Resilient Topology Design for Wireless Backhaul: A Deep Reinforcement Learning Approach

Abdelmoaty, Ahmed; Naboulsi, Diala; Dahman, Ghassan; Poitau, Gwenael; Gagnon, François

doi:10.1109/lwc.2022.3207358

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…The work by Abdelmoaty et al [115] provides valuable insights into the sub-optimality concern. In their study, the authors conducted a comparative analysis between two approaches: resilient topology design for wireless backhaul using integer linear programming (ILP) and employing a DRL-based method.…”

Section: Sub-optimal Solutions In Drl Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

Gautam

2023

Electricity

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In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.

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Section: Sub-optimal Solutions In Drl Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

Gautam

2023

Electricity

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“…Accordingly, mobile networks witness a huge demand in terms of larger data rates and massive connected devices. It is anticipated for global mobile data traffic to margin 230 exabytes (EB) per month, and the connected devices to pass 90 million by 2026 [1].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based SC-FDMA Channel Equalization

Mohamed

2024

IJEETC

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It is very challenging to design an effective wireless communication system. That’s because of numerous factors affecting the performance of a typical wireless communication system, such as nonlinear channel distortions and impairments. single carrier frequency division multiple access (SC-FDMA) is a multiple access scheme that is an important part of the long-term evolution (LTE) standard for uplink transmission. An advanced mobile radio system’s multiple access schemes should indeed meet stringent requirements, such as a low bit error rate (BER). In this article, we investigate the equalization problem for nonlinear channel distortions and impairments using deep neural networks (NN). We introduce a novel combined deep neural network channel equalization and symbol detection scheme based on a deep learning (DL) recurrent feedback (RF) long short-term memory (LSTM) neural network to achieve blind equalization and decoding for SC-FDMA systems without knowing the channel state information (CSI). To train the model efficiently, the training data is gathered by simulation, with channel effects and noise treated as a complete black box. CSI and constellation demapping are learned by a deep neural network (DNN) model. Then, the frequency-domain sequences that have been corrupted are implicitly equalized to get the broadcasted signal back. Our specified SC-FDMA system, which uses a quadrature phase-shift keying (QPSK) modulation method and the suggested Deep Learning-based model channel equalizer, performs better than the existing equalizers by an average of 1 to 4 dB at moderate signal-to-noise (SNR) ratios, according to simulation data. A complexity comparison between the proposed and the conventional equalizers was conducted in terms of training time, execution time, and number of operations. On combined channel equalization and symbol detection, the suggested system delivers state-of-the-art performance.

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“…The resilient of data transmission is severely limited not only by these characteristics but also by the possibility of malicious or unreliable nodes joining the communication process [3]. For these reasons, there is a need to provide resilient data transmission and a resilient routing process to eliminate these characteristics and situations and to ensure resilient communication in the discovery and maintenance phases of the communication path, but also in the data transmission phase itself [4,5]. To achieve these goals and requirements, a resilient routing algorithm based on decentralized blockchain technology and artificial intelligence (AI) has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Resilient enhancements of routing protocols in MANET

Baumgartner,

Papaj,

Kurkina

et al. 2024

Peer-to-Peer Netw. Appl.

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Resilient of routing processes is one of the biggest challenges for data transmission in mobile networks without infrastructure. Communication under current routing protocols is through a communication path that, although the shortest, may not perform satisfactorily in terms of resilient. Routing and communication within such a path may take place using nodes that are malicious or inappropriate in the communication process due to malicious or poor technical state. This paper presents a new algorithm for various uses of mobile ad hoc networks not only in edge networks with infrastructure but also with the possibility of being used in the cloud solutions. We have modified decentralized blockchain technology and artificial intelligence using deep learning methods that have been implemented in routing processes. The objective of this algorithm was to select the most resilient communication path from the source to the destination node. Such a communication path selection consisted of selecting the nodes that were most suitable in terms of resilience, where the selection nodes was provided through a network and technical parameters. The key quality of service metrics, throughput, total delay, number of delivered signaling and data packets and the ratio between them were used to evaluate the proposed resilient routing algorithm. Modified resilient routing protocols achieved improvement in all the analyzed parameters compared to the original routing protocols. The improvement in these parameters led to an increase in the resilience of the routing process based on the actual data obtained from each node in the network and previous communications.

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Resilient Topology Design for Wireless Backhaul: A Deep Reinforcement Learning Approach

Cited by 7 publications

References 10 publications

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

Deep Learning-Based SC-FDMA Channel Equalization

Resilient enhancements of routing protocols in MANET

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