal leakage suppression improves the MSE performance significantly over the conventional one.

Complexity reduction for lattice reduction aided detection in MIMO-OFDM systems

A Lattice reduction (LR) aided receiver for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems can achieve near ML detector performance. However, Performing LR for each subcarrier can result in a large complexity. To deal with this problem, a low complexity scheme for lattice reduction (LR) aided detection in the MIMO OFDM system is presented. From exploiting frequency correlation among the neighboring channel matrix and the uni-modular transformation matrix of the preceding subcarrier, nearly LR reduced channel matrix is produced. Simulation results show that the proposed scheme can achieve the same performance with the conventional system while its complexity is significantly lower.

Sum MSE Minimization for Downlink Multi-Relay Multi-User MIMO Network

We propose methods of linear transceiver design for two different power constraints, sum relay power constraint and per relay power constraint, which determine signal processing matrices such as base station (BS) transmitter, relay precoders and user receivers to minimize sum mean square error (SMSE) for multi-relay multi-user (MRMU) networks. However, since the formulated problem is non-convex one which is hard to be solved, we suboptimally solve the problems by defining convex subproblems with some fixed variables. We adopt iterative sequential designs of which each iteration stage corresponds to each subproblem. Karush-Kuhn-Tucker (KKT) theorem and SMSE duality are employed as specific methods to solve subproblems. The numerical results verify that the proposed methods provide comparable performance to that of a full relay cooperation bound (FRCB) method while outperforming the simple amplify-and-forward (SAF) and minimum mean square error (MMSE) relaying in terms of not only SMSE, but also the sum rate.

Multimode precoder design for STBC with limited feedback in MIMO based wireless communication system

In this paper, a multimode precoder design for space-time block code (STBC) is investigated, which varies the number of streams depending on the channel condition. We develop a design criterion of minimizing the vector symbol error rate and derive an efficient offline algorithm to generate precoders. Simulation results show that the multimode precoded STBC (MM-STBC) provides substantial performance improvements compared with the single mode precoded orthogonal STBC (SM-STBC) for a fixed data-rate. It also outperforms the multimode precoded multiple-input multiple-output (MM-MIMO) system when feedback rate is very limited.

Multivariated Bayesian Compressive Sensing in Wireless Sensor Networks

Recently, compressive sensing has been studied in wireless sensor networks, which allows an aggregator to recover the desired sparse signal with fewer active sensor nodes. In this paper, we consider heterogeneous sensing environments, where the sensing quality varies due to the differences in the physical environment of each sensor node. We consider a Bayesian compressive sensing approach and propose two efficient algorithms that decrease the number of active sensor nodes while maintaining high performance. Both the selection algorithms aim to reduce the estimation error by minimizing the determinant of the error covariance matrix, which is proportional to the volume of the confidence ellipsoid. The first algorithm is the centralized greedy selection algorithm, which can achieve a nearly optimal solution in terms of the minimum confidence ellipsoid. It can also achieve almost the same level of performance as the combinatorial selection method, but has a lower complexity and outperforms the conventional convex relaxation method. The second algorithm is the decentralized selection algorithm, which is derived by approximating the determinant of the error covariance matrix. Unlike the centralized greedy algorithm, it can be done by each sensor node without heavy overhead or high complexity. Furthermore, we prove that the decentralized selection algorithm becomes equivalent to the centralized greedy algorithm as the number of sensor nodes increases. Our simulation results show that the centralized greedy selection algorithm provides the best performance while the decentralized algorithm performs nearly as well as the centralized algorithm as the number of sensor nodes increases.

Lattice Reduction Aided Detection with Reduced Complexity for Time-Correlated MIMO Channel

In this paper, lattice reduction (LR) aided detection with reduced complexity is developed for the detection of the multiple-input multiple-output (MIMO) systems in time- correlated channel. The proposed scheme computes the LLL-reduced channel matrix at the present time by using the present channel matrix multiplied with the unimodular transformation matrix of the previous symbol time as the initial value of LLL reduction algorithm. Simulation results show that the proposed scheme can achieve the same performance with significantly reduced complexity. Implementation of the proposed scheme with fixed number of iteration for limited complexity in practical system provides the almost same performance as conventional scheme.

Iterative Detection and ICI Cancellation for MISO-mode DVB-T2 System with Dual Carrier Frequency Offsets

In the DVB-T2 system with a multiple-input single-output (MISO) transmission mode, Alamouti coded orthogonal frequency division multiplexing (OFDM) signals are transmitted simultaneously from two spatially separated transmitters in a single frequency network (SFN). In such systems, each transmit-receive link may have a distinct carrier frequency offset (CFO) due to the Doppler shift and/or frequency mismatch between the local oscillators. Thus, the received signal experiences dual CFOs. This not only causes dual phase errors in desired data but also introduces inter-carrier interference (ICI), which cannot be removed completely by simply performing a CFO compensation. To overcome this problem, this paper proposes an iterative detection with dual phase errors compensation technique. In addition, we propose a successive-iterative ICI cancellation technique. This technique successively eliminates ICI in the initial iteration by exploiting pre-detected data pairs. Then, in subsequent iterations, it performs a fine interference cancellation using a priori information, iteratively fed back from the channel decoder. In contrast to previous works, the proposed techniques do not require estimates of dual CFOs. Their performances are evaluated via a full DVB-T2 simulator. Simulation results show that the DVB-T2 receiver equipped with the proposed dual phase errors compensation and the successive-iterative ICI cancellation techniques achieves almost the same performance as ideal dual CFOs-free systems, even for large dual CFOs.