Dynamic frequency hopping channel management in cognitive radio ad-hoc networks

Yoo, Sang‐Jo; Won, Jong-Min; Seo, Myunghwan; Cho, Hyung-Weon

doi:10.1109/apcc.2015.7412550

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…Figure 12 shows the data transmission and sensing utilities of the proposed GA-based joint sensing and resource allocation optimization for different population sizes. The fairness index used in this paper is given in (27). As we can see, the proposed method guarantees the required minimum primary protection probability (P min D = 0.8), and as increasing the generation number, it reaches to higher than 0.9.…”

Section: Simulation Resultsmentioning

confidence: 95%

“…Two sets of traffic demands of member nodes are examined in Figure 15A and B. ω 1 = ω 2 = 0.5 and N = K = 10 are used. The fairness index used in this paper is given in (27). Compared with the noncontrolled method, in which the data transmission utility always increases linearly for the actual allocated data utilization, the proposed method shows that the fair index of the proposed GA-based method is closer to the ideal value (Fairness Index = O n /T n = 1.0) than that of the uncontrolled-method.…”

Section: Simulation Resultsmentioning

confidence: 98%

“…Based on the all received information from the member nodes, the cluster head determines hopping channel pattern and sensing and data transmission schedule. 27 At each scheduled hopping time, data transmission member nodes and sensing member nodes are derived using the proposed GA. This hopping channel pattern and schedule information is broadcasted by the cluster head to all member nodes.…”

Section: Frequency Hopping-based Cr Ad-hoc Network Architecturementioning

confidence: 99%

“…27 At each scheduled hopping time, data transmission member nodes and sensing member nodes are derived using the proposed GA. The cluster head determines a channel set that is commonly available at all member nodes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Joint spectrum sensing and resource allocation optimization using genetic algorithm for frequency hopping–based cognitive radio networks

Yoo

Shrestha

Seo

et al. 2018

Int J Communication

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In cognitive radio (CR) networks, secondary users should effectively use unused licensed spectrums, unless they cause any harmful interference to the primary users. Therefore, spectrum sensing and channel resource allocation are the 2 main functionalities of CR networks, which play important roles in the performance of a CR system. To maximize the CR system utility, we propose a joint out-of-band spectrum sensing and operating channel allocation scheme based on genetic algorithm for frequency hopping-based CR networks.In this paper, to effectively sense the primary signal on hopping channels at each hopping slot time, a set of member nodes sense the next hopping channel, which is called out-of-band sensing. To achieve collision-free cooperative sensing reporting, the next channel detection notification mechanism is presented.Using genetic algorithm, the optimum sensing and data transmission schedules are derived. It selects a sensing node set that participate the spectrum sensing for the next expected hopping channel during the current channel hopping time and another set of nodes that take opportunity for transmitting data on the current hopping channel. The optimum channel allocation is performed in accordance with each node's individual traffic demand. Simulation results show that the proposed scheme can achieve reliable spectrum sensing and efficient channel allocation.

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Section: Simulation Resultsmentioning

confidence: 95%

Section: Simulation Resultsmentioning

confidence: 98%

Section: Frequency Hopping-based Cr Ad-hoc Network Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint spectrum sensing and resource allocation optimization using genetic algorithm for frequency hopping–based cognitive radio networks

Yoo

Shrestha

Seo

et al. 2018

Int J Communication

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“…There is a relevant study on a cognitive radio (CR) engine that is similar to the one proposed in this paper, which presents a technique for the dynamic exploitation of available channels not occupied by the primary user for the CR node user by observing the channel environment via spectrum sensing [7]. This paper shows the method’s ability to avoid collision with the primary user on the specific channel currently used by the CR node user.…”

Section: Introductionmentioning

confidence: 99%

Applying Case-Based Reasoning to Tactical Cognitive Sensor Networks for Dynamic Frequency Allocation

Park

Lee²,

Choi³

et al. 2018

Sensors

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In this paper, a cognitive radio engine platform is proposed for exploiting available frequency channels for a tactical wireless sensor network while aiming to protect incumbent communication devices, known as the primary user (PU), from undesired harmful interference. In the field of tactical communication networks, there is an urgent need to identify available frequencies for opportunistic and dynamic access to channels on which the PU is active. This paper introduces a cognitive engine platform for determining the available channels on the basis of a case-based reasoning technique deployable as a core functionality on a cognitive radio engine to enable dynamic spectrum access (DSA) with high fidelity. To this end, a plausible learning engine to characterize the channel usage pattern is introduced to extract the best channel candidate for the tactical cognitive radio node (TCRN). The performance of the proposed cognitive engine was verified by simulation tests that confirmed the reliability of the functional aspect, which includes the learning engine, as well as the case-based reasoning engine. Moreover, the efficacy of the TCRN with regard to the avoidance of collision with the PU operation, considered the etiquette secondary user (SU), was demonstrated.

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Spectrum Sensing for Cognitive Radio Using a Filter Bank Approach

Bagchi¹,

Siddiqui²

2021

Intelligent Multi‐modal Data Processing

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Dynamic frequency hopping channel management in cognitive radio ad-hoc networks

Cited by 7 publications

References 6 publications

Joint spectrum sensing and resource allocation optimization using genetic algorithm for frequency hopping–based cognitive radio networks

Joint spectrum sensing and resource allocation optimization using genetic algorithm for frequency hopping–based cognitive radio networks

Applying Case-Based Reasoning to Tactical Cognitive Sensor Networks for Dynamic Frequency Allocation

Spectrum Sensing for Cognitive Radio Using a Filter Bank Approach

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