AI-Based Wireless Channel Prediction - Generalized Models for Adaptive Measurements and Predictions
dc.contributor.author | Chen, Bingcheng | |
dc.contributor.department | Chalmers tekniska högskola / Institutionen för elektroteknik | sv |
dc.contributor.examiner | Svensson, Tommy | |
dc.contributor.supervisor | Winges, Johan | |
dc.contributor.supervisor | Kumar Nagalapur, Keerthi | |
dc.contributor.supervisor | Sattari, Mehdi | |
dc.contributor.supervisor | Zhang, Xinlin | |
dc.date.accessioned | 2024-07-18T08:13:16Z | |
dc.date.available | 2024-07-18T08:13:16Z | |
dc.date.issued | 2024 | |
dc.date.submitted | ||
dc.description.abstract | In wireless communication systems, channels can be time-varying, and their conditions can change due to factors such as mobility, interference, and environmentalconditions, which brings challenges in maintaining communication reliability. Hence, acquiring channel state information (CSI) is a critical step in physical layer of wireless communication. However, when accurate CSI or precoder fed back by a User Equipment (UE) is used by a network for MIMO precoding, the received CSI/precoder can get outdated quickly, consequently resulting in a loss of user and system throughput due to the utilization of outdated precoders. In a feature introduced in 3GPP Rel-18, a UE can be configured to measure multiple instances of the channel, use them to predict a number of channel/precoder instances in the future and report the predictions to the network. The performance of such a scheme depends highly on the ability to predict the channel with high accuracy. In this study, we explored AI-based methods for channel prediction to address this challenge. By utilizing a past window size of measured channel sequences, the proposed method forecasts future channel conditions. Out of all the AI models tested, the Transformer Encoder-Only model demonstrated superior performance, its counterparts and a classical non-AI based autoregressive (AR) model. We also found that, training the model with a diverse dataset with a mix of UE velocities, embedding across the time dimension within the Transformer EncoderOnly model, and constructing the model within the complex domain yields enhanced generalization capability. Furthermore, we developed a generalized model capable of using varying number of channel measurements to predict varying number of channel instances in the future. | |
dc.identifier.coursecode | EENX30 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12380/308312 | |
dc.language.iso | eng | |
dc.setspec.uppsok | Technology | |
dc.subject | Artificial intelligence | |
dc.subject | Channel state information | |
dc.subject | CSI prediction | |
dc.subject | MIMO | |
dc.title | AI-Based Wireless Channel Prediction - Generalized Models for Adaptive Measurements and Predictions | |
dc.type.degree | Examensarbete för masterexamen | sv |
dc.type.degree | Master's Thesis | en |
dc.type.uppsok | H | |
local.programme | Information and communication technology (MPICT​), MSc |
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