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    Performance optimization of multiple RIS-assisted multiuser MIMO communication systems
    (Wiley, 2025-05-16)
    Francisco Rubén Castillo Soria
    ;
    Roilhi Frajo Ibarra‐Hernández
    ;
    Carlos Adrián Gutiérrez Diaz de León
    ;
    Abel García Barrientos
    ;
    Sharon Macias‐Velasquez
    Integrating reconfigurable intelligent surfaces (RISs) into wireless communication systems represents a crucial challenge. This task can be even more challenging when dealing with multiple users connected to a system sharing spectrum, time, or power resources. This chapter explores the challenges of integrating the RIS into multiuser downlink transmission systems. The efficiency and applicability of RIS-assisted multiple input-single output (MISO) and multiple input-multiple output (MIMO) schemes are analyzed in terms of bit error rate (BER) performance and complexity of the algorithms and techniques utilized. Likewise, the effects of the different wireless propagation channel models are analyzed. Approaches of blind RISs and optimization algorithms are also reviewed. Our simulation results showed up to 37 dB gain in BER curves when using N-RIS surfaces when the system has SE = 12 bpcu/user, 32 Tx antennas, and 8 users with 4 Rx antennas. These outcomes validate an increase in performance by adopting N-RIS surfaces and optimization techniques for adequate phase searching. This chapter also reviews the reported frameworks that apply machine learning algorithms to improve the overall system performance. Topics such as estimation of channel state information, beamforming applications, federated learning, and demodulation applications are reviewed. Finally, we shed light on trends and open research areas in this emerging topic. © 2024 The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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    Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models
    (MDPI AG, 2025-04-04)
    Pedro Torres-Bermeo
    ;
    Kevin López-Eugenio
    ;
    ;
    Guillermo Palacios-Navarro
    ;
    José Varela-Aldás
    The efficient sizing and characterization of the load curves of distribution transformers are crucial challenges for electric utilities, especially given the increasing variability of demand, driven by emerging loads such as electric vehicles. This study applies clustering techniques and predictive models to analyze and predict the behavior of transformer demand, optimize utilization factors, and improve infrastructure planning. Three clustering algorithms were evaluated, K-shape, DBSCAN, and DTW with K-means, to determine which one best characterizes the load curves of transformers. The results show that DTW with K-means provides the best segmentation, with a cross-correlation similarity of 0.9552 and a temporal consistency index of 0.9642. For predictive modeling, supervised algorithms were tested, where Random Forest achieved the highest accuracy in predicting the corresponding load curve type for each transformer (0.78), and the SVR model provided the best performance in predicting the maximum load, explaining 90% of the load variability (R2 = 0.90). The models were applied to 16,696 transformers in the Ecuadorian electrical sector, validating the load prediction with an accuracy of 98.55%. Additionally, the optimized assignment of the transformers’ nominal power reduced installed capacity by 39.27%, increasing the transformers’ utilization factor from 31.79% to 52.35%. These findings highlight the value of data-driven approaches for optimizing electrical distribution systems.