LLM4CP Training and Testing Dataset

This dataset is from the paperLLM4CP: Adapting Large Language Models for Channel PredictionThe training and validation datasets contain 8,000 and 1,000 samples respectively, and the user speeds are evenly distributed between 10 and 100 km/h. The test dataset contains 10 speeds ranging from 10 km/h to 100 km/h, with 1,000 samples for each speed.

During the experimental phase of the study, the team used the QuaDRiGa simulator to generate a time-varying channel dataset that complies with the 3GPP standard for performance verification.

The team set up a MISO-OFDM system with a dual-polarized UPA (uniform planar array) on the base station side and a single omnidirectional antenna on the user side, with the antenna spacing being half the wavelength at the center frequency. The bandwidth of both the uplink and downlink channels is 8.64 MHz, and the pilot frequency spacing is 180 kHz. For both TDD and FDD modes, the center frequencies of both the uplink and downlink channels are set to 2.4 GHz. For FDD mode, the uplink and downlink channels are adjacent. The research team set the pilot frequency spacing time to 0.5 ms in the prediction experiment.

• TDD: It is a duplex mode of a communication system, used to separate the receiving and transmitting channels in mobile communication systems.

• FDD: refers to the uplink (mobile station to base station) and downlink (base station to mobile station) operating on two separate frequencies (with certain frequency spacing requirements).

The study considers the 3GPP urban macro channel model and non-line-of-sight scenarios. The number of clusters is 21, and the number of paths in each cluster is 20. The initial position of the user is randomized, and the movement trajectory is set to linear.

The research results will be published in 2024 asLLM4CP: Adapting Large Language Models for Channel Prediction" was invited to be published in the Journal of Communications and Information Networks. Peking University is the first completion unit of this research work, and the collaborating units include the Hong Kong University of Science and Technology (Guangzhou) and the Hong Kong University of Science and Technology. Professor Cheng Xiang is the corresponding author of the paper, and Peking University doctoral student Liu Boxun is the first author.

HyperAI Super Neural NetworkFor the first time! GPT-2 empowers the physical layer of wireless communications, and the Peking University team proposes a channel prediction solution based on pre-trained LLM"A detailed paper interpretation of the research was given in the title.