Evaluation of Multi-Beam Massive MIMO Considering MAC Layer Using IEEE802.11ac and FDD-LTE

Massive multiple-input multiple-output (MIMO) transmission has attracted attention as a key technology for use in fifth-generation mobile communication systems. Multi-beam massive MIMO systems that apply beam selection in analog components and blind algorithms in digital components to eliminate the requirement for channel state information have been proposed as a method for reducing overhead. In this study, we developed an adaptive modulation scheme for implementing multi-beam massive MIMO and used computer simulation to compare it with digital and analog⁻digital hybrid beam-forming methods. The effectiveness of the proposed system was verified in a medium access control layer based on the IEEE802.11ac and frequency division duplex-LTE representative wireless communication standards

Mechanochemically assisted hydrothermal synthesis of Sn-substituted MFI-type silicates

Substitution of Al atoms in a zeolite framework by catalytic metal atoms has attracted considerable attention because the catalytic behavior can be tuned by the substituted atoms. In the present study, Sn-substituted MFI-type silicates were synthesized using a hydrothermal reaction of an amorphous Si-O-Sn precursor prepared by mechanochemical grinding of SiO2 and Sn(OH)4. The mechanochemical treatment was found to be a key technique for obtaining the amorphous Si-O-Sn precursor, where tetrahedral Sn4+ species were incorporated into the amorphous matrix. The Sn content in the framework of the MFI-type silicates was successfully controlled by the initial HCl/Si molar ratio of the hydrothermal procedures. Optical reflectance measurements revealed that the Sn4+ ions were dispersedly incorporated into the silicate framework while preserving the initial tetrahedrally coordinated species. Infrared results imply that the resulting Sn-substituted MFI-type silicate has Brønsted acid character. Precise control of the Brønsted and Lewis acid properties by Sn doping is a promising approach to the development of novel types of zeolite-based catalytic materials

Examples of prompts for English translation and answering medical questions.

A: A simple “Japanese prompt” used for answering Japanese questions. B: Simple “English prompts” used for Japanese-to-English translation and answering translated questions. C: Our optimized “English with optimized prompts”. The final optimized two-step prompts comprised a “system”, “sample output”, and “question input” sections. GPT was initially instructed to translate HTML-based Japanese questions into simple, direct, and improved English. In both processes, the system of requirement and an exemplary output scenario were provided within the prompts. In the question input section, the HTML-based Japanese questions were inputted to the English translation process, and sequentially, the English-translated questions were used to obtain the answers of the 117th NMLE questions.</p

Variations in the scoring rates across languages, prompt adjusting levels, and GPT models.

Translating the Japanese questions into English text improved the scoring rates; however, it increased the output error rate. Upon further adjusting the prompts, the scoring rates improved, and the output error decreased. Moreover, switching from the GPT-3.5 model to the GPT-4 model enhanced the scoring rates and almost eliminated errors.</p

Performance of optimal GPT-4 model with optimized prompt for the 117^th NMLE in Japan.

Performance of optimal GPT-4 model with optimized prompt for the 117th NMLE in Japan.</p

Study overview.

Questions from the 116th NMLE in Japan were used as the prompt optimization dataset and those from 117th NMLE were utilized as the performance-testing dataset after removing the image-based questions. During the prompt optimization process, questions from the prompt optimization dataset were input into GPT-3.5-turbo and GPT-4, using simple prompts in both Japanese and English along with optimized prompts in English. Subsequently, we evaluated the outputs from GPT-3.5-turbo and GPT-4 with optimized prompts. After adjusting the prompts, the GPT-4 model with the optimized prompts was tested on the performance-testing dataset (117th NMLE).</p