Q-Learning Based Co-Operative Spectrum Mobility in Cognitive Radio Networks

Das, A.; Ghosh, S.; Das, Nabanita; Barman, Abhirup Das

doi:10.1109/lcn.2017.80

Cited by 24 publications

(14 citation statements)

References 14 publications

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“…The user's transmission bandwidth is 3.8MHz. Inspired by work [40]- [42], the transmission signal is rising cosine waveforms. The demodulation threshold (th) is 10 dB.…”

Section: Simulation Results and Analysis A Simulation Settingsmentioning

confidence: 99%

Pattern-Aware Intelligent Anti-Jamming Communication: A Sequential Deep Reinforcement Learning Approach

Liu

Chen

et al. 2019

IEEE Access

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This paper investigates the problem of anti-jamming communication in dynamic and intelligent jamming environment. A sequential deep reinforcement learning algorithm (SDRLA) without prior information is proposed, and raw spectrum information is used as the input of SDRLA. The proposed SDRLA algorithm mainly contains two parts: Firstly, deep learning and sliding window principle are introduced to identify jamming patterns; Secondly, reinforcement learning is carried out to make on-line channel selection based on recognized jamming patterns. In addition, channel switching cost is introduced for the purpose of formulating the trade-off relationship between throughput and overhead. Taking advantage of both deep learning and reinforcement learning, this method can realize rapid and effective anti-jamming channel selection with no need for modeling the jammer's characteristics. Simulation results show the convergence and effectiveness of the proposed SDRLA algorithm. Compared with single-mode reinforcement learning, our approach can reach better convergence performance and higher utility. INDEX TERMS Pattern-aware, reinforcement learning (RL), deep learning (DL), anti-jamming communication, channel switching cost.

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“…The user's transmission bandwidth is 3.8MHz. Inspired by work [40]- [42], the transmission signal is rising cosine waveforms. The demodulation threshold (th) is 10 dB.…”

Section: Simulation Results and Analysis A Simulation Settingsmentioning

confidence: 99%

Pattern-Aware Intelligent Anti-Jamming Communication: A Sequential Deep Reinforcement Learning Approach

Liu

Chen

et al. 2019

IEEE Access

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“…In (28), the numerator is the interference probability and denominator is the probability of PU existence. In order to estimate the interference probability, we calculate the conditional probabilities and sum the results of that PU is busy from each sensing result (D 0 , D 1 ) in the interval (SU on ) where the SU operates from the decision the PU is in idle state in the previous sensing time as show in (29). In the interval in which the SU operates, the sensor node performs the IA-based sensing.…”

Section: Probabilistic Estimation Of Interference Ratio and Transmissmentioning

confidence: 99%

“…Jan et al has assigned a sub-band for spectrum sensing order to obtain high throughput while minimizing the number of sub-bands that the SU should sense [28]. Das et al has cooperatively calculated the reward for the idle and busy states of the channel and evaluated the priority of the channel [29]. Yang et al considered hardware reconfiguration energy consumptions and time delays when selecting a sub-band for wide-band sensing [30].…”

Section: Introductionmentioning

confidence: 99%

Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio

Jang

Yoo

2018

J Wireless Com Network

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In cognitive radio (CR) system, secondary user (SU) should use available channels opportunistically when the primary user (PU) does not exist. In CR network, SUs have to detect the PU signal with sufficient sensing time to guarantee the detection probability and minimize the interference to the PU, while the CR system should have enough data transmission time to maximize the transmission opportunity of the SU. Therefore, the sensing time and data transmission time of the SU are generally considered as main optimization parameters to maximize the throughput of the CR system. In this paper, a separate sensing node is designated and the sensing is continuously performed using the interference alignment (IA) technique. In this paper, the designated sensing node estimates the interference ratio and transmission opportunity loss ratio. To satisfy the primary user's interference requirement and maximize secondary throughput, we proposed dynamic adjustment mechanism for sensing slot time and sensing report interval using reinforcement learning in time-varying communication environment. The experimental results show that the proposed approach can minimize the interference on PU and enhance the transmission opportunity of SUs.

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“…The spectral decision function plays a very important role for the optimal performance of the cognitive radio network (CRN) within the cognitive cycle (Lala, Balkhi, & Mir, 2017) since the proper move at a timely instant within the radioelectric spectrum guarantees that the PUs do not suffer from any interference during the use of licensed channels, and that the SUs have uninterrupted communications without delays that cause information loss. Hence, most of the research work has focused on improving and establishing more assertive strategies for optimal development (Das, Ghosh, Das, & Barman, 2017;Hoque, Azmal, & Arif, 2017;Kyryk & Yanyshyn, 2017).…”

Section: Introductionmentioning

confidence: 99%

Optimal value of past samples for decision making in cognitive radio networks

Estupiñan

Suárez

Ramírez

2020

Tecnura

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Contexto: El modelado y predicción del uso del espectro por parte de los PU es un aspecto importante para reducir la interferencia entre los SU y PU, y mejorar el desempeño de la decisión espectral. Lo anterior requiere de información espectral pasada, que permita al algoritmo modelar el comportamiento del PU. Objetivo: Determinar el valor óptimo de muestras pasadas y tiempo de recalculo de criterios de decisión, para los algoritmos de toma de decisiones en redes de radio cognitiva. Metodología: Se realizan varios experimentos de simulación a partir del algoritmo FFAHP, en dos diferentes enfoques, tiempo-real y mejor-esfuerzo, con tráfico alto y bajo, en la banda de frecuencia GSM. Se realiza un análisis estadístico de los datos obtenidos, variando los parámetros de time range, mientras criteria time permanece constante, y viceversa. Resultados: Para tráfico alto es suficiente con tomar 1800 muestras anteriores para calcular el valor inicial de los parámetros y actualizarlos cada 10 minutos (1800). Si el tráfico es bajo es suficiente con tomar 5400 muestras anteriores para calcular el valor inicial de los parámetros y actualizarlos cada 10 minutos (1800). Conclusiones: No es necesario un número elevado de muestras anteriores para determinar el valor inicial de los parámetros de decisión para obtener un buen desempeño de la tasa de handoff, así como tampoco lo es para la actualización de los mismos, para un tráfico correspondiente a la banda de frecuencia GSM. Financiamiento: El presente trabajo es un resultado de un proyecto de investigación financiado por el Centro de Investigaciones y Desarrollo Científico de la Universidad Distrital Francisco José de Caldas.

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Q-Learning Based Co-Operative Spectrum Mobility in Cognitive Radio Networks

Cited by 24 publications

References 14 publications

Pattern-Aware Intelligent Anti-Jamming Communication: A Sequential Deep Reinforcement Learning Approach

Pattern-Aware Intelligent Anti-Jamming Communication: A Sequential Deep Reinforcement Learning Approach

Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio

Optimal value of past samples for decision making in cognitive radio networks

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