AI-Based Wireless Channel Prediction - Generalized Models for Adaptive Measurements and Predictions

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dc.contributor.authorChen, Bingcheng
dc.contributor.departmentChalmers tekniska högskola / Institutionen för elektrotekniksv
dc.contributor.examinerSvensson, Tommy
dc.contributor.supervisorWinges, Johan
dc.contributor.supervisorKumar Nagalapur, Keerthi
dc.contributor.supervisorSattari, Mehdi
dc.contributor.supervisorZhang, Xinlin
dc.date.accessioned2024-07-18T08:13:16Z
dc.date.available2024-07-18T08:13:16Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractIn 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precoder fed back by a User Equipment (UE) is used by a network for MIMO precoding, the received CSI/precoder can get outdated quickly, consequently resulting in a loss of user and system throughput due to the utilization of outdated precoders.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.coursecodeEENX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308312
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectArtificial intelligence
dc.subjectChannel state information
dc.subjectCSI prediction
dc.subjectMIMO
dc.titleAI-Based Wireless Channel Prediction - Generalized Models for Adaptive Measurements and Predictions
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeInformation and communication technology (MPICT​), MSc
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