Approaching the cell switch-off problem in 5G ultra-dense networks with dynamic multi-objective optimization

Luna, Francisco; Cano, Pablo H. Zapata; Gonzalez-Macias, Juan C.; Valenzuela-Valdés, Juan F.

doi:10.1016/j.future.2019.10.005

Cited by 13 publications

(3 citation statements)

References 27 publications

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“…As a last experiment, the beam switch-off problem is considered [37]. It is desired to know how the coverage in the factory degrades if some of the 27 SSBs available in the transmitter are turned off (configuration B).…”

Section: Beam-specific Coverage and Switch-off Optimization Studymentioning

confidence: 99%

FR2 5G Networks for Industrial Scenarios: Experimental Characterization and Beam Management Procedures in Operational Conditions

Ramírez-Arroyo,

López,

Rodríguez

et al. 2024

IEEE Trans. Veh. Technol.

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Industrial environments constitute a challenge in terms of radio propagation due to the presence of machinery and the mobility of the different agents, especially at mmWave bands. This paper presents an experimental evaluation of a FR2 5G network deployed in an operational factory scenario at 26 GHz. The experimental characterization, performed with autonomous mobile robots that self-navigate the industrial lab, leads to the analysis of the received power along the factory and the evaluation of reference path gain models. The proposed assessment deeply analyzes the physical layer of the communication network under operational conditions. Thus, two different network configurations are assessed by measuring the power received in the entire factory, providing a comparison between deployments. Additionally, beam management procedures, such as beam recovery, beam sweeping or beam switching, are analyzed since they are crucial in environments where mobile agents are involved. They aim for a zero interruption approach based on reliable communications. The results analysis shows that beam recovery procedures can perform a beam switching to an alternative serving beam with power losses of less than 1.6 dB on average. Beam sweeping analysis demonstrates the prevalence of the direct component in Line-of-Sight conditions despite the strong scattering component and large-scale fading in the environment.

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Section: Beam-specific Coverage and Switch-off Optimization Studymentioning

confidence: 99%

FR2 5G Networks for Industrial Scenarios: Experimental Characterization and Beam Management Procedures in Operational Conditions

Ramírez-Arroyo,

López,

Rodríguez

et al. 2024

IEEE Trans. Veh. Technol.

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show abstract

“…The 5G is an upcoming technology intended to provide any type of service, anywhere and anytime [8], [9]. The proposed data rates of 5G are up to 50x (1 Gbps at the cell edge and 10+ Gbps in other areas) with 10x latency improvement and reductions in energy and cost similar to those of the previous generations [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

Vision towards 5G: Comparison of radio propagation models for licensed and unlicensed indoor femtocell sensor networks

Shabbir

Kütt

Alam

et al. 2021

Physical Communication

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“…In recent years, dynamic optimization has caught the rising attention of researchers. Compared with static multiobjective optimization, dynamic multi-objective problems (DMOPs) are much closer to real-world application, like 5G ultra-dense networks [1], water quality anomaly detection [2], multi-crane-scheduling problem [3], and cycling energy systems [4]. However, it is worth mentioning that DMOPs contain dynamic characteristics, including changing Pareto front, Pareto set, the number of objectives or other parameters.…”

Section: Introductionmentioning

confidence: 99%

Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization

Zhang

Wang

2021

Complex Intell. Syst.

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Multi-objective problems in real world are often contradictory and even change over time. As we know, how to find the changing Pareto front quickly and accurately is challenging during the process of solving dynamic multi-objective optimization problems (DMOPs). In addition, most solutions obey different distributions in decision space and the performance of NSGA-III when dealing with DMOPs should be further improved. In this paper, centroid distance is proposed and combined into NSGA-III with transfer learning together for DMOPs, called TC_NSGAIII. Centroid distance-based strategy is regarded as a prediction method to prevent some inappropriate individuals through measuring the distance of the population centroid and reference points. After the distance strategy, transfer learning is used for generating an initial population using the past experience. To verify the effectiveness of our proposed algorithm, NSGAIII, Tr_NSGAIII (NSGA-III combining with transfer learning only), Ce_NSGAIII (NSGA-III combining with centroid distance only), and TC_NSGAIII are compared. Seven state-of-the-art algorithms have been used for comparison on CEC 2015 benchmarks. Besides, transfer learning and centroid distance are regarded as a dynamic strategy, which is incorporated into three static algorithms, and the performance improvement is measured. What’s more, twelve benchmark functions from CEC 2015 and eight sets of parameters in each function are used in our experiments. The experimental results show that the performance of algorithms can be greatly improved through the proposed approach.

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Approaching the cell switch-off problem in 5G ultra-dense networks with dynamic multi-objective optimization

Cited by 13 publications

References 27 publications

FR2 5G Networks for Industrial Scenarios: Experimental Characterization and Beam Management Procedures in Operational Conditions

FR2 5G Networks for Industrial Scenarios: Experimental Characterization and Beam Management Procedures in Operational Conditions

Vision towards 5G: Comparison of radio propagation models for licensed and unlicensed indoor femtocell sensor networks

Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization

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