• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.5
• /
• pp.1043-1050
• /
• 2018

In this paper, we propose a reinforce learning based on cooperative sensing scheme to select optimal secondary users(SUs) to enhance the detection performance of spectrum sensing in Cognitive radio(CR) networks. The SU with high accuracy is identified based on the similarity between the global sensing result obtained through cooperative sensing and the local sensing result of the SU. A fusion center(FC) uses similarity of SUs as reward value for Q-learning to determine SUs which participate in cooperative sensing with accurate sensing results. The experimental results show that the proposed method improves the detection performance compared to conventional cooperative sensing schemes.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.11
• /
• pp.2099-2106
• /
• 2006

In this paper, we addressed the node positioning method for minimizing the sensing energy consumption in wireless sensor networks with adjustable sensing ranges. It is necessary for minimizing the sensing energy consumption to minimize the overlapped sensing area by neighboring nodes. To find a optimal node position, we derived a optimal equations by using the overlapped areas, each node's radiuses and expended angles of opposite neighboring nodes. Based on it, we devised a new node positioning method, called as ASRC(Adjustable Sensing Ranges Control). Unlike existing condition based model, our proposed method was derived from mathematical formula, and we confirmed its validity of sensing energy consumption through simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.1
• /
• pp.58-73
• /
• 2014

As is well known, cooperative sensing can significantly improve the sensing accuracy as compared to local sensing in cognitive radio networks (CRNs). However, a large number of cooperative secondary users (SUs) reporting their local detection results to the fusion center (FC) would cause much overhead, such as sensing delay and energy consumption. In this paper, we propose a fast cooperative sensing scheme, called double threshold fusion (DTF), to reduce the sensing overhead while satisfying a given sensing accuracy requirement. In DTF, FC respectively compares the number of successfully received local decisions and that of failed receptions with two different thresholds to make a final decision in each reporting sub-slot during a sensing process, where cooperative SUs sequentially report their local decisions in a selective fashion to reduce the reporting overhead. By jointly considering sequential detection and selective reporting techniques in DTF, the overhead of cooperative sensing can be significantly reduced. Besides, we study the performance optimization problems with different objectives for DTF and develop three optimum fusion rules accordingly. Simulation results reveal that DTF shows evident performance gains over an existing scheme.

• Journal of Information Processing Systems
• /
• v.15 no.3
• /
• pp.604-615
• /
• 2019

Single-user spectrum sensing is susceptible to multipath effects, shadow effects, hidden terminals and other unfavorable factors, leading to misjudgment of perceived results. In order to increase the detection accuracy and reduce spectrum sensing cost, we propose an adaptive cooperative sensing strategy based on an estimated signal-to-noise ratio (SNR). Which can adaptive select different sensing strategy during the local sensing phase. When the estimated SNR is higher than the selection threshold, adaptive double threshold energy detector (ED) is implemented, otherwise cyclostationary feature detector is performed. Due to the fact that only a better sensing strategy is implemented in a period, the detection accuracy is improved under the condition of low SNR with low complexity. The local sensing node transmits the perceived results through the control channel to the fusion center (FC), and uses voting rule to make the hard decision. Thus the transmission bandwidth is effectively saved. Simulation results show that the proposed scheme can effectively improve the system detection probability, shorten the average sensing time, and has better robustness without largely increasing the costs of sensing system.

• Journal of Communications and Networks
• /
• v.14 no.2
• /
• pp.188-194
• /
• 2012

Cognitive networks (CNs) are capable of enabling dynamic spectrum allocation, and thus constitute a promising technology for future wireless communication. Whereas, the implementation of CN will lead to the requirement of an increased energy-arrival rate, which is a significant parameter in energy harvesting design of a cognitive user (CU) device. A well-designed spectrum-sensing scheme will lower the energy-arrival rate that is required and enable CNs to self-sustain, which will also help alleviate global warming. In this paper, spectrum sensing in a multi-user cognitive ad hoc network with a wide-band spectrum is considered. Based on the prospective spectrum sensing, we classify CN operation into two modes: Distributed and centralized. In a distributed network, each CU conducts spectrum sensing for its own data transmission, while in a centralized network, there is only one cognitive cluster header which performs spectrum sensing and broadcasts its sensing results to other CUs. Thus, a wide-band spectrum that is divided into multiple sub-channels can be sensed simultaneously in a distributed manner or sequentially in a centralized manner. We consider the energy consumption for spectrum sensing only of an analog-to-digital convertor (ADC). By formulating energy consumption for spectrum sensing in terms of the sub-channel sampling rate and whole-band sensing time, the sampling rate and whole-band sensing time that are optimal for minimizing the total energy consumption within sensing reliability constraints are obtained. A power dissipation model of an ADC, which plays an important role in formulating the energy efficiency problem, is presented. Using AD9051 as an ADC example, our numerical results show that the optimal sensing parameters will achieve a reduction in the energy-arrival rate of up to 97.7% and 50% in a distributed and a centralized network, respectively, when comparing the optimal and worst-case energy consumption for given system settings.

• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.565-565
• /
• 2004

• Journal of KIISE
• /
• v.43 no.12
• /
• pp.1412-1419
• /
• 2016

In cognitive radio sensor networks (CRSNs), secondary users (SUs) can occupy licensed bands opportunistically without causing interferences to primary users (PUs). SUs perform spectrum sensing to detect the presence of PUs. Sensing time is a critical parameter for spectrum sensing that can yield a tradeoff between sensing performance and secondary throughput. In this study, we investigate new approaches for spectrum sensing by exploring the tradeoff from a) spectrum sensing for PU detection (SSPD) and b) spectrum sensing for secondary throughput (SSST). In the proposed scheme, the first sensing result of the current frame determines the dynamic performance of the second spectrum sensing. Energy constraint in CRSNs leads to maximized network energy efficiency via optimization of sensing time. Simulation results show that the proposed scheme of SSPD and SSST improves network performance in terms of energy efficiency and secondary throughput, respectively.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.48 no.3
• /
• pp.50-59
• /
• 2011

In this paper, we present a spectrum sensing method by exploiting the relationship of previous and current detected data sets in frequency domain. Most of the traditional spectrum sensing methods only consider the current detected data sets of Primary User (PU). Previous state of PU is a kind of conditional probability that strengthens the reliability of the detector. By considering the relationship of the previous and current spectrum sensing, cross entropy-based spectrum sensing is proposed to detect PU signal more effectively, which has a strengthened performance and is robust. When previous detected signal is noise, the discriminating ability of cross entropy-based spectrum sensing is no better than conventional entropy-based spectrum sensing. To address this problem, we propose an improved cross entropy-based frequency-domain spectrum sensing. Regarding the spectrum sensing scheme, we have derived that the proposed method is superior to the cross entropy-based spectrum sensing. We proceed a comparison of the proposed method with the up-to-date entropy-based spectrum sensing in frequency-domain. The simulation results demonstrate the performance improvement of the proposed spectrum sensing method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.2
• /
• pp.253-263
• /
• 2015

Lately, spectrum sensing performance has been improved by using cooperate spectrum sensing which each results of sensing of several secondary users are reported to the fusion center. Using Cognitive Radio, secondary user is able to share a bandwidth allocated to primary user. In this paper, we propose a new decentralized cooperative spectrum sensing scheme which compensates the performance degradation of existing decentralized cooperative spectrum sensing considering the error probability of the channel which sensed result of the secondary user is delivered to the fusion center in decentralized cooperative spectrum sensing. In addition, a sensing threshold optimization of minimizing the error probability of decentralized cooperative spectrum sensing is introduced by deriving the equation and the optimal sensing threshold has been confirmed to maximize the decentralized cooperative spectrum sensing performance.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1397-1399
• /
• 2003

When and Where, What kind of agricultural products will be produced and provided for the market? It is a commercial requirement, and also an academic questions to remote sensing technology. Crop physiology analysis and growth monitoring are important elements for precision agriculture management. Remote sensing technology supplies us more selections and available spaces in this dynamic change study by producing images of different spatial, spectral and temporal resolutions. Especially, the hyperspectral remote sensing should do play a key role in crop growth investigation at national, regional and global scales. In the past five years, Chinese academy of sciences and Japan NTT-DATA have made great efforts to establish a prototype information service system to dynamically survey the vegetable planting situation in Nagano area of Japan mainly based on remote sensing data. For such concern, a flexible and light-duty flight system and some practical data processing system and some necessary background information should be rationally made together. In addition, some studies are also important, such as quick pre-processing for hyperspectral data, Multi-temporal vegetation index analysis, hyperspectral image classification in support of GIS data, etc. In this paper, several spectral data analysis models and a designed airborne platform are provided and discussed here.