• 한국ITS학회:학술대회논문집
• /
• 한국ITS학회 2010년도 춘계학술대회
• /
• pp.48-52
• /
• 2010

• 한국위성정보통신학회논문지
• /
• 제10권1호
• /
• pp.49-55
• /
• 2015

인지무선 네트워크에서 스펙트럼 센싱은 우선사용자에게 간섭을 주지 않기 위해 기본적으로 수행해야 하는 단계이다. 스펙트럼 센싱에 요구되는 샘플 수는 2차 사용자의 성능에 직접적으로 영향을 주기 때문에, 2차 사용자의 성능과 우선사용자에 대한 간섭은 트레이드오프 관계에 있다. 스펙트럼 센싱에 필요한 샘플 수는 요구되는 오검출 확률, 검출확률 및 우선 사용자의 최소 요구 SNR로 부터 얻어진다. 우선 사용자 센싱에 요구되는 SNR은 2차 사용자의 전송반경과 관련 있기 때문에, 2차사용자들을 모아 센싱집합으로 구성하고 요구되는 전송영역을 최소화시킴으로써 스펙트럼 센싱에 요구되는 우선사용자의 SNR을 완화시킬 수 있다. 따라서 스펙트럼 센싱에 필요한 최소 샘플 수를 줄임으로써 인지무선 네트워크의 전송량을 향상시킬 수 있다. 본 논문에서는 이를 위해 센싱집합인 클러스터링을 통해 게임이론으로 클러스터의 크기에 따라 얻는 이득과 손실을 트레이드오프로 디자인하고, 시뮬레이션을 통해 제안된 방법의 성능을 확인한다.

• 한국ITS학회:학술대회논문집
• /
• 한국ITS학회 2010년도 춘계학술대회
• /
• pp.33-36
• /
• 2010

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제4권6호
• /
• pp.1023-1041
• /
• 2010

In cognitive radio networks, cooperative spectrum sensing schemes are proposed to improve the performance of detecting licensees by secondary users. Commonly, the cooperative sensing can be realized by means of hard decision fusion (HDF) or soft decision fusion (SDF) schemes. The SDF schemes are superior to the HDF ones in terms of the detection performance whereas the HDF schemes are outperforming the SDF ones when the traffic overhead is taken into account. In this paper, a hybrid SFD-HDF cluster-based approach is developed to jointly exploit the advantages of SFD and HDF schemes. Different SDF schemes have been proposed and compared within a given cluster whereas the OR-rule base HDF scheme is applied to combine the decisions reported by cluster headers to a common receiver or base station. The computer simulations show promising results as the performance of the proposed scenario of hybridizing soft and hard fusion schemes is significantly outperforming other different combinations of conventional SDF and HDF schemes while it noticeably reduces the network traffic overhead.

• 정보과학회 논문지
• /
• 제43권1호
• /
• pp.119-125
• /
• 2016

인지 무선 기술은 보조 사용자가 (SUs) 주 사용자 (PUs)에 간섭을 주지 않고 기회주의적 방식으로 라이선스 스펙트럼을 사용할 수 있는 기술이다. 인지 라디오의 핵심 기술은 스펙트럼 센싱이다. 그러나 인지 무선 센서 네트워크에서 에너지 효율적인 스펙트럼 센싱 기법에 대한 연구는 많지 않다. 본 논문에서는 클러스터 기반의 인지 무선 센서 네트워크를 위한 에너지 효율적인 협력 스펙트럼 센싱 노드 선택 기법을 제안한다. 제안하는 기법에서, 허위 경보 확률 및 에너지 소비를 최적화하기 위하여 클러스터 내 스펙트럼 센싱 노드들의 수를 최소화하게 된다. 시뮬레이션 결과를 통하여 본 논문에서 제안한 최적의 스펙트럼 센싱 노드 수를 적용하므로 스펙트럼 센싱 효율성이 향상되었고, 또한 네트워크의 에너지 효율성도 보장된 것을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권7호
• /
• pp.1951-1975
• /
• 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.

• Journal of Electrical Engineering and Technology
• /
• 제10권5호
• /
• pp.2179-2188
• /
• 2015

Recently, many researches have been done to solve the challenging problem of Blind Source Separation (BSS) problems in the underdetermined cases, and the “Two-step” method is widely used, which estimates the mixing matrix first and then extracts the sources. To estimate the mixing matrix, conventional algorithms such as Single-Source-Points (SSPs) detection only exploits the sparsity of original signals. This paper proposes a new underdetermined mixing matrix estimation method for time-delayed mixtures based on the receiver prior exploitation. The prior information is extracted from the specific structure of the complex-valued mixing matrix, which is used to derive a special criterion to determine the SSPs. Moreover, after selecting the SSPs, Agglomerative Hierarchical Clustering (AHC) is used to automaticly cluster, suppress, and estimate all the elements of mixing matrix. Finally, a convex-model based subspace method is applied for signal separation. Simulation results show that the proposed algorithm can estimate the mixing matrix and extract the original source signals with higher accuracy especially in low SNR environments, and does not need the number of sources before hand, which is more reliable in the real non-cooperative environment.