RL-IoT: Reinforcement Learning-Based Routing Approach for Cognitive Radio-Enabled IoT Communications

Malik, Tauqeer Safdar; Malik, Kaleem Razzaq; Afzal, Ayesha; Wang, Lei; Song, Houbing; Shah, Nadir

doi:10.1109/jiot.2022.3210703

Cited by 15 publications

(11 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reinforcement learning (RL), as an online autonomous learning technique, has demonstrated strong adaptability and effective problem-solving capabilities. It has been widely employed in various key technologies of cognitive radio (CR) such as resource allocation, anti-interference, and AMC [1]- [4] . Reference [5] combines multi-objective reinforcement learning and artificial neural networks to adjust radio parameters to improve decision-making efficiency.…”

Section: Introductionmentioning

confidence: 99%

Joint decision-making of communication waveform and power based on Q-learning

Wenchao,

Dou,

Chen

et al. 2024

Second International Conference on Informatics, Networking, and Computing (ICINC 2023)

View full text Add to dashboard Cite

This paper devises a decision-making method to address the varying communication requirements across diverse scenarios. The proposed method can objectively measure the performance of communication, and the weights of various indicators can be customized to adapt to different communication scenarios. Furthermore, in order to enhance communication performance, this paper has investigated a reinforcement learning models suitable for waveform and power decision-making, and proposes an explored action deletion algorithm based on the Q-learning method. The simulation results demonstrate that the proposed method outperforms existing methods in terms of convergence speed and algorithm accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Joint decision-making of communication waveform and power based on Q-learning

Wenchao,

Dou,

Chen

et al. 2024

Second International Conference on Informatics, Networking, and Computing (ICINC 2023)

View full text Add to dashboard Cite

show abstract

“…Internet of Things (IoT) has been considered as one of the key and enabling paradigms for a plethora of applications in healthcare, smart cities, and smart homes [1]. In the IoT era, various low-power wireless sensor devices are connected to the network and interact with each other [2].…”

Section: Introductionmentioning

confidence: 99%

“…• By exploiting the moment-matching approach, the proposed system's power outage probability, information outage probability, and sum throughput performances are derived in closed form over Nakagami-m fading channels 1 . The power and information outage probabilities are combined into a closed-form joint outage probability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Performance Analysis of RIS-Aided DCSK-WPC System With Energy Buffer

Xie

Cai

Kaddoum³

2023

IEEE Trans. Commun.

View full text Add to dashboard Cite

In this paper, we propose a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and energy buffer aided multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) system, which consists of a STAR-RIS, an access point (AP), and reflection users and transmission users with energy buffers. In the proposed system, the multi-antenna AP can transmit information and energy to several single-antenna reflection and transmission users simultaneously in a NOMA fashion, where the power transfer and information transmission states of the users are modeled using Markov chains. The reflection and transmission users harvest and store the energy in energy buffers as additional power supplies. The power outage probability, information outage probability, sum throughput, and joint outage probability closed-form expressions of the proposed system are derived over Nakagami-m fading channels, which are validated via simulations. Results demonstrate that the proposed system achieves better performance in comparison to the STAR-RIS aided MISO SWIPT-NOMA buffer-less, conventional RIS and energy buffer aided MISO SWIPT-NOMA, and STAR-RIS and energy buffer aided MISO SWIPT-time-division multiple access (TDMA) systems. Furthermore, a particle swarm optimization based power allocation (PSO-PA) algorithm is designed to maximize the sum throughput with a constraint on the joint outage probability. Simulation results illustrate that the proposed PSO-PA algorithm can achieve an improved sum throughput performance of the proposed system. Index Terms-Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), energy buffer, simultaneous wireless information and power transfer (SWIPT), non-orthogonal multiple access (NOMA), Markov chains.

show abstract

“…This allows secondary users (SUs) to use the spectrum vacancy to transmit. In other words, SUs dynamically accesses the licensed spectrum when (or where) it is temporally available [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

RL-IoT: Reinforcement Learning-Based Routing Approach for Cognitive Radio-Enabled IoT Communications

Cited by 15 publications

References 37 publications

Joint decision-making of communication waveform and power based on Q-learning

Joint decision-making of communication waveform and power based on Q-learning

Design and Performance Analysis of RIS-Aided DCSK-WPC System With Energy Buffer

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

Contact Info

Product

Resources

About