2021 8th International Conference on Computer and Communication Engineering (ICCCE) 2021
DOI: 10.1109/iccce50029.2021.9467158
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User Association for Multi-Tenant Heterogeneous Network Slicing Using Genetic Algorithm

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Cited by 4 publications
(5 citation statements)
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“…In Table 10, we categorize the reviewed proposals based on the ML technique and the type and number of used algorithms. According to our analysis of the literature review on resource management in RAN slicing, as shown in T-NN 1 Evolutionary Algorithms [65], [66], [68], [69], [67], [70], [72], [71] GA 8 [35], [79], [80], [81], [129], [130], [95] AC 7 [78] MAB 1 [111], [85], [117], [86], [87], [88], [136], [90], [96], [97], [98], [151], [126], [99], [123], [131], [114], [118], [94], [100], [103], [119], [104], [105] DQN 25 [109], [108] Q-Learning 2 Reinforcement Learning [115], [112], [84] Double DQN 3 …”
Section: Discussion and Future Research Directionsmentioning
confidence: 99%
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“…In Table 10, we categorize the reviewed proposals based on the ML technique and the type and number of used algorithms. According to our analysis of the literature review on resource management in RAN slicing, as shown in T-NN 1 Evolutionary Algorithms [65], [66], [68], [69], [67], [70], [72], [71] GA 8 [35], [79], [80], [81], [129], [130], [95] AC 7 [78] MAB 1 [111], [85], [117], [86], [87], [88], [136], [90], [96], [97], [98], [151], [126], [99], [123], [131], [114], [118], [94], [100], [103], [119], [104], [105] DQN 25 [109], [108] Q-Learning 2 Reinforcement Learning [115], [112], [84] Double DQN 3 …”
Section: Discussion and Future Research Directionsmentioning
confidence: 99%
“…In addition, the problem of frequency interference in inter-cells, intra-cells, inter-RANs, or intra-RANs should be considered. Although some proposals such as [40], [68], [69], [67], [72], [80], [81], [115], [112], [85], [86], [87], [88], [136], [98], [113], [153], [144], [155], [114], [118], [109], [119], [105], [157] have addressed the dynamic power allocation issue, presenting an efficient dynamic power allocation method with a low computational and time complexity is a vital requirement. Possible Solution: Dynamic power allocation in existing proposals has been done in two manners: 1) power allocation with an iterative method using Lagrangian coefficients taking into account channel conditions and interference (e.g., [81], [117], [119]) 2) considering power as an action in online algorithms and selecting the desired power by the agent for each user, evaluating the selected power in accordance with the channel conditions and interference such as [118].…”
Section: Discussion and Future Research Directionsmentioning
confidence: 99%
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“…Firstly, the isolation property of slices was used to eliminate the interference between base stations, while user access control algorithms were adopted to find a stable match between user devices and different network slices. The authors of [21] pointed out that user access based on the maximum signal-to-noise ratio was not an effective access control strategy. Therefore, the authors investigated the user access problem for multi-tenant network slices in heterogeneous networks combined with fairness, quality of service, energy consumption and energy efficiency aspects while considering the priority of tenants and users, and utilized genetic algorithms were implemented for user access control to maximize the weighting and rate.…”
Section: Related Workmentioning
confidence: 99%