Combining Multi-objective Evolutionary Approach and Machine Learning to Optimize PCI Configuration in Large-scale LTE Networks

Chen, Liuling; Cheng, Peng; Wang, Yuanting; Wen, Yinghong

doi:10.1109/iccet55794.2022.00014

Cited by 3 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, the authors of [32] also combined q-learning with MOPSO to realize parameter control, and the distance between the previous best position and the best position of the current population is used as the state for parameter selection. Based on NSGA-II, the authors of [33] utilized q-learning to adjust the crossover and mutation probabilities with population diversity, evolutionary iteration number, and average ftness, thereby enhancing population diversity. Te authors of [34] proposed a general framework of parameter control with reinforcement learning for single-objective evolutionary computation.…”

Section: Transfer Learning-based Parameter Control Methodmentioning

confidence: 99%

Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement Learning

Zhou,

Zhang,

Niu

et al. 2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

To address the challenge of parameter adjustment in complex environments, this paper introduces a transfer learning-based parameter control framework via deep reinforcement learning for multiobjective evolutionary algorithms (MOEAs). To avoid the requirement for accurate Pareto front information, this framework is proposed with comprehensive global-state information, including basic problem features, the relative position of individuals, the distribution of fitness value, and the grid-IGD. Building on this framework, four reinforced multiobjective evolutionary algorithms (r-MOEAs) are proposed and tested on four DTLZ benchmarks and eight WFG benchmarks. The results of the comparative analyses reveal that compared with the original MOEAs, the four r-MOEAs exhibit faster convergence and stronger robustness. It is also confirmed that our proposed parameter control framework has the capability to learn knowledge from different experiences and improve the performance of MOEAs.

show abstract

Section: Transfer Learning-based Parameter Control Methodmentioning

confidence: 99%

Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement Learning

Zhou,

Zhang,

Niu

et al. 2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Mwanje et al [2] examined the limits of PCI autoconfiguration in ultra-dense networks, shedding light on the challenges in such complex environments. The integration of machine learning and heuristic optimization has also been explored for PCI configuration, as demonstrated by Shahab et al [11], Wu et al [13], and Chen et al [14].…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Hybrid Approach for PCI Configuration and Optimization in 5G EN-DC Heterogeneous Networks

Li,

Yang,

Kim

et al. 2024

J Netw Syst Manage

View full text Add to dashboard Cite

With the development of 5G technologies and the implementation of EN-DC architecture in heterogeneous networks, managing Physical Cell Identity (PCI) has become increasingly complex. EN-DC, facilitating the coexistence of eNBs and gNBs, creates a densely populated environment that heightens the risk of PCI collisions and confusions. This study introduces a novel hybrid approach to PCI configuration in EN-DC networks, integrating centralized and distributed strategies. By organizing the network into clusters and employing newly introduced algorithms, Symmetrical Comparison (SC) and Symmetrical Triangular Cycling (STC), the method efficiently identifies and resolves PCI confusions. Simulations were conducted to evaluate the effectiveness of the proposed model under various scenarios, revealing its proficiency in preventing PCI confusion and 𝑚𝑜𝑑 30 collisions. The results underscore the critical role of PCI pool size and offer insights into network planning and optimization. Despite some challenges in handling specific collisions, such as 𝑚𝑜𝑑 3 and 𝑚𝑜𝑑 4, the study suggests that incorporating reinforcement learning techniques could provide more adaptive solutions, laying the foundation for future research in this area. The research contributes to the evolving landscape of 5G EN-DC networks, emphasizing the importance of intelligent design and meticulous planning in network management.

show abstract

Cluster-Based Hybrid Approach for PCI Configuration and Optimization in 5G EN-DC Heterogeneous Networks

Li,

Yang,

Kim

et al. 2023

Preprint

View full text Add to dashboard Cite

With the development of 5G technologies and the implementation of EN-DC architecture in heterogeneous networks, managing Physical Cell Identity (PCI) has become increasingly complex. EN-DC, facilitating the coexistence of eNBs and gNBs, creates a densely populated environment that heightens the risk of PCI collisions and confusions. This study introduces a novel hybrid approach to PCI configuration in EN-DC networks, integrating centralized and distributed strategies. By organizing the network into clusters and employing newly introduced algorithms, Symmetrical Comparison (SC) and Symmetrical Triangular Cycling (STC), the method efficiently identifies and resolves PCI confusions. Simulations were conducted to evaluate the effectiveness of the proposed model under various scenarios, revealing its proficiency in preventing PCI confusion and \(mod 30\) collisions. The results underscore the critical role of PCI pool size and offer insights into network planning and optimization. Despite some challenges in handling specific collisions, such as \(mod 3\) and \(mod 4\), the study suggests that incorporating reinforcement learning techniques could provide more adaptive solutions, laying the foundation for future research in this area. The research contributes to the evolving landscape of 5G EN-DC networks, emphasizing the importance of intelligent design and meticulous planning in network management.

show abstract

Combining Multi-objective Evolutionary Approach and Machine Learning to Optimize PCI Configuration in Large-scale LTE Networks

Cited by 3 publications

References 17 publications

Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement Learning

Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement Learning

Cluster-Based Hybrid Approach for PCI Configuration and Optimization in 5G EN-DC Heterogeneous Networks

Cluster-Based Hybrid Approach for PCI Configuration and Optimization in 5G EN-DC Heterogeneous Networks

Contact Info

Product

Resources

About