A relay selection scheme using Q-learning algorithm in cooperative wireless communications

Jung, Hong-Gyu; Kim, Kwang‐Yul; Kim, Junsu; Shin, Oh‐Soon; Shin, Yoan

doi:10.1109/apcc.2012.6388091

Cited by 15 publications

(14 citation statements)

References 8 publications

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“…Specifically, the forward propagation algorithms apply the carefully trained weight matrixes and bias vectors for carrying out the associated linear and [294] reduced-state SARSA cellular network dynamic channel allocation considering both mobile traffic and call handoffs. [295] on-policy SARSA CR network distributed multiagent sensing policy relying on local interactions among SU [296] on-policy SARSA MANET energy-aware reactive routing protocol for maximizing network lifetime [297] on-policy SARSA HetNet resource management for maximizing resource utilization and guaranteeing QoS [298] approximate SARSA P2P network energy harvesting aided power allocation policy for maximizing the throughput [299] Q-learning WBAN power control scheme to mitigate interference and to improve throughput [300] Q-learning OFDM system adaptive modulation and coding not relying on off-line training from PHY [301] Q-learning cooperative network efficient relay selection scheme meeting the symbol error rate requirement [302] decentralized Q-learning CR network aggregated interference control without introducing signaling overhead [303] convergent Q-learning WSN sensors' sleep scheduling scheme for minimizing the tracking error activation operations. By contrast, the backward propagation algorithms, which are widely used in the industrial field define a so-called loss function for quantifying the difference between the output produced by the training samples' and the real output.…”

Section: Deep Learning In Wireless Networkmentioning

confidence: 99%

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

469

200

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Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (Het-Nets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

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Section: Deep Learning In Wireless Networkmentioning

confidence: 99%

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

469

200

View full text Add to dashboard Cite

show abstract

“…Q-learning is also used for relay selection based on physical layer parameters in [28]. Motivated by the works in [10], [27]- [29], we propose a cross-layer relay selection scheme using Q-learning that maximizes the link layer throughput.…”

Section: Introductionmentioning

confidence: 99%

Learning-based relay selection for cooperative networks

Saha

Ghosh

Hamouda

2014

2014 IEEE Global Communications Conference

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We investigate a cross-layer relay selection scheme based on Q-learning algorithm. For the study, we consider multirelay adaptive decode and forward (DF) cooperative-diversity networks over multipath time-varying Rayleigh fading channels. The proposed scheme selects relay subsets that maximizes the link layer transmission efficiency without having knowledge of channel state information (CSI). Results show that the proposed scheme outperforms the capacity based cooperative transmission with the same number of reliable relays in terms of transmission efficiency gain. Furthermore, the proposed scheme is shown to offer high bandwidth efficiency from low to high signal-to-noise ratios (SNRs).

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“…This paper uses a Q-learning based relay selection scheme to select an optimal relay and reduce the latency in multi-hop wireless networks. Nowadays, there are many relay selection schemes using reinforcement learning in cooperative networks [9][10][11][12][13]. These papers are focused on using reinforcement learning to improve performance through selected relays.…”

Section: Introductionmentioning

confidence: 99%

A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks

Paek

Lee

et al. 2020

Applied Sciences

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In wireless communication systems, reliability, low latency and power are essential in large scale multi-hop environment. Multi-hop based cooperative communication is an efficient way to achieve goals of wireless networks. This paper proposes a relay selection scheme for reliable transmission by selecting an optimal relay. The proposed scheme uses a signal-to-noise ratio (SNR) based Q-learning relay selection scheme to select an optimal relay in multi-hop transmission. Q-learning consists of an agent, environment, state, action and reward. When the learning is converged, the agent learns the optimal policy which is a rule of the actions that maximize the reward. In other words, the base station (BS) knows the optimal relay to select and transmit the signal. At this time, the cooperative communication scheme used in this paper is a decode-and-forward (DF) scheme in orthogonal frequency division multiplexing (OFDM) system. The Q-learning in the proposed scheme defines an environment to maximize a reward which is defined as SNR. After the learning process, the proposed scheme finds an optimal policy. Furthermore, this paper defines a reward which is based on the SNR. The simulation results show that the proposed scheme has the same bit error rate (BER) performance as the conventional relay selection scheme. However, this paper proposes an advantage of selecting fewer relays than conventional scheme when the target BER is satisfied. This can reduce the latency and the waste of resources. Therefore, the performance of the multi-hop transmission in wireless networks is enhanced.

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A relay selection scheme using Q-learning algorithm in cooperative wireless communications

Cited by 15 publications

References 8 publications

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Learning-based relay selection for cooperative networks

A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks

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