Earticle

CR(Cognitive Radio) technology is maximized frequency resource efficiency using unused frequency band. Conventional cooperative spectrum sensing system has threshold based on fixed false alarms. We proposed new system model. The system model is analyzed by setting the false alarm through CSI(channel state information) to get resonable threshold. Simulation results show improvement of detection probability.

In this paper, we propose spectrum sensing using cooperative relay to solve problem of sensing performance degradation due to CPE (Customer-Primise equipments) which causes low SNR (signal-to-noise ratio) problem. In cooperative communication systems, AF (amplify-and-forward) and DF (decoded-and-forward) is widely used for relay mechanism. Also, it is expected that a cooperative relay scheme guarantees a high sensing performance by its diversity gain. Based on these backgrounds, we applied a cooperative relay scheme to CR (cognitive radio) system, and simulation results show a sensing performance comparison between AF and DF.

스펙트럼 센싱은 인지 라디오에서 가장 핵심이 되는 기술이다. 이미 여러 논문에서 에너지 검출기를 바탕으로 하는 스펙트럼 센싱 기법들에 대해 연구하였지만, 실제 시스템에서는 노이즈의 분산을 정확히 추정 하는 것이 어려우므로 에너지 검출기를 쓰면 시스템이 요구하는 오경보확률을 유지할 수 없는 문제가 생긴다. 이에 본 논문에서는, 인지 라디오 가 예측 가능하지만 알지 못하는 주사용자의 신호를 검출해야 할 때 노이즈의 분산을 몰라도 스펙트럼을 검출할 수 있는 새로운 검출기를 제안한다. 시뮬레이션을 통해 제안한 검출기는 노이즈의 분산을 몰라도 스펙트럼을 검출할 수 있으며 노이즈 분산의 변화에 강인한 특성을 지닌다는 것을 보인다.

Spectrum sensing is one of the most important technologies in cognitive radio. Although many studies have considered energy detection technique as the spectrum sensing technique, noise variance in practical systems is difficult to estimate accurately. Thus, in the real system, the probability of false alarm will not be maintained constant. In this paper, with considering that the cognitive radio does not know the primary user's signal, we propose a new spectrum sensing scheme which can operate without the information of noise variance. Through simulations, we show that the proposed scheme can detect spectrum with the condition of unknown noise information and have robustness for the change of noise variance.

군 통신을 중심으로 적의 탐지를 최소화하기 위해 저피탐(low probability of detection, LPD) 통신 시스템의 개발이 관심을 받고 있으며, 다양한 환경에서 높은 탐지 확률을 보장하는 신호 탐지 기술에 대한 필요성이 대두되고 있다. 따라서 본 논문은 스펙트럼 센싱을 이용하여 신호 존재 여부를 판단하는 통신 신호 탐지 기법을 제안한다. 수집 신호는 고속 샘플링된 후 fast fourier transform (FFT)를 통해 주파수 스펙트럼으로 변환된다. 변환된 스펙트럼은 시간 축으로 누적되어 2차원 스펙트로그램을 형성하며, 이를 딥러닝 모델의 입력으로 사용한다. 일차적으로 합성곱 신경망(convolutional neural network, CNN)을 이용해 신호 존재 여부를 판단하는 기법을 제안한다. 컴퓨터 모의실험 결과, 제안하는 CNN 기법은 기존 임계값 기반 기법 대비 약 2dB의 성능 향상을 보인다. 특히, signal to noise ratio (SNR) = –6dB 환경에서 약 35% 높은 탐지 정확도를 보인다. 또한, you only look once (YOLO)를 활용하여 신호의 시간-주파수 위치를 인식하는 기법을 제안한다. 실험 결과, YOLO 기반 시간-주파수 위치 인식은 SNR = 1dB부터 80% 이상의 intersection over union (IOU) 성능을 달성한다.

In military communications, low probability of detection (LPD) systems are increasingly important to minimize enemy interception, driving demand for robust detection techniques. This paper proposes a spectrum sensing–based method to determine signal presence. Collected signals are sampled, transformed by fast fourier transform (FFT), and accumulated into spectrograms for deep learning input. A convolutional neural network (CNN) identifies signal existence, achieving approximately 2 dB gain and 35% higher accuracy at signal to noise ratio (SNR) = –6 dB. A you only look once (YOLO) model then localizes time– frequency positions, reaching over 80% Intersection Over Union (IOU) from SNR = 1 dB onward.

TV 대역 White Space 송수신기에서 스펙트럼 센싱을 위한 RF 에너지 검출기 구조를 제안하였다. 제안된 에너지 검출기는 RF 능동 필터 구조에 기반하며, RF 저잡음 증폭기에 고역통과필터를 포함하는 피드포워드 루프를 추가함으로써 원하는 RF 주파수 성분만 통과시키고 그 외의 대역은 감쇄시키는 동작을 수행한다. 본 연구에서는 기존의 구조가 갖는 단점인 단측파 대역만 억압할 수 있는 문제를 해결하고자 양측파 대역을 동시에 억압할 수 있는 새로운 구조를 제안하였고, 간단한 시스템 모델링을 통해 구성요소의 Non-ideality에 의한 RF 에너지 검출기 성능에 대한 영향을 평가하였다. 또한, 시스템 시뮬레이션을 통해 양측파대역이 효과적으로 감쇄되어 RF 에너지 검출기로서 동작할 수 있음을 보였다.

An RF energy detector for spectrum sensing in TV white space transceiver is presented. It is based on an RF active filtering technique that comprises a low-noise amplifier with a frequency-translation high-pass filtering feedfoward loop, which attenuates the unwanted sideband energy and only passes the wanted band energy. Unlike the conventional architecture, a new architecture that can attenuate both sidebands at the same time is proposed. A simplified system modeling method is presented to assess the non-ideality effects on the RF energy detector performances. System behavioral simulations demonstrate that the proposed architecture can be instrumental for realizaing a RF energy detector circuit in CMOS.

본 논문은 무전원의 백스케터 통신이 결합된 RF 에너지 하비스팅 시스템에서 주파수 자원의 효율을 높이기 위한 스펙트럼 센싱 알고리즘을 연구했다. 송신기 측에 스팩트럼 센싱 알고리즘을 이용해서 유휴주파수 대역을 찾고, 찾은 유휴주파수 대역 중에서 페이 딩 영향이 적은 채널을 선택한다. 알고리즘을 적용한 송신기는 페이딩의 영향이 적은 신호를 송신하여, 수신측에서 수신신호의 간섭 을 완화시키고, 수신신호강도를 향상시킬 수 있다. 따라서, 백스케터 통신이 결합된 RF 에너지 하비스팅에 스팩트럼 센싱 알고리즘 이 적용된 송신기를 사용하고, 컴퓨터 시뮬레이션 결과를 통해 백스케터 통신의 BER과 수신거리별 수신세기, 그리고 RF 에너지 하비스팅의 성능이 이전보다 향상하는 것을 확인하였다.

In this paper, we studied a spectrum sensing algorithm for the efficient use of available spectrum in RF energy harvesting system combined with backscatter communication. We first looked for white spaces and then, selected low fading channel among white spaces using spectrum sensing algorithm at a transmitter. The transmitter employing the algorithm alleviates signal interference and improves the received signal strength indication through signals transmitted by low fading channel. The proposed RF energy harvesting system combined with backscatter communication is used the transmitter employing the algorithm. As a result of computer simulations, we can find the performance improvements of RF energy harvesting, BER of backscatter communication, and the received signal strength per distance of backscatter tag.

본 논문에서는 멀티콥터의 효율적 영상 전송을 위해 필요한 압축센싱 기법을 제안한다. 제안된 구조는 압축센싱에 기반한 데이터 용량을 줄이는 것에 중점을 둔다. 우선 Spectrum sensing의 기본원리를 설명하고 AMP(Approximate Message Passing)와 CoSaMP(Compressive Sampling Matched Pursuit)을 수학적 분석과 모의실험 결과를 통해서 비교한다. 또한 두 알고리즘을 계산시 간과 복잡도 관점에서 평가하고 멀티콥터 동작에 적합한 알고리즘을 제안한다. 본 논문의 실험결과는 AMP 알고리즘이 CoSaMP 알고리즘보다 계산시간이 적고 이미지 에러 확률도 낮다는 것을 보여준다.

This paper proposed a novel compressed sensing (CS) technique for an efficient video transmission of multi-copter. The proposed scheme is focused on reduction of the amount of data based on CS technology. First, we describe basic principle of Spectrum sensing. And then we compare AMP(Approximate Message Passing) with CoSaMP(Compressive Sampling Matched Pursuit) through mathematical analysis and simulation results. They are evaluated in terms of calculation time and complexity, then the promising algorithm is suggestd for multicopter operation. The result of experiment in this paper shows that AMP algorithm is more efficient than CoSaMP algorithm when it comes to calculation time and image error probability.

스펙트럼 센싱은 인지무선통신(CR, Cognitive Radio) 네트워크를 구성함에 있어 핵심적인 기술 중에 하나이다. 2차 사용자인 CR사용자는 1차 사용자에게 간섭을 주지 않기 위해 스펙트럼을 센싱 하여 해당 채널이 비어있거나 규정된 레벨 이하일 경우에만 해당 채널을 사용하게 된다. 최근 스마트폰의 사용으로 데이터의 트래픽이 증가함에 따라 광대역 서비스가 급증하고 있으며, 그에 따라 한 번에 넓은 대역을 센싱하는 광대역 센싱 기법의 필요성이 점차 높아지고 있다. 이러한 광대역 센싱은 인지무선통신이 수행되기 위한 매우 중요한 기술 중 하나이다. 높은 분해능을 갖는 광대역 센싱을 수행하기 위해서는 신뢰 할 수 있는 신호를 얻기 위한 알고리즘 처리가 선행되어야 한다. 따라서, 본 논문에서는 채널 상태가 좋지 않은 부 대역을 제외한 나머지 대역을 CR 사용자가 각각 센싱하여 정보를 취합하여 1차 사용자의 유무를 결정하는 협력 센싱 기법을 제안하였다. 이 때, 신호의 신뢰성을 높이기 위해 각각의 부 대역으로 나눈 CSI(Channel State Information)를 이용하여, 채널 상태를 파악한다. 또한, 검출 기법으로는 시스템 복잡도를 감소시키기 위하여 에너지검출 기법을 사용한다. 모의실험을 통해 성능을 검증하였고 그 성능은 SNR(Signal Noise ratio)에 따른 검출 확률로 나타내었다.

Spectrum sensing is a critical functionality of CR network; it allow secondary user to detect spectral holes and to opportunistically use under-utilized frequency bands without causing harmful interference to primary use. Recently, wideband service has been increase for processing abundance of data traffic. So CR network needs a realizable implementation design of spectrum sensing for wideband. To get high resolution performance of wideband sensing must precede algorithm processing for reliability signal detection. By the way, the performance of spectrum sensing can be degraded due to fading and shadowing. In order to overcome this problem, we propose system model of wideband sensing scheme on energy detected collaborative technique. we divide wideband into narrowbands and use narrowbands to detect signal excepting some narrowbands including bad channel through the CSI. And we simulate and analyze in terms of detection probability with various SNR.

Spectrum sensing plays an essential role in a cognitive radio network, which enables opportunistic access to an underutilized licensed spectrum. In conventional cooperative spectrum sensing (CSS), all cognitive users (CUs) in the network spend the same amount of time on spectrum sensing and waste time in remaining silent when other CUs report their sensing results to the fusion center. This problem is solved by the superposition cooperative spectrum sensing (SPCSS) scheme, where the sensing time of a CU is extended to the reporting time of the other CUs. Subsequently, SPCSS assigns the CUs different sensing times and thus affects both the sensing performance and the throughput of the system. In this paper, we propose an algorithm to determine the optimal sensing time of each CU for SPCSS that maximizes the achieved system throughput. The simulation results prove that the proposed scheme can signi?cantly improve the throughput of the cognitive radio network compared with the conventional CSS.

In this paper, the average probability of the symbol error rate (SER) and throughput are studied in the presence of joint spectrum sensing and data transmission in a cognitive relay network, which is in the environment of an optimal power allocation strategy. In this investigation, the main component in calculating the secondary throughput is the inclusion of the spatial false alarms, in addition to the conventional false alarms. It has been shown that there exists an optimal secondary power amplification factor at which the probability of SER has a minimum value, whereas the throughput has a maximum value. We performed a Monte-Carlo simulation to validate the analytical results.

The different issues in the wireless channel, such as multipath fading, shadowing, and hidden terminal problems, limit the sensing abilities of the single user in a cognitive radio network (CRN). In this connection, it is more suitable to follow cooperative spectrum sensing (CSS). However, the various categories of malicious users (MUs) pose threats to the performance of CSS. These MUs are eager to report false sensing data to the fusion center (FC) to deceive its decision. This work considered the CSS performance in the presence of always yes MU (AYMU), opposite MU (OMU), and always no MU (ANMU). Moreover, the considered particle swarm optimization (PSO) algorithm takes out reports of the MUs using one to many sensing distances (OTMSD) and z-score outlier detection techniques before the final decision. Simulation results validate the effectiveness of the proposed scheme in the presence of MUs.

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.

In cognitive radio (CR), spectrum sensing is an essential function since secondary users (SUs) must determine whether the primary user (PU) is utilizing the channel or not, and furthermore, SUs opportunistically access the licensed channel when the PU is absent. In this paper, spectrum sensing is implemented by energy detection, and a software-defined radio testbed is built to evaluate sensing performance by energy detection in a real environment. In particular, the testbed was built based on the GNU's Not Unix (GNU) Radio software platform and Universal Software Radio Peripheral National Instruments 2900 devices. More specifically, a new block of energy detection is developed by using an out-of-tree module from GNU Radio. To successfully integrate CR into the cloud computing paradigm, we also implement cloud computing-based spectrum sensing by utilizing a cloud server with ThingSpeak, such that we can store, process, and share the sensing information more efficiently in a centralized way in the cloud server.

Spectrum sensing in cognitive radio networks allows secondary users to sense the unused spectrum without causing interference to primary users. Cognitive radio requires more accurate sensing results from unused portions of the spectrum. Accurate spectrum sensing techniques can reduce the probability of false alarms and misdetection. In this paper, a two-stage spectrum sensing scheme is proposed for cooperative spectrum sensing in cognitive radio networks. In the first stage, spectrum sensing is executed for each secondary user using energy detection based on double adaptive thresholds to determine the spectrum condition. If the energy value lies between two thresholds, a fuzzy logic scheme is applied to determine the channel conditions more accurately. In the second stage, a fusion center combines the results of each secondary user and uses a fuzzy logic scheme for combining all decisions. The simulation results show that the proposed scheme provides increased sensing accuracy by about 20% in some cases.

This work proposes a Spectrum Sensing (SS) scheme based on a Convolutional Autoencoder (CAE) for application in Cognitive Radio Networks. The channel occupancy is modeled as an anomaly detection problem. The CAE is trained only with noise-signal samples so that any random modulated signal observed in the wireless channel will be regarded as an outlier. The relationship between the detection threshold and the system performance is evaluated. Experiments demonstrate that the SS proposal can identify primary signal presence with better accuracy (high detection and low false alarm rate) than conventional Energy Detection.

Cognitive radio has been attracting increased attention as an effective approach to improving spectrum efficiency. One component of cognitive radio, spectrum sensing, has an important relationship with the performance of cognitive radio. In this paper, after a summary and analysis of the existing spectrum-sensing algorithms, we report that the existing eigenvalue-based semi-blind detection algorithm and blind detection algorithm have not made full use of the eigenvalues of the received signals. Applying multi-antenna systems to cognitive users, we design a variety of spectrum-sensing algorithms based on the joint distribution of the eigenvalues of the received signal. Simulation results validate that the proposed algorithms in this paper are able to detect whether the signal of the primary user exists or not with high probability of detection in an environment with a low signal-to-noise ratio. Compared with traditional algorithms, the new algorithms have the advantages of high detection performance and strong robustness

In spectrum sensing, there is a tradeoff between the probability of missed detection and the probability of a false alarm according to the value of the sensing threshold. Therefore, it is important to determine the sensing threshold suitable to the environment of cognitive radio networks. In this study, we consider a cognitive radio-based ad hoc network where secondary users directly communicate by using the same frequency band as the primary system and control their transmit power on the basis of the distance between them. First, we investigate a condition in which the primary and the secondary users can share the same frequency band without harmful interference from each other, and then, propose an algorithm that controls the sensing threshold dynamically on the basis of the transmit power of the secondary user. The analysis and simulation results show that the proposed sensing threshold control algorithm has low probabilities of both missed detection and a false alarm and thus, enables optimized spectrum sharing between the primary and the secondary systems.

주파수 자원 가치는 무선통신의 발전과 함께 더욱 커지고 있다. 하지만 앞으로 정보화 사회에서는 주파수 자원의 수요가 공급에 비하여 매우 많기 때문에 주파수 부족 현상이 심각하게 대두된다. 따라서 사용되지 않고 있는 주파수 자원을 효율적으로 이용하기 위한 기술로서 최근 각광을 받고 있는 Cognitive Radio(CR) 기술이 필요한 시점이다. 본 논문은 임베디드 보드에서 CR 기반의 효율적인 Spectrum Sensing 기법을 적용하였다. 현재 이론적으로 제시되어 있는 여러 Spectrum Sensing 기법들에 대해 살펴보고 이 기법들을 CR 환경이 구현된 임베디드 플랫폼에 적용시켜 MAC 메시지를 통해 성능을 검증하였다.

The worth of frequency resources is growing up because of development of wireless communication. However, frequency resources is short, because demand is much more than supply. So it needs Cognitive Radio(CR), which manages frequency resources efficiently. In this paper, we apply the efficient Spectrum Sensing method based on CR in an embedded board. We examine various Spectrum Sensing methods and apply these methods to an embedded platform on CR environment and verify the performance through the MAC message.

In Orthogonal Frequency Division Multiplexing OFDM, a cyclic prefix (CP) is applied to reduce the intervention between symbols. The CP results in bandwidth wastage, and the solution to this problem leads to the cognitive radio. In the first part of the work, OFDM with CP is presented and integrated with cyclostationary spectrum sensing. Subsequently, OFDM with cyclostationary spectrum sensing without CP is proposed with the function of the filter at the transmitter and receiver being to utilize the bandwidth that is wasted in the CP phase. The Bit Error Rate BER, signal probability detection, false alarm, and error probability are obtained and analyzed.

In cognitive radios, spectrum sensing plays an important role in accurately detecting the presence or absence of a licensed user. However, the intervention of malicious users (MUs) degrades the performance of spectrum sensing. Such users manipulate the local results and send falsified data to the data fusion center; this process is called spectrum sensing data falsification (SSDF). Thus, MUs degrade the spectrum sensing performance and increase uncertainty issues. In this paper, we propose a method based on the Hausdorff distance and a similarity measure matrix to measure the difference between the normal user evidence and the malicious user evidence. In addition, we use the Dempster-Shafer theory to combine the sets of evidence from each normal user evidence. We compare the proposed method with the k-means and Jaccard distance methods for malicious user detection. Simulation results show that the proposed method is effective against an SSDF attack.