• Journal of information and communication convergence engineering
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• v.13 no.4
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• pp.221-227
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• 2015

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 significantly improve the throughput of the cognitive radio network compared with the conventional CSS.

• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.429-436
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• 2008

In this paper, we study an optimization which determines the optimal sensing time and the number of cooperative sensing cognitive users for cooperative spectrum sensing scheme in cognitive radio networks. In cooperative spectrum sensing, cognitive users originally in inactive status are activated and take part in spectrum sensing along with transmitting cognitive users resulting in a reduced sensing time. Tradeoff between transmission rate gain and energy consumption due to cooperative sensing is formulated as a mixed integer programming problem which is solved for the optimal values.

• International Journal of Advanced Culture Technology
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• v.1 no.2
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• pp.18-23
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• 2013

In this paper, we consider the problem of designing optimal sensing time and the minimization of energy consumption in the Cognitive radio Network. Trade-off between throughput and the sensing time are observed, and the equations are derived for the optimal choice of design variables. In this paper, we also look at the optimization problem involving all the design parameters together. The advantages of the proposed scheme for the spectrum sensing and access process are shown through simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.984-996
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• 2014

In this paper, optimal sensing time allocation for adaptive multiband spectrum sensing-transmission procedure is investigated. The sensing procedure consists of an exploration phase and a detection phase. We first formulate an optimization problem to maximize the throughput by designing not only the overall sensing time, but also the sensing time for every stage in the exploration and detection phases, while keeping the miss detection probability for each channel under a pre-defined threshold. Then, we transform the initial non-convex optimization problem into a convex bilevel optimization problem to make it mathematically tractable. Simulation results show that the optimized sensing time setting in this paper can provide a significant performance gain over the previous studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4387-4404
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• 2015

The energy-efficient design of sensing-based spectrum sharing of a multi-input and multi-output (MIMO) cognitive radio (CR) system with imperfect multiple antenna spectrum sensing is investigated in this study. Optimal resource allocation strategies, including sensing time and power allocation schemes, are studied to maximize the energy efficiency (EE) of the secondary base station under the transmit power and interference power constraints. EE problem is formulated as a nonlinear stochastic fractional programming of a nonconvex optimal problem. The EE problem is transformed into its equivalent nonlinear parametric programming and solved by one-dimension search algorithm. To reduce searching complexity, the search range was founded by demonstration. Furthermore, simulation results confirms that an optimal sensing time exists to maximize EE, and shows that EE is affected by the spectrum detection factors and corresponding constraints.

• Journal of electromagnetic engineering and science
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• v.12 no.1
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• pp.1-7
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• 2012

In this paper, we investigated the throughput of a cognitive radio network where two primary frequency channels (PCs) are sensed and opportunistically accessed by N secondary users. The sharing sensing member (SSM) protocol is introduced to sense both PCs simultaneously. According to the SSM protocol, N SUs (Secondary User) are divided into two groups, which allows for the simultaneous sensing of two PCs. With a frame structure, after determining whether the PCs are idle or active during a sensing slot, the SUs may use the remaining time to transmit their own data. The throughput of the network is formulated as a convex optimization problem. We then evaluated an iterative algorithm to allocate the optimal sensing time, fusion rule and the number of members in each group. The computer simulation and numerical results show that the proposed optimal allocation improves the throughput of the SU under a misdetection constraint to protect the PCs. If not, its initial date of receipt shall be nullified.

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

• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.79-84
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• 2017

In a cognitive radio network (CRN), cognitive radio users (CUs) should be powered by a small battery for their operations. The operations of the CU often include spectrum sensing and data transmission. The spectrum sensing process may help the CU avoid a collision with the primary user (PU) and may save the energy that is wasted in transmitting data when the PU is present. However, in a time-slotted manner, the sensing process consumes energy and reduces the time for transmitting data, which degrades the achieved throughput of the CRN. Subsequently, the sensing process does not always offer an advantage in regards to throughput to the CRN. In this paper, we propose a scheme to find an optimal policy (i.e., perform spectrum sensing before transmitting data or transmit data without the sensing process) for maximizing the achieved throughput of the CRN. In the proposed scheme, the data collection period is considered as the main factor effecting on the optimal policy. Simulation results show the advantages of the optimal policy.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1171-1173
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• 2003

There are two datasets to estimate the area-mean and time-mean precipitation rate. For one, an array of surface rain gauges represents a series of rods that have to the time axis of the volume. And another data is that of a remote sensing make periodic overpasses at a fixed interval such as radar. The problem of optimally combining data from surface rain gauge data and remote sensed data is considered. In order to combining remote sensed data with Automatic Weather Station (AWS), we use optimal weighting method, which is similar to the method of [2]. They had suggested optimal weights that minimized value of the mean square error. In this paper, optimal weight is evaluated for the cases such as Changma, summer Monsoon, Typhoon and orographic rain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.2987-3002
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• 2013

The problem of optimal channel sensing in heterogeneous cognitive networks is considered to maximize the system throughput performance. The characteristics of an optimal operating sensing point maximizing the overall system rate are investigated under several rate criteria including the sum rate, the minimum of the primary and secondary rates, and the secondary rate with a guaranteed primary rate. Under the sum rate criterion, it is shown that the loss by imperfect sensing is no greater than half of the sum rate achieved by the perfect time sharing approach in a two user case if the sensing point is optimally designed.