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    A Comprehensive Review on ISAC for 6G: Enabling Technologies, Security, and AI/ML Perspectives
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Aldirmaz-Colak, Sultan; Namdar, Mustafa; Basgumus, Arif; Ozyurt, Serdar; Kulac, Selman; Calik, Nurullah; Yazici, Mehmet Akif
    Integrated sensing and communication (ISAC) has received significant attention over the past five years as a key component of the 6G vision for next-generation communication systems. Starting with the fundamentals of ISAC, we present strategies focused on sensing-centric and communication-centric ISAC systems that satisfy both radar and communication objectives using the same physical hardware and waveform. The communication-centric ISAC focuses primarily on improving communication performance beyond 5G/6G cellular networks and Wi-Fi standards, while also performing additional processing required for detection. On the other hand, the sensing-centric ISAC prioritizes sensing and detection capabilities while incorporating communication functions in the most efficient way possible. In this survey, we present a comprehensive review of both ISAC systems along with new approaches for enabling technologies and highlight unique challenges and existing solutions that are already available. We explore the roles of innovative functions in enabling ISAC, including joint waveform design, edge computing, vehicular sensing and communications, and their practical applications. We then discuss 3GPP standardization efforts related to ISAC as a future direction for the 6G vision. We also outline possible future research directions, with a specific emphasis on security, artificial intelligence, and machine learning.
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    A Handover Decision Optimization Method Based on Data-Driven MLP in 5G Ultra-Dense Small Cell HetNets
    (Springer, 2025) Riaz, Hamidullah; Ozturk, Sitki; Aldirmaz-Colak, Sultan; Calhan, Ali
    Deploying Ultra-Dense Small Cells (UDSCs) in Heterogeneous Networks (HetNets) introduces advantages such as increased capacity and expanded coverage over conventional HetNets. However, these advantages come at the expense of some challenges during the Handover (HO) process. Radio Link Failure (RLF) and Unnecessary Handover (UHO) are severe among these challenges. To address these issues, accurate setting and optimization of Handover Control Parameters (HCPs), including Handover Margin (HOM) and Time-To-Trigger (TTT), are necessary. Inaccurate adjustment and optimization of HCPs in live networks may lead to underperformance. Thus, this paper proposes a method that optimizes the obtained dataset by developing an algorithm that adjusts HOM and TTT based on related metrics such as RLF and UHO. The optimized dataset is then applied to a Multi-Layer Perception (MLP) model within a developed HO decision algorithm to predict both HOM and TTT, considering user speed, Reference Signal Received Power (RSRP), Signal to Interference plus Noise Ratio (SINR), and cell load. Simulation results showed that the proposed method outperforms the well-known A3 method in terms of Handover Rate (HOR), Handover Failure (HOF), Handover Ping-Pong (HOPP) and RLF by approximately 90.9%, 76.6%, 79.8% and 75%, respectively.