Neural networks based Physical Cell Identity assignment for self organized 3GPP Long Term Evolution

Shahab, Muhammad Basit; Bhatti, Abdul Aziz

doi:10.1109/tsp.2012.6256276

Cited by 5 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[0~215], 𝑛𝑜𝑑𝑒𝑇𝑦𝑝𝑒 (𝑟𝑃𝑜𝑠𝑋∈ [1,11],𝑟𝑃𝑜𝑥𝑌∈ [1,17]) , 𝑃𝐶𝐼 𝑖𝑛𝑖𝑡𝑖𝑎𝑙 = −1). The size of 𝑁𝑜𝑑𝑒𝑀𝑎𝑝𝐿𝑖𝑠𝑡 is determined by the number of base stations according to Equation (2).…”

Section: Simulation and Analysismentioning

confidence: 99%

“…Several approaches to PCI allocation have been proposed, ranging from graph coloring-based methods [3][4]10] to neural network-based techniques [11]. For instance, Bandh et al [3] mapped the PCI assignment problem to graph coloring, offering an efficient solution for initial assignment and network growth.…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…Shahab et al [11] proposed a neural network-based graph coloring technique for PCI assignments in self-organized LTE networks, highlighting its efficiency. Wu et al [13] proposed a heuristic optimization model for PCI allocation in real-life LTE networks, demonstrating its effectiveness in reducing interference.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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