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▷ Advancements in Device-Free Indoor Localization Using Machine Learning and 28 GHz Band Technology

 

✅ Keywords:

    • ➡️ #MillimeterWave #MMwave #28GHz #IndoorLocation #WirelessInSite #Remcom #Matlab #ML #MachineLearning #AI #ArtificialIntelligence #RegressionLearner #Classification
    ✅ Introduction:
    • In this edition of our newsletter, we delve into groundbreaking research exploring the realms of device-free indoor localization techniques. Utilizing the power fluctuations in a communication link due to the presence of a human body, our researchers have employed advanced machine learning methods in the 28 GHz band. This innovative approach aims to redefine indoor positioning, offering unprecedented accuracy and efficiency.
    ✅ Content:
    • In our quest to enhance indoor localization technology, our research team conducted comprehensive studies. Through meticulous simulations and experiments, we evaluated the potential of machine learning algorithms in the 28 GHz band, focusing on the effects of human presence within an enclosed space.
      • 1. Database Construction and Simulation:
        • Our researchers meticulously constructed a database using ray tracing simulations, involving a system comprising 4 receivers and up to 2 transmitters. These simulations, conducted with individuals standing within the room, laid the foundation for our investigations.
      • 2. Machine Learning Algorithms and Localization Error Reduction:
        • By employing machine learning techniques, specifically independent classifiers, our team achieved remarkable results. Statistical localization error reductions of at least 16% and 19% were observed with one and two transmitters, respectively. Furthermore, the integration of classifiers with regression algorithms led to additional improvements. These findings offer a promising avenue for more precise indoor positioning.
      • 3. Factors Influencing Positioning Error:
        • Our studies shed light on critical factors influencing positioning errors. The size of blocks (strips) into which the study area is partitioned and the number of examples per class were found to significantly impact the accuracy of indoor localization. Understanding these factors is crucial for refining our methods and advancing the field.
      ✅ Conclusions:
        • In conclusion, our research signifies a significant leap forward in the realm of device-free indoor localization. Through the integration of machine learning algorithms, specifically independent classifiers and regression techniques, we have achieved substantial reductions in positioning errors. These findings hold the promise of revolutionizing various sectors, including smart home automation, healthcare, and industrial applications.

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