Deep learning through evolution: A hybrid approach to scheduling in a dynamic environment

Fagan, David; Fenton, Michael; Lynch, David; Kučera, Štěpán; Claußen, Holger; O’Neill, Michael

doi:10.1109/ijcnn.2017.7965930

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…This entity keeps track of the user device, and there can only be one MME connected to a device at a time. The S-GW takes care of the data packet routing, forwarding, and manages mobility between LTE and other networks [83]- [85]. This component also allows for the replication of user data for lawful interception.…”

Section: A Lte Network Architecturementioning

confidence: 99%

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A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

et al. 2021

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The Machine-to-Machine (M2M) communication refers to an autonomous communication among devices that aims for a massive number of connected devices. M2M communication can support ubiquitous communication and full mechanical automation, and it will change everything from industry to ourselves. Recent developments in communication technology make Long Term Evolution (LTE)/Long Term Evolution-Advance (LTE-A) a promising technology for supporting M2M communication. LTE can support the diverse characteristic of M2M communication due to its IP connectivity, coverage area, and scalability. Therefore, the LTE schedulers should satisfy the need for M2M communication.Motivated from these facts, in this paper, we present a survey on classification of LTE / LTE-A scheduling methodologies from the perspective of M2M communication. We classify the schedulers based on their objectives, such as energy efficiency, spectrum efficiency, group-based, and Quality-of-Service (QoS) support for Machine Type Communication Devices (MTCDs). We also highlight the scope of future research direction for the scheduling work.

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Section: A Lte Network Architecturementioning

confidence: 99%

“…Fagan et al [85] applied deep learning approach for downlink scheduling. The data-set is derived using a genetic algorithm over many simulated random UE data reports and used to train the deep learning network.…”

Section: Hybrid / Multi-objective Techniquesmentioning

confidence: 99%

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

et al. 2021

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“…Conventional methods for static scheduling include genetic algorithm, [7][8][9] particle swarm, 10,11 heuristic search, 12,13 and so on. Dynamic scheduling algorithms include load balancing, [14][15][16] fuzzy stochastic optimization, 10,17 deep reinforcement learning, 18,19 and so on. A heuristic search algorithm is adopted to find the optimal reconfiguration scheme for the target security protocol in given solution space.…”

Section: Related Workmentioning

confidence: 99%

HiCoACR: A reconfiguration decision-making model for reconfigurable security protocol based on hierarchically collaborative ant colony

Yi¹,

Liao

Du³

et al. 2020

International Journal of Distributed Sensor Networks

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Reconfigurable security protocols, with dynamic protocol configuration and flexible resource allocation, have become a state-of-the-art technology to guarantee the security of space-ground integrated network. However, reconfiguration decision-making for reconfigurable security protocols remains a major challenge in order to adapt to diverse secure service requirements and deploy higher security level but more complicated security strategies in nodes with limited resources and computing abilities. To handle this problem commendably, a hierarchically collaborative ant colony–based reconfiguration decision-making model called HiCoACR is proposed. This model, inspired by the ideas of hierarchical reinforcement learning and population collaboration, decomposes the reconfiguration decision-making problem into two sub-problems by introducing a two-level hierarchy ant colony consisting of the Explorer and the Worker. The Explorer controls directions of protocol reconfiguration and generates abstract scheduling sub-goals which are conveyed from the Worker. While the Worker schedules most suitable cryptogram resources for each sub-goal received and produces the optimal reconfiguration solution which is verified and re-optimized by a Lévy process–based stochastic gradient descent algorithm. Both the Explorer and the Worker adopt a modified version of ant colony algorithm to fulfill its targets, where a hierarchical pheromone is defined to reinforce positive behaviors of each ant colony. Experiment results suggest that HiCoACR outperforms baseline algorithms and possesses well model transferability.

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A Hierarchical Approach to Grammar-Guided Genetic Programming: The Case of Scheduling in Heterogeneous Networks

Saber

Fagan

Lynch

et al. 2018

Theory and Practice of Natural Computing

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Deep learning through evolution: A hybrid approach to scheduling in a dynamic environment

Cited by 7 publications

References 18 publications

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

HiCoACR: A reconfiguration decision-making model for reconfigurable security protocol based on hierarchically collaborative ant colony

A Hierarchical Approach to Grammar-Guided Genetic Programming: The Case of Scheduling in Heterogeneous Networks

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