Spectrum-Agile Cognitive Radios Using Multi-Task Transfer Deep Reinforcement Learning

Aref, Mohamed A.; Jayaweera, Sudharman K.

doi:10.1109/twc.2021.3076180

Cited by 12 publications

(2 citation statements)

References 40 publications

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“…In [ 20 ], the authors propose a cognitive engine design that enables a radio to find transmission opportunities in the non-contiguous wideband spectrum to avoid interference with CR, using multi-task transfer deep reinforcement learning that can be applied in the licensed spectrum to improve quality-of-service (QoS). The spectrum is partitioned into sub-bands, each made of a number of narrowband channels.…”

Section: Related Workmentioning

confidence: 99%

Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band

Soares

Passos

Castellanos

2023

Sensors

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Due to the propagation characteristics in the 2.5 GHz band, the signal is significantly degraded by building entry loss (BEL), making coverage in indoor environments in some cases non-existent. Signal degradation inside buildings is a challenge for planning engineers, but it can be seen as a spectrum usage opportunity for a cognitive radio communication system. This work presents a methodology based on statistical modeling of data collected by a spectrum analyzer and the application of machine learning (ML) to leverage the use of those opportunities by autonomous and decentralized cognitive radios (CRs), independent of any mobile operator or external database. The proposed design targets using as few narrowband spectrum sensors as possible in order to reduce the cost of the CRs and sensing time, as well as improving energy efficiency. Those characteristics make our design especially interesting for internet of things (IoT) applications or low-cost sensor networks that may use idle mobile spectrum with high reliability and good recall.

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Section: Related Workmentioning

confidence: 99%

Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band

Soares

Passos

Castellanos

2023

Sensors

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“…Spectrum sensing is a necessary prerequisite in cognitive radio systems. Cooperative spectrum sensing (CSS) can avoid the impact of shadowing and multipath fading, make full use of spatial differences, and overcome the shortcomings of single-user spectrum sensing [ 4 , 5 ]. However, malicious users (MUs) in the network may send incorrect data when uploading data to the fusion center (FC) to achieve their own purposes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing

Xie

Zhou

Gao

2023

Sensors

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In cognitive radio systems, cooperative spectrum sensing (CSS) can effectively improve the sensing performance of the system. At the same time, it also provides opportunities for malicious users (MUs) to launch spectrum-sensing data falsification (SSDF) attacks. This paper proposes an adaptive trust threshold model based on a reinforcement learning (ATTR) algorithm for ordinary SSDF attacks and intelligent SSDF attacks. By learning the attack strategies of different malicious users, different trust thresholds are set for honest and malicious users collaborating within a network. The simulation results show that our ATTR algorithm can filter out a set of trusted users, eliminate the influence of malicious users, and improve the detection performance of the system.

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DDPG with Transfer Learning and Meta Learning Framework for Resource Allocation in Underlay Cognitive Radio Network

Mishra

Srivastava

Sharan

2023

Wireless Pers Commun

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Spectrum-Agile Cognitive Radios Using Multi-Task Transfer Deep Reinforcement Learning

Cited by 12 publications

References 40 publications

Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band

Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band

Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing

DDPG with Transfer Learning and Meta Learning Framework for Resource Allocation in Underlay Cognitive Radio Network

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