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

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.673-694
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• 2021

In this paper, we intend to study the energy efficiency (EE) optimization for a simultaneous wireless information and power transfer (SWIPT)-based distributed antenna system (DAS). Firstly, a DAS-SWIPT model is formulated, whose goal is to maximize the EE of the system. Next, we propose an optimal resource allocation method by means of the Karush-Kuhn-Tucker condition as well as an ergodic method. Considering the complexity of the ergodic method, a suboptimal scheme with lower complexity is proposed by using an antenna selection scheme. Numerical results illustrate that our suboptimal method is able to achieve satisfactory performance of EE similar to an optimal one while reducing the calculation complexity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.87-92
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• 2015

In this Paper, we proposed optimization of system performance, optimal splitting factor ${\alpha}$ applied to power splitting protocol with relay protocol with decode and forward undergo co-channel interference. We can possible to optimize and maximize the channel capacity of the receive performance and the efficiency of the network through optimal factor of splitting protocol. We verified BER performance and Channel capacity and Outage probability for the proposed scheme over Rayleigh fading through Monte-Carlo simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.414-423
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• 2009

This paper considers a linear precoding scheme that achieves near optimal sum rate. While the minimum mean square error (MMSE) precoding provides the better MSE performance at all signal-to-noise ratio (SNR) than the zero forcing (ZF) precoding, its sum rate shows superior performance to ZF precoding at low SNR but inferior performance to ZF precoding at high SNR, From this observation, we first propose a near optimal linear precoding scheme in terms of sum rate. The resulting precoding scheme regularizes ZF precoding to maximize the sum rate, resulting in better sum rate performance than both ZF precoding and MMSE precoding at all SNR ranges. To find regularization parameters, we propose a simple algorithm such that locally maximal sum rate is achieved. As a low complexity alternative, we also propose a simple power re-allocation scheme in the conventional regularized channel inversion scheme. Finally, the proposed scheme is tested under the presence of channel estimation error. By simulation, we show that the proposed scheme can maintain the performance gain in the presence of channel estimation error and is robust to the channel estimation error.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.36-44
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• 2014

Exploiting the energy-delay tradeoff for energy saving is critical for developing green wireless communication systems. In this paper, we investigate the delay-constrained energy-efficient packet transmission. We aim to minimize the energy consumption of multiple randomly arrived packets in an additive white Gaussian noise channel subject to individual packet delay constraints, by taking into account the practical on-off circuit power consumption at the transmitter. First, we consider the offline case, by assuming that the full packet arrival information is known a priori at the transmitter, and formulate the energy minimization problem as a non-convex optimization problem. By exploiting the specific problem structure, we propose an efficient scheduling algorithm to obtain the globally optimal solution. It is shown that the optimal solution consists of two types of scheduling intervals, namely "selected-off" and "always-on" intervals, which correspond to bits-per-joule energy efficiency maximization and "lazy scheduling" rate allocation, respectively. Next, we consider the practical online case where only causal packet arrival information is available. Inspired by the optimal offline solution, we propose a new online scheme. It is shown by simulations that the proposed online scheme has a comparable performance with the optimal offline one and outperforms the design without considering on-off circuit power as well as the other heuristically designed online schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1393-1400
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• 2006

This paper optimizes the access probabilities (APs) in a network resource allocation scheme based on access probabilities in order that the waiting time and the blocking probability are minimized under the given constraints, and obtains its performance. In order to optimize APs, an infinite number of balance equations is reduced to a finite number of balance equations by applying Neuts matrix geometric method. And the nonlinear programming problem is converted into a linear programming problem. As a numerical example, the performance measures of waiting time and blocking probability for optimal access probabilities and the maximum utilization under the given constraints are obtained. And it is shown that the scheme with optimal APs gives more performance build-up than the strategy without optimization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4A
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• pp.360-370
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• 2010

In this paper, a distributed beamforming problem is considered in an amplify-and-forward (AF) wireless relay network consist of multiple source-destination pairs and relaying nodes. To exploit degree of freedom of the number of beamformers, in the first step, we proposed that the sources transmit their signals through orthogonal channels. During the second step, the relays transmit their received signals multiplied by complex weights to amplify and compensate for phase changes introduced by the backward channels through one common channel. The optimal beamforming vectors are obtained through minimization of the total relay transmit power while the signal-to-interference-plus-noise ratios (SINRs) at the destinations are above certain thresholds to meet a quality of services (QoSs) level. In the numerical example, it is shown that the proposed scheme needs less transmit power for moderate network data rates than other schemes, such as space division multiplexing or time-division multiplexing scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.564-582
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• 2015

This paper proposes a novel swarm intelligence optimization method which integrates bacterial foraging optimization (BFO) with quantum computing, called quantum bacterial foraging optimization (QBFO) algorithm. In QBFO, a multi-qubit which can represent a linear superposition of states in search space probabilistically is used to represent a bacterium, so that the quantum bacteria representation has a better characteristic of population diversity. A quantum rotation gate is designed to simulate the chemotactic step for the sake of driving the bacteria toward better solutions. Several tests are conducted based on benchmark functions including multi-peak function to evaluate optimization performance of the proposed algorithm. Numerical results show that the proposed QBFO has more powerful properties in terms of convergence rate, stability and the ability of searching for the global optimal solution than the original BFO and quantum genetic algorithm. Furthermore, we examine the employment of our proposed QBFO for cognitive radio spectrum allocation. The results indicate that the proposed QBFO based spectrum allocation scheme achieves high efficiency of spectrum usage and improves the transmission performance of secondary users, as compared to color sensitive graph coloring algorithm and quantum genetic algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.753-761
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• 2012

In this paper, we study a power allocation technique for Tomlinson-Harashima precoding (THP) in multi-user multiple input single output (MISO) downlink systems. In contrast to previous approaches, a mutual information based method is exploited for maximizing the sum rate of zero-forcing THP systems. Then, we propose a simple power allocation algorithm which assigns proper power level for modulo operated users. Simulation results show that the proposed scheme outperforms a conventional water-filling method, and it provides similar performance with near optimal method with much reduced complexity.

• Journal of Communications and Networks
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• v.18 no.1
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• pp.112-122
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• 2016

Cognitive radio (CR) is considered an attractive technology to deal with the spectrum scarcity problem. Multi-radio access technology (multi-RAT) can improve network capacity because data are transmitted by multiple RANs (radio access networks) concurrently. Thus, multi-RAT embedded in a cognitive radio network (CRN) is a promising paradigm for developing spectrum efficiency and network capacity in future wireless networks. In this study, we consider a new CRN model in which the primary user networks consist of heterogeneous primary users (PUs). Specifically, we focus on the energy-efficient resource allocation (EERA) problem for CR users with a special location coverage overlapping region in which heterogeneous PUs operate simultaneously via multi-RAT. We propose a two-tier crossover genetic algorithm-based search scheme to obtain an optimal solution in terms of the power and bandwidth. In addition, we introduce a radio environment map to manage the resource allocation and network synchronization. The simulation results show the proposed algorithm is stable and has faster convergence. Our proposal can significantly increase the energy efficiency.