• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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• pp.3029-3045
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• 2017

The main processes of a cognitive radio system include spectrum sensing, spectrum decision, spectrum sharing, and spectrum conversion. Experimental results show that these stages introduce a time delay that affects the spectrum sensing accuracy, reducing its efficiency. To reduce the time delay, the frequency spectrum prediction was proposed to alleviate the burden on the spectrum sensing. In this paper, the deep recurrent neural network (DRNN) was proposed to predict the spectrum of multiple time slots, since the existing methods only predict the spectrum of one time slot. The continuous state of a channel is divided into a many time slots, forming a time series of the channel state. Since there are more hidden layers in the DRNN than in the RNN, the DRNN has fading memory in its bottom layer as well as in the past input. In addition, the extended Kalman filter was used to train the DRNN, which overcomes the problem of slow convergence and the vanishing gradient of the gradient descent method. The spectrum prediction based on the DRNN was verified with a WiFi signal, and the error of the prediction was analyzed. The simulation results proved that the multiple slot spectrum prediction improved the spectrum efficiency and reduced the energy consumption of spectrum sensing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권9호
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• pp.2101-2119
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• 2013

The spectrum allocation is an attractive issue for mobile cognitive radio (CR) network. However, the time-varying characteristic of the spectrum allocation is not fully investigated. Thus, this paper originally deduces the probabilities of spectrum availability and interference constrain in theory under the mobile environment. Then, we propose a prediction mechanism of the time-varying available spectrum lists and the dynamic interference topologies. By considering the node mobility and primary users' (PUs') activity, the mechanism is capable of overcoming the static shortcomings of traditional model. Based on the mechanism, two prediction-based spectrum allocation algorithms, prediction greedy algorithm (PGA) and prediction fairness algorithm (PFA), are presented to enhance the spectrum utilization and improve the fairness. Moreover, new utility functions are redefined to measure the effectiveness of different schemes in the mobile CR network. Simulation results show that PGA gets more average effective spectrums than the traditional schemes, when the mean idle time of PUs is high. And PFA could achieve good system fairness performance, especially when the speeds of cognitive nodes are high.

• Journal of Communications and Networks
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• 제16권2호
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• pp.209-216
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• 2014

Cognitive radio (CR) has been proposed to solve the spectrum utilization problem by dynamically exploiting the unused spectrum. In CR networks, a spectrum selection scheme is an important process to efficiently exploit the spectrum holes, and an efficient channel allocation scheme must be designed to minimize interference to the primary network as well as to achieve better spectrum utilization. In this paper, we propose a multichannel selection algorithm that uses spectrum hole prediction to limit the interference to the primary network and to exploit channel characteristics in order to enhance channel utilization. The proposed scheme considers both the interference length and the channel capacity to limit the interference to primary users and to enhance system performance. By using the proposed scheme, channel utilization is improved whereas the system limits the collision rate of the CR packets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1489-1503
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• 2018

Spectrum reuse has attracted much concern of researchers and scientists, however, the dynamic spectrum access is challenging, since an individual secondary user usually just has limited sensing abilities. One key insight is that spectrum usage forecasting among secondary users, this inspiration enables users to obtain more informed spectrum opportunities. Therefore, spectrum usage forecasting is vital to cognitive radio networks (CRNs). With this insight, a spectrum usage forecasting model for the occurrence of primary users prediction is derived in this paper. The proposed model is based on auto regressive enhanced primary user emergence reasoning (AR-PUER), which combines linear prediction and primary user emergence reasoning. Historical samples are selected to train the spectrum usage forecasting model in order to capture the current distinction pattern of primary users. The proposed scheme does not require the knowledge of signal or of noise power. To verify the performance of proposed spectrum usage forecasting model, we apply it to the data during the past two months, and then compare it with some other sensing techniques. The simulation results demonstrate that the spectrum usage forecasting model is effective and generates the most accurate prediction of primary users occasion in several cases.

• 한국항행학회논문지
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• 제19권4호
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• pp.318-322
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• 2015

무선통신망에서 예측기법을 이용하는 경우는 대부분 사용자호가 요구하는 자원의 크기를 예측하여 미리 요구자원을 예약함으로써 사용자호가 요구하는 품질을 보장한다. 그러나 본 논문에서는 무선인지통신망에서 면허사용자가 사용하지 않는 스펙트럼홀(spectrum hole)자원의 크기를 예측하여 대여사용자의 스펙트럼 핸드오프호의 서비스 품질을 향상시킬 수 있는 채널할당방법을 제안한다. 스펙트럼홀의 예측은 위너예측모델을 이용한다. 채널할당 방법은 대여사용자호를 초기 발생호와 스펙트럼 핸드오프호로 구분하고, 예측된 스펙트럼홀 자원의 일정부분을 예약하여 스펙트럼 핸드오프호에 우선적으로 할당한다. 시뮬레이션을 통하여 제안한 기법이 스펙트럼홀 예측을 사용하지 않는 방법보다 대여사용자의 스펙트럼 핸드오프호의 서비스 품질을 향상(50% 예약시 평균 11% 개선)시킬 수 있음을 보인다.

• 한국항행학회논문지
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• 제20권5호
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• pp.440-445
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• 2016

인지라디오망에서 제한된 스펙트럼 자원을 효율적으로 이용하는 방법으로 PU (primary user)가 사용하지 않는 스펙트럼 홀의 크기를 예측하여 SU (secondary user)가 이용하는 방법이 있다. 본 논문은 SU의 서비스품질을 만족시키기 위하여, SU스펙트럼 홉핑호의 손실확률 (SHDP; spectrum hopping call dropped probability)을 최소화는 적응 호수락제어 기법을 제안한다. 이 방법은 호수락제어, 대역폭예측, 적응대역폭할당으로 구성된다. 예측기법은 스펙트럼홀의 크기뿐만 아니라, SU스펙트럼 홉핑호가 요구하는 대역폭크기를 함께 예측하며, 예약할 수 있는 자원의 크기가 부족할 때는 적응대역폭할당을 이용하여 SU스펙트럼 홉핑호의 손실확률을 최소화시킨다. 예측기법은 위너예측기법을 이용한다. 시뮬레이션을 통하여 제안한 방법의 성능을 기존방법과 비교하고, SHDP를 최소화 할 수 있음을 보인다.

• 응용통계연구
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• 제24권6호
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• pp.995-1006
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• 2011

A time series can be decomposed into simple components with a multiscale method. Empirical mode decomposition(EMD) is a recently invented multiscale method in Huang et al. (1998). It is natural to apply a classical prediction method such a vector autoregressive(AR) model to the obtained simple components instead of the original time series; in addition, a prediction procedure combining a classical prediction model to EMD and Hilbert spectrum is proposed in Kim et al. (2008). In this paper, we suggest to adopt principal component analysis(PCA) to the prediction procedure that enables the efficient selection of input variables among obtained components by EMD. We discuss the utility of adopting PCA in the prediction procedure based on EMD and Hilbert spectrum and analyze the daily worm account data by the proposed PCA adopted prediction method.

• 전기학회논문지
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• 제62권1호
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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.

• 한국전자파학회논문지
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• 제23권8호
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• pp.900-908
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• 2012

최근 스마트폰의 등장에 따른 무선 트래픽 증가에 효율적으로 대처하기 위해서는 능동적인 주파수 정책이 필요하다. 이에 이동통신 주파수 소요량 예측과 함께 미래에 요구되는 무선 LAN 주파수 소요량을 예측할 필요가 있다. 미래에 요구되는 무선 LAN의 주파수 소요량은 미래의 무선 LAN 트래픽을 예측하고, 이를 무선 LAN의 시스템 효율로 나눔으로써 계산된다. 본 논문에서는 기존에 전통적인 무선 LAN 트래픽 예측법과 함께 이동통신으로부터 offloading하는 스마트폰 트래픽을 고려한 새로운 무선 LAN 트래픽 예측법을 제안하였다. 또한, AP의 증가에 따른 주파수 간섭 효과를 통계적으로 모델링하여 무선 LAN의 시스템 효율 계산 시 적용함으로써 실제 환경에 근접한 무선 LAN 주파수 소요량을 예측할 수 있도록 하였다. 예측된 주파수 소요량을 바탕으로 미래의 무선 LAN 활성화에 대비하여 AP간 간섭을 최소화할 수 있는 정책 방향을 제안하였다.

• 한국항행학회논문지
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• 제21권4호
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• pp.347-352
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• 2017

본 논문에서는 셀룰러 기반 무선 인지망에서 스펙트럼 인지(CR)기술을 이용하여 모바일 사용자의 핸드오버 호의 손실확률을 줄이는 방법을 제안한다. 제안한 방법에서는 모바일이 방문할 셀을 Ziv-Lempel 알고리듬을 이용하여 예측하고, 방문할 셀에 할당된 채널이 부족할 때는 CR기술에 기초한 스펙트럼 홀 자원을 예측하여 모바일 사용자를 지원한다. 스펙트럼 홀 자원의 크기는 신경망기법으로 예측하며, 예측된 스펙트럼 홀 자원은 핸드오버 호가 초기 발생 호 보다 우선하여 사용할 수 있게 한다. 시뮬레이션을 통하여 셀룰러 이동 통신망에 CR기술을 사용함으로써 모바일 사용자의 핸드오버 호 손실확률을 줄일 수 있음을 보인다.