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Visar 1 - 5 av 2026
  • Post
    AI-Based Wireless Channel Prediction - Generalized Models for Adaptive Measurements and Predictions
    (2024) Chen, Bingcheng; Chalmers tekniska högskola / Institutionen för elektroteknik; Svensson, Tommy; Winges, Johan; Kumar Nagalapur, Keerthi; Sattari, Mehdi; Zhang, Xinlin
    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precoder fed back by a User Equipment (UE) is used by a network for MIMO precoding, the received CSI/precoder can get outdated quickly, consequently resulting in a loss of user and system throughput due to the utilization of outdated precoders.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.
  • Post
    Holistic Diagnosis via Multimodal Foundation Models
    (2024) Pauli, Oskar; Chalmers tekniska högskola / Institutionen för elektroteknik; Graell i Amat, Alexandre; Ceccobello, Chiara; Östman, Johan
    The healthcare domain has data in many different forms, or modalities. They can be in the form of x-ray images, time-series of certain events like heart rate or blood pressure, textual data from notes etc. Medical practitioners uses many different modalities every day to make informed and sound decisions. With the recent success of small and large language models, it is natural to try and incorporate them with multimodal capabilities in the healtcare domain. This thesis seeks to investigate how well small language models can perform on predictive tasks in healthcare using multimodal data. To explore this, projectors that project data from different sources to the embedding space of a language model was developed. While the results show that a multimodal language model is better than a single-sourced version, it is still being outperformed by the XGBoost model. Even though it is being outperformed, the model proposed shows promise in regards to generalizability, potentially streamlining predictive tasks in healthcare. The thesis argue that even if improvements needs to be made and the challenges it poses can be difficult to handle, further advancements can lead to facilitating medical practitioners in a very efficient way.
  • Post
    Sensing and Communicataion using FMCW Radar
    (2024) Janetzko, Leon; Rex, Alexander; Chalmers tekniska högskola / Institutionen för elektroteknik; Wymeersch, Henk
  • Post
    Perception and Decision-making with YOLO and ChatGPT Integration for Intelligent Robot Navigation
    (2024) Krishnamurthi, Suraj; Hjertvist, Oscar; Chalmers tekniska högskola / Institutionen för elektroteknik; Ramirez-Amaro, Karinne
  • Post
    Stochastic MPC for Autonomous Vehicles in Uncertain Situations
    (2024) Zhang, Qun; Salih, Saeed Ponnarikkal; Chalmers tekniska högskola / Institutionen för elektroteknik; Murgovski, Nikolce; Börve, Erik; Laine, Leo
    Abstract In this thesis, an Model Predictive Control (MPC) based trajectory planning algorithm is first introduced for controlling trucks on highways. Given the uncertainties that exist between theoretical models and real vehicles, this study further analyzes these uncertainties and proposes an Stochastic Model Predictive Control (SMPC) based trajectory planning algorithm. The algorithm avoids collisions by tightening constraints and is validated in the CARLA simulation environment. Experimental results show that the SMPC-based trajectory planning algorithm has obvious advantages in terms of safety performance compared with the standard MPC. However, the method also sacrifices certain driving performance and increases computational complexity, which is mainly due to the tightened traffic constraints. This study not only verifies the effectiveness of SMPC in handling uncertainty and enhancing safety but also provides both an experimental and theoretical basis for future work.