Macrocell Protection Interference Alignment in Two-Tier Downlink Heterogeneous Networks

Conventional interference alignment (IA) has been developed to mitigate interference problems for the coexistence of picocells and macrocells. This paper proposes a macrocell protection interference alignment (MCP-IA) in two-tier MIMO downlink heterogeneous networks. The proposed method aligns the interference of the macro user equipment (UE) and mitigates the interference of the pico-UEs with a minimum mean squared error interference rejection combining (MMSE-IRC) receiver. Compared to the conventional IA, the proposed MCP-IA provides an additional array gain obtained by the precoder design of the macro BS and a diversity gain achieved by signal space selections. The degrees of freedom (DoF) of the proposed MCP-IA are equal to or greater than that of the conventional IA and are derived theoretically. Link level simulations show the link capacity and the DoF of the macro UE, and also exhibit the proposed MCP-IA attaining additional array gain and diversity gain. The system level simulation illustrates that the proposed method prevents the interference of the macro UE completely and preserves the throughput of the pico-UE irrespective of the number of picocells. For 4 × 2 antenna configuration, the system level simulation demonstrates that the proposed MCP-IA throughput of the macro UE is not affected by the number of picocells and that the proposed MCP-IA throughput of the picocells approaches that of single-user MIMO (SU-MIMO) with a 3% loss

ウマの骨折に対する生体組織工学及びロッキングコンプレッションプレートを用いた骨折治療に関する研究

博士(獣医学)岐阜大学(Gifu University

Macrocell Protection Interference Alignment in Two-Tier Downlink Heterogeneous Networks

Conventional interference alignment (IA) has been developed to mitigate interference problems for the coexistence of picocells and macrocells. This paper proposes a macrocell protection interference alignment (MCP-IA) in two-tier MIMO downlink heterogeneous networks. The proposed method aligns the interference of the macro user equipment (UE) and mitigates the interference of the pico-UEs with a minimum mean squared error interference rejection combining (MMSE-IRC) receiver. Compared to the conventional IA, the proposed MCP-IA provides an additional array gain obtained by the precoder design of the macro BS and a diversity gain achieved by signal space selections. The degrees of freedom (DoF) of the proposed MCP-IA are equal to or greater than that of the conventional IA and are derived theoretically. Link level simulations show the link capacity and the DoF of the macro UE, and also exhibit the proposed MCP-IA attaining additional array gain and diversity gain. The system level simulation illustrates that the proposed method prevents the interference of the macro UE completely and preserves the throughput of the pico-UE irrespective of the number of picocells. For 4×2 antenna configuration, the system level simulation demonstrates that the proposed MCP-IA throughput of the macro UE is not affected by the number of picocells and that the proposed MCP-IA throughput of the picocells approaches that of single-user MIMO (SU-MIMO) with a 3% loss

Design and Implementation of an HCPS-Based PCB Smart Factory System for Next-Generation Intelligent Manufacturing

The next-generation intelligent smart factory system that is proposed in this paper could improve product quality and realize flexible, efficient, and sustainable product manufacturing by comprehensively improving production and management innovation via its digital network and intelligent methods that reflect the characteristics of its printed circuit board (PCB) manufacturing design and on-site implementation. Intelligent manufacturing systems are complex systems that are composed of humans, cyber systems, and physical systems and aim to achieve specific manufacturing goals at an optimized level. Advanced manufacturing technology and next-generation artificial intelligence (AI) are deeply integrated into next-generation intelligent manufacturing (NGIM). Currently, the majority of PCB manufacturers are firms that specialize in processing orders from leading semiconductor and related product manufacturers, such as Samsung Electronics, TSMC, Samsung Electro-Mechanics, and LG Electronics. These top companies have been responsible for all product innovation, intelligent services, and system integration, with PCB manufacturers primarily playing a role in intelligent production and system integration. In this study, the main implementation areas were divided into manufacturing execution system (MES) implementation (which could operate the system using system integration), data gathering, the Industrial Internet of Things (IIoT) for production line connection, AI and real-time monitoring, and system implementation that could visualize the collected data. Finally, the prospects of the design and on-site implementation of the next-generation intelligent smart factory system that detects and controls the occurrence of quality and facility abnormalities are presented, based on the implementation system