• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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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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• v.7 no.9
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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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• v.16 no.2
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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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• v.12 no.4
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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.

• Journal of Advanced Navigation Technology
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• v.19 no.4
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• pp.318-322
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• 2015

In wireless communication networks, most of the prediction techniques are used for predicting the amount of resource required by user's calls for improving their demanding quality of service. However, we propose a channel allocation scheme based on predicting the resources of white spectrum holes for improving the QoS of rental user's spectrum handoff calls for cognitive radio networks in this paper. This method is supported by Wiener predictor to predict the amount of white spectrum holes of license user's free spectrum resources. We classify rental user's calls into initial calls and spectrum handoff calls, and some portion of predicted spectrum-hole resources is reserved for spectrum handoff calls' priority allocation. Simulations show that the performance of the proposed scheme outperforms in spectrum handoff call's dropping rate than an existing method without spectrum hole prediction(11% average improvement in 50% reservation).

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.440-445
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• 2016

There is a scheme where secondary users (SU) use predicted spectrum holes for primary users (PU) not to utilize for efficient utilization of the limited spectrum resources in cognitive radio networks. In this paper, we propose an adaptive call admission control framework that minimizes spectrum hopping call dropped probability (SHDP) for satisfying SU quality of service (QoS). The scheme is based on a call admission control (CAC), bandwidth prediction and adaptive bandwidth assignment. The prediction model predicts not only the number of spectrum holes, but requested bandwidth of SU spectrum hopping call, and then the CAC minimizes SHDP via an adaptive bandwidth assignment in resources not being enough for reservation. We bring Wiener prediction model to predict the resources. Simulations are conducted to compare the performance of proposed scheme with an existing, and show its ability of minimizing the SHDP.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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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.

• 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.23 no.8
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• pp.900-908
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• 2012

Owing to the proliferation of smart phone users, a proactive spectrum policy is needed in order to deal with increasing data traffic. Therefore, the prediction of frequency requirements for future wireless local area network (WLAN) as well as a licensed cellular communication is necessary. In this paper, we proposed a new prediction method for WLAN spectrum requirements. This method includes both a traditional prediction method and an offloading percentage from cellular network, Also, it can consider a frequency interference between access points using a statistical approach. Based on these approaches, we can predict the spectrum requirements of future domestic WLAN services considering the frequency interference. Finally, we suggest the spectrum policy for WLAN which can prevent spectrum shortage of future WLAN services.

• Journal of Advanced Navigation Technology
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• v.21 no.4
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• pp.347-352
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• 2017

In this paper, we propose a method that reduces mobile user's handover call dropping probability by using cognitive radio technology(CR) in cellular - based wireless cognitive radio networks. The proposed method predicts a cell to visit by Ziv-Lempel algorithm, and then supports mobile user with prediction of spectrum holes based on CR technology when allocated channels are short in the cell. We make neural network predict spectrum hole resources, and make handover calls use the resources before initial calls. Simulation results show CR technology has the capability to reduce mobile user handover call dropping probability in cellular mobile communication networks.