• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.219-221
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• 2012

The concave utility in the Network Utility Maximization (NUM) problem is only suitable for elastic flows. In networks with multiclass traffic, the utility can be concave, linear, step or sigmoidal. Hence, the basic NUM becomes a nonconvex optimization problem. The current approach utilizes the standard dual-based decomposition method. It does not converge in case of scarce resource. In this paper, we propose an algorithm that always converges to a local optimal solution to the nonconvex NUM after solving a series of convex approximation problems. Our techniques can be applied to any log-concave utilities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10A
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• pp.1023-1033
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• 2007

This study considers a joint congestion control, routing and power control for energy-constrained wireless networks. A mathematical model is introduced which includes maximization of network utility, maximization of network lifetime, and trade-off between network utility and network lifetime. The framework would maximize the overall throughput of the network where the overall throughput depends on the data flow rates which in turn is dependent on the link capacities. The link capacity on the other hand is a function of transmit power levels and link Signal-to-Interference-plus-Noise-Ratio (SINR) which makes the power allocation problem inherently difficult to solve. Using dual decomposition techniques, subgradient method, and logarithmic transformations, a joint algorithm for rate and power allocation problems was formulated. Numerical examples for each optimization problem were also provided.

• ETRI Journal
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• v.36 no.6
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• pp.960-967
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• 2014

In this paper, an analytical framework is proposed for the optimization of network performance through joint congestion control, channel allocation, rate allocation, power control, scheduling, and routing with the consideration of fairness in multi-channel wireless multihop networks. More specifically, the framework models the network by a generalized network utility maximization (NUM) problem under an elastic link data rate and power constraints. Using the dual decomposition technique, the NUM problem is decomposed into four subproblems - flow control; next-hop routing; rate allocation and scheduling; power control; and channel allocation - and finally solved by a low-complexity distributed method. Simulation results show that the proposed distributed algorithm significantly improves the network throughput and energy efficiency compared with previous algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.59-68
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• 2009

This paper addresses a hybrid multi-focus image fusion scheme using the recent curvelet transform constructions. Hybridization is obtained by combining the MS fusion rule with a novel "copy" method. The proposed scheme use MS rule to fuse the m most significant terms in spectrum of an image at each decomposition level. The scheme is dubbed in this work as m-term fusion in adherence to its use of the MSC (most significant coefficients) in the transform set at any given scale, orientation, and translation. We applied the edge-sensitive objective quality measure proposed by Xydeas and Petrovic to evaluate the method. Experimental results show that the proposed scheme is a potential alternative to the redundant, shift-invariant Dual-Tree Complex Wavelet transforms. In particular, it was confirmed that a 50% m-term fusion produces outputs with no visible quality degradation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1517-1524
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• 2022

In this paper, we consider a heterogeneous network (HetNet) consisting of one macro base station and multiple small base stations, and assume the coordinated multi-point transmission between the base stations. In addition, we assume that the channel between the base station and the user consists of path loss and Rayleigh fading. Under these assumptions, we present the energy efficiency (EE) achievable by the user for a given base station and we formulate an optimization problem of dynamic cell selection and transmit power allocation to maximize the total EE of the HetNet. In this paper, we propose an unsupervised deep learning method to solve the optimization problem. The proposed deep learning-based scheme can provide high EE while having low complexity compared to the conventional iterative convergence methods. Through the simulation, we show that the proposed dynamic cell selection scheme provides higher EE performance than the maximum signal-to-interference-plus-noise ratio scheme and the Lagrangian dual decomposition scheme, and the proposed transmit power allocation scheme provides the similar performance to the trust region interior point method which can achieve the maximum EE.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5212-5230
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• 2016

How to determine the right number of small base stations to activate in multi-cell uplinks to match traffic from a fixed quantity of K users is an open question. This paper analyses the uplink cooperative that jointly receives base stations activation to explore this question. This paper is different from existing works only consider transmitting power as optimization objective function. The global objective function is formulated as a summation of two terms: transmitting power for data and coordinated overhead for control. Then, the joint base stations activation and beamforming problem is formulated as a mixed integer second order cone optimization. To solve this problem, we develop two polynomial-time distributed methods. Method one is a two-stage solution which activates no more than K small base stations (SBSs). Method two is a heuristic algorithm by dual decomposition to MI-SOCP that activates more SBSs to obtain multiple-antennae diversity gains. Thanks to the parallel computation for each node, our methods are more computationally efficient. The strengths and weaknesses of these two proposed two algorithms are also compared using numerical results.

• Journal of Communications and Networks
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• v.13 no.4
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• pp.360-366
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• 2011

In this paper, we investigate the problem of power allocation in a heterogeneous network that is composed of a pair of cognitive users (CUs) and an infrastructure-based primary network. Since CUs have only limited effective spectrum-sensing ability and primary users (PUs) are not active all the time in all locations and licensed bands, we set up a new multi-area model to characterize the heterogeneous network. A novel combined interference-avoidance policy corresponding to different PU-appearance situations is introduced to protect the primary network from unacceptable disturbance and to increase the spectrum secondary-reuse efficiency. We use dual decomposition to transform the original power allocation problem into a two-layer optimization problem. We propose a low-complexity joint power-optimizing method to maximize the transmission rate between CUs, taking into account both the individual power-transmission constraints and the combined interference power constraint of the PUs. Numerical results show that for various values of the system parameters, the proposed joint optimization method with combined PU protection is significantly better than the opportunistic spectrum access mode and other heuristic approaches.

• ETRI Journal
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• v.37 no.3
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• pp.471-479
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• 2015

Traditional designs of cognitive radio (CR) focus on maximizing system throughput. In this paper, we study the joint overlay and underlay power allocation problem for orthogonal frequency-division multiple access-based CR. Instead of maximizing system throughput, we aim to maximize system energy efficiency (EE), measured by a "bit per Joule" metric, while maintaining the minimal rate requirement of a given CR system, under the total power constraint of a secondary user and interference constraints of primary users. The formulated energy-efficient power allocation (EEPA) problem is nonconvex; to make it solvable, we first transform the original problem into a convex optimization problem via fractional programming, and then the Lagrange dual decomposition method is used to solve the equivalent convex optimization problem. Finally, an optimal EEPA allocation scheme is proposed. Numerical results show that the proposed method can achieve better EE performance.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.3
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• pp.87-92
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• 2006

The rocket grade hydrogen peroxide has been widely used as a monopropellant in propulsion systems. In the present paper, we described an experimental study of a catalytic reactor that employs two stage catalyst beds to enhance the low temperature performance of the reactor inlet. $K_2MnO_4$ was chosen as the catalyst for the initial stage of the reactor bed for its superior behavior in the low temperature regime. Alumina sol-gel method was successfully applied for coating $K_2MnO_4$ on a reactor bed of cordierite monolith. LSC was used for the catalyst of the second stage of the reactor. The reactor with combined catalyst beds was built and tested to exhibit superior performance in low temperature regime and high decomposition efficiency.

• Journal of Satellite, Information and Communications
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• v.11 no.2
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• pp.55-59
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• 2016

Conventional RF energy harvesting systems can harvest energy and decode information from same source as an Hybirid-AP (H-AP). However, harvesting efficiency is seriously dependent on distance between users and H-AP. Therefore, in this paper, we proposed a transmission model for RF harvesting consisting of information and power source separately called Decoupled RF Energy harvesting networks. Main purpose of this paper is to maximize energy efficiency under various constraints of transmit power from H-AP and power beacon (PB), minimum quality of service and quality of harvested power of each users. To measure proposed model's performance, we proposed optimal time scheduling algorithms for energy efficiency (EE) maximization using Lagrangian dual decomposition theory that locally maximizes the EE by obtaining suboptimal values of three arguments : transmit power of H-AP, transmit power of PB, frame splitting factor. Experiment results show that the proposed energy-efficient algorithms converge within a few iterations with its optimality and greatly improve the EE compared to that of baseline schemes.