• 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.11 no.3
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• pp.1425-1440
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• 2017

In wireless communications, antenna selection (AS) is a widely used method for reducing comparable cost of multiple RF chains in MIMO systems. As is well known, most of literatures on combining AS with MIMO techniques concern linear modulations such as phase shift keying (PSK) and quadrature amplitude modulation (QAM). The combination of CPM and MIMO has been considered an optimal choice that can improve its capacity without loss of power and spectrum efficiency. The aim of this paper is to investigate joint transmit and receive antenna selection (JTRAS) in CPM MIMO systems. Specifically, modified incremental and decremental JTRAS algorithms are proposed to adapt to arbitrary number of selected transmit or receive antennas. The computational complexity of several JTRAS algorithms is analyzed from the perspective of channel capacity. As a comparison, the performances of bit error rate (BER) and spectral efficiency are evaluated via simulations. Moreover, computational complexity of the JTRAS algorithms is simulated in the end. It is inferred from discussions that both incremental JTRAS and decremental JTRAS perform close to optimal JTRAS in BER and spectral efficiency. In the sense of practical scenarios, adaptive JTRAS can be employed to well tradeoff performance and computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2047-2066
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• 2016

In this paper, we investigate joint power allocation and relay selection (PARS) schemes in non-orthogonal cooperative protocol (NOCP) based cognitive relay networks. Generally, NOCP outperforms the orthogonal cooperative protocol (OCP), since it can provide more transmit diversity. However, most existing PARS schemes in cognitive relay networks focus on OCP, which are not suitable for NOCP. In the context of NOCP, we first derive the joint constraints of transmit power limit for secondary user (SU) and interference constraint for primary user (PU). Then we formulate optimization problems under the aforementioned constraints to maximize the capacity of SU in amplify-and-forward (AF) and decode-and-forward (DF) modes, respectively. Correspondingly, we derive the closed form solutions with respect to different parameters. Numerical results are provided to verify the performance improvement of the proposed PARS schemes.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.96-101
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• 2000

Nowadays, everyone emphasis the important of environment. it is found that try to apply using the naturally favorable method when arrange irrigation and drainage channel. But we have no accurate standard of region selection yet. so it is make a many problem. The purpose of this study is the making of standard which is optimal region selection for the creation of the naturally favorable waterfront area. We surveyed data of twenty site in korea where are managed by the KARICO(Korea Agricultural Rural Infrastructure Corporation). We analysed the data using suitable three method(simple adding point method, subjectivity decision method, checklist method) for purpose of this study.

• ETRI Journal
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• v.39 no.5
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• pp.729-736
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• 2017

This work investigates secure cluster-aided multi-hop randomize-and-forward networks. We present a hop-by-hop multi-hop transmission scheme with relay selection, which evaluates for each cluster the relays that can securely receive the message. We propose an analytical model to derive the secure connectivity probability (SCP) of the hop-by-hop transmission scheme. For comparison, we also analyze SCPs of traditional end-to-end transmission schemes with two relay-selection policies. We perform simulations, and our analytical results verify that the proposed hop-by-hop scheme is superior to end-to-end schemes, especially with a large number of hops or high eavesdropper channel quality. Numerical results also show that the proposed hop-by-hop scheme achieves near-optimal performance in terms of the SCP.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6A
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• pp.494-499
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• 2009

In this paper, we provide an efficient method of user selection for achieving the maximum system throughput in downlink multiuser Multiple-Input Multiple-Output (MIMO) systems. A proposed method is for selecting a fine user set only with powers of each user channel and angles between them. This algorithm is simpler than SUS because there is no considering about the optimal value of correlation. The proposed method finds the user set toward maximizing system throughput, so it has high performance.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.12
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• pp.20-25
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• 2008

Codebook-based opportunistic beamforming (COBF) technique provides a beam selection diversity to the conventional opportunistic beamforming. In this paper, we design the random matrix and codebook for the COBF technique based on a kappa-factor channel model. Applying the proposed design to the COBF, nearly optimal beams are generated. Therefore, the COBF shows an outstanding performance without regard to the channel correlation related to the kappa-factor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.140-142
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• 2013

Cognitive radio (CR) has been recently proposed to dynamically access unused-spectrum. Since the spectrum availability for opportunistic access is determined by spectrum sensing, sensing control is identified as one of the most crucial issues of cognitive radio networks. Out-of-band sensing to find an available channels to sense. Sensing is also required in case of spectrum hand-off. Sensing process needs to be done very fast in order to enhance the quality of service (QoS) of the CR nodes, and transmission not to be cut for longer time. During the sensing, the PU(primary user) detection probability condition should be satisfied. We adopt a channel prediction method to find target channels. Proposed prediction method combines chaotic global method and chaotic local method for channel idle probability prediction. Global method focus on channel history information length and order number of prediction model. Local method focus on local prediction trend. Through making simulation, Proposed method can find an available channel with very high probability, total sensing time is minimized, detection probability of PU's are satisfied.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.4
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• pp.442-450
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• 2008

In this paper, the phase diverisity in a direct-conversion receiver for a UHF RFID reader is analyzed and the optimal I/Q signal combining methods is presented with respect to tag modulation. At first, fading characteristics of a single channel receiver is shown to prove the importance of phase diversity due to the phase relationship between the backscattered signal and the local oscillator. And the optimal signal combining methods are presented in order to overcome the signal power reduction due to phase diversity. In case of ASK, the power combining method is presented for the optimal I/Q combining. And the arctangent and principal component combining methods using covariance matrix of I and Q channels are presented for the optimal I/Q combining in case of PSK. In order to analyze the performance of suggested methods, the selection diversity and the optimal combining methods are compared. According to analysis and simulation results, the optimal combining methods have a maximum 3 dB SNR enhancement than selection diversity.