• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.11
• /
• pp.3204-3217
• /
• 2023

In this paper, the downlink of the multicast based spatial modulation systems is investigated. Specifically, physical layer multicasting is introduced to increase the number of access users and to improve the communication rate of the spatial modulation system in which only single radio frequency chain is activated in each transmission. To minimize the bit error rate (BER) of the multicast based spatial modulation system, a joint optimizing algorithm of antenna selection and multicast precoding is proposed. Firstly, the joint optimization is transformed into a mixed-integer non-linear program based on single-stage reformulation. Then, a novel iterative algorithm based on the idea of branch and bound is proposed to obtain the quasioptimal solution. Furthermore, in order to balance the performance and time complexity, a low-complexity deflation algorithm based on the successive convex approximation is proposed which can obtain a sub-optimal solution. Finally, numerical results are showed that the convergence of our proposed iterative algorithm is between 10 and 15 iterations and the signal-to-noise-ratio (SNR) of the iterative algorithm is 1-2dB lower than the exhaustive search based algorithm under the same BER accuracy conditions.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.35 no.3
• /
• pp.259-266
• /
• 2011

We present a new dual sequential approximate optimization (SAO) algorithm called SD-TDQAO (sequential dual two-point diagonal quadratic approximate optimization). This algorithm solves engineering optimization problems with a nonlinear objective and nonlinear inequality constraints. The two-point diagonal quadratic approximation (TDQA) was originally non-convex and inseparable quadratic approximation in the primal design variable space. To use the dual method, SD-TDQAO uses diagonal quadratic explicit separable approximation; this can easily ensure convexity and separability. An important feature is that the second-derivative terms of the quadratic approximation are approximated by TDQA, which uses only information on the function and the derivative values at two consecutive iteration points. The algorithm will be illustrated using mathematical and topological test problems, and its performance will be compared with that of the MMA algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.3
• /
• pp.1325-1344
• /
• 2019

Recently, to satisfy mobile users' increasing data transmission requirement, energy efficiency (EE) resource allocation in distributed antenna systems (DASs) has become a hot topic. In this paper, we aim to maximize EE in DASs subject to constraints of the minimum data rate requirement and the maximum transmission power of distributed antenna units (DAUs) with different density distributions. Virtual cell is defined as DAUs selected by the same user equipment (UE) and the size of virtual cells is dependent on the number of subcarriers and the transmission power. Specifically, the selection rule of DAUs is depended on different scenarios. We develop two scenarios based on the density of DAUs, namely, the sparse scenario and the dense scenario. In the sparse scenario, each DAU can only be selected by one UE to avoid co-channel interference. In order to make the original non-convex optimization problem tractable, we transform it into an equivalent fractional programming and solve by the following two sub-problems: optimal subcarrier allocation to find suitable DAUs; optimal power allocation for each subcarrier. Moreover, in the dense scenario, we consider UEs could access the same channel and generate co-channel interference. The optimization problem could be transformed into a convex form based on interference upper bound and fractional programming. In addition, an energy-efficient DAU selection scheme based on the large scale fading is developed to maximize EE. Finally, simulation results demonstrate the effectiveness of the proposed algorithm for both sparse and dense scenarios.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.6
• /
• pp.1229-1244
• /
• 2017

In recent years, as demand for data-based analytical methodologies increases in various fields, optimization methods have been developed to handle them. In particular, various constraints required for problems in statistics and machine learning can be solved by convex optimization. Alternating direction method of multipliers (ADMM) can effectively deal with linear constraints, and it can be effectively used as a parallel optimization algorithm. ADMM is an approximation algorithm that solves complex original problems by dividing and combining the partial problems that are easier to optimize than original problems. It is useful for optimizing non-smooth or composite objective functions. It is widely used in statistical and machine learning because it can systematically construct algorithms based on dual theory and proximal operator. In this paper, we will examine applications of ADMM algorithm in various fields related to statistics, and focus on two major points: (1) splitting strategy of objective function, and (2) role of the proximal operator in explaining the Lagrangian method and its dual problem. In this case, we introduce methodologies that utilize regularization. Simulation results are presented to demonstrate effectiveness of the lasso.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.11
• /
• pp.5209-5228
• /
• 2016

Device-to-device (D2D) communication underlaying cellular networks can bring significate benefits for improving the performance of mobile services. However, it hinges on elaborate resource sharing scheme to coordinate interference between cellular users and D2D pairs. We formulate a joint mode selection, link allocation and power control optimization problem for D2D communication sharing uplink resources in a multi-user cellular network and consider the efficiency and the fairness simultaneously. Due to the non-convex difficulty, we propose a three-step scheme: firstly, we conduct mode selection for D2D pairs based on a minimum distance metric after an admission control and obtain some cellular candidates for them. And then, a cellular candidate will be paired to each D2D pair based on fairness. Finally, we use Lagrangian Algorithm to formulate a joint power control strategy for D2D pairs and their reused cellular users and a closed-form of solution is derived. Simulation results demonstrate that our proposed algorithms converge in a short time. Moreover, both the sum rate of D2D pairs and the energy efficiency of cellular users are improved.

• Journal of Communications and Networks
• /
• v.18 no.3
• /
• pp.420-428
• /
• 2016

In this paper, we investigate the scheduling and power allocation for coordinated multi-point transmission in downlink long term evolution advanced (LTE-A) systems, where orthogonal frequency division multiple-access is used. The proposed scheme jointly optimizes user selection, power allocation, and modulation and coding scheme (MCS) selection to maximize the weighted sum throughput with fairness consideration. Considering practical constraints in LTE-A systems, the MCSs for the resource blocks assigned to the same user need to be the same. Since the optimization problem is a combinatorial and non-convex one with high complexity, a low-complexity algorithm is proposed by separating the user selection and power allocation into two subproblems. To further simplify the optimization problem for power allocation, the instantaneous signal-to-interference-plus-noise ratio (SINR) and the average SINR are adopted to allocate power in a single cell and multiple coordinated cells, respectively. Simulation results show that the proposed scheme can improve the average system throughput and the cell-edge user throughput significantly compared with the existing schemes with limited feedback.

• Journal of Korean Society of Transportation
• /
• v.16 no.1
• /
• pp.151-164
• /
• 1998

본 논문은 통행배정과 교통신호제어기의 결합문제를 풀기 위한 새로운 해법의 제시를 목적으로 한다. 통행배정과 신호제어 결합모형은 네트웍 디자인 문제(Network Design Problem)로 비선형 비분리 목적함수(Nonlinear and Nonseparable Objective Function)와 비선형제약 및 비컴백스 집합(Nonlinear and Non-Convex Set)형태로 인해 다수의 국지해(Multiple Local Optima)를 갖는 특징이 있다. 따라서 이렇게 복잡하고 난해한 문제를 푸는 해법은 많은 국지해중에 가장 최소한 값(Global Optima)을 찾을수 있는 방법을 제공하여야한다. 전체최적해(Global Optima)를 찾 을 수 있는 기존의 방법들은 확률적최적화방법(Stochastic Optimization Methods)에 속한다. 본연구에서는 이러한 방법중 금속공학에서 발 견된 모의담금빌법(Simulated Annealing Method)에 근거한 해법을 제시한다. 이방법이 통행배정과 신호제어 결합문제에 적용되는지 검토하기 위해 이해법의 수렴성(Convergence)을 증명했으며 또한 실제 프로그램된 모형을 작은 고안된 네트워크에 적 용했다. 마지막으로는 개발된 해법의 실용성을 실험하기 위해 두 가지의 보다 큰 도로망에 적용 및 분석을 했다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.5
• /
• pp.423-431
• /
• 2000

In this paper, the effect of computing time-delay in the real-time digital fuzzy control systems is investigated and the design methodology of a real-time digital fuzzy controller(DFC) to overcome the problems caused by it is presented. We propose the fuzzy feedback controller whose output is delayed with unit sampling period. The analysis and the design problem considering computing time-delay is very easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy control system is solved by the linear matrix inequality(LMI) theory. Convex optimization techniques are utilized to find the stable feedback gains and a common positive definite matrix P for the designed fuzzy control system Furthermore, we develop a real-time fuzzy control system for backing up a computer-simulated truck-trailer with the consideration of the computing time-delay. By using the proposed method, we design a DFC which guarantees the stability of the real time digital fuzzy control system in the presence of computing time-delay.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10a
• /
• pp.697-699
• /
• 2004

Despite many successful spectral clustering algorithm (based on the spectral decomposition of Laplacian(1) or stochastic matrix(2) ) there are several unsolved problems. Most spectral clustering Problems are based on the normalized of algorithm(3) . are close to the classical graph paritioning problem which is NP-hard problem. To get good solution in polynomial time. it needs to establish its convex form by using relaxation. In this paper, we apply a novel optimization technique. semidefinite programming(SDP). to the unsupervised clustering Problem. and present a new multiple Partitioning method. Experimental results confirm that the Proposed method improves the clustering performance. especially in the Problem of being mixed with non-compact clusters compared to the previous multiple spectral clustering methods.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.23 no.1
• /
• pp.96-100
• /
• 2012

In high static field magnetic resonance imaging(MRI) systems, $B_0$ fields of 7 T and 9.4 T, the impressed RF field shows larger inhomogeneity than in clinical MRI systems with B0 fields of 1.5 T and 3.0 T. In multi-channel RF coils, the magnitude and phase of the input to each coil element can be controlled independently to reduce the non-uniformity of the impressed RF $B_1^+$ field. The convex optimization technique has been used to obtain the optimum excitation parameters with iterative solutions for homogeneity in a selected ROI(Region of Interest). To demonstrate the technique, the multichannel transmission line coil was modeled together with a human head phantom at 400 MHz for the 9.4 T MRI system and $B_1^+$ fields are obtained. In this paper, all the optimized $B_1^+$ in each isolated ROIs are combined to achieve significantly improved homogeneity over the entire field of view. The simulation results for 9.4 T MRI systems are discussed in detail.