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The emerging 6G networks’ exponential growth mobile apps and ultra-dense device deployments demand intelligent and seamless handover processes, especially in Mobile Ad-hoc Network Assisted Topologies (MANAT) based on Software Defined Networking (SDN) architectures. Traditional handover strategies face increasing delays, packet loss, and erroneous decision making in heavily congested and dynamic environments. To meet these concerns, this paper presents an optimized handover management scheme based on an Enhanced Particle Swarm Optimization (EPSO) with a Hooke–Jeeves algorithm. This hybrid strategy leverages EPSO’s global search capabilities and Hooke–Jeeves local search precision to enable accurate target base station selection using multi RSSI, SINR, RTT, mobility pattern, and QoS metric thresholds. Navigation experiments conducted within a 6G-SDN simulation testbed with Mininet-WiFi and Ryu controller showcase dramatic increases in handover success rates and decreases in latency and packet loss. Moreover, the experimental data demonstrate a strong STA density and average handover count relationship where rising STA numbers trigger increased handover rate due to greater network competition. Test cases were run with different values for the convergence threshold (δ) interval (0.05, 0.1, 0.15) along with various configurations of objective function weights including RSSI, Latency, Jitter, Bandwidth, Speed, and Packet Loss, where the algorithm showed handover counts of 9700 to 43 for 60 STAs. This showcases the model’s extreme flexibility and precision agility. In summary, the tailored model design explored in this study presents a comprehensive, flexible, and efficient mobility management framework cognizant of QoS parameters for emerging 6G mobile networks.

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Optimized Handover Management for MANAT in 6G-SDN Using EPSO-Based Hooke–Jeeves Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Noor T. Mahmood,
  • Methaq Talib Gaata,
  • Ahmed T. Sadiq,
  • Ali Al-Sherbaz

摘要

The emerging 6G networks’ exponential growth mobile apps and ultra-dense device deployments demand intelligent and seamless handover processes, especially in Mobile Ad-hoc Network Assisted Topologies (MANAT) based on Software Defined Networking (SDN) architectures. Traditional handover strategies face increasing delays, packet loss, and erroneous decision making in heavily congested and dynamic environments. To meet these concerns, this paper presents an optimized handover management scheme based on an Enhanced Particle Swarm Optimization (EPSO) with a Hooke–Jeeves algorithm. This hybrid strategy leverages EPSO’s global search capabilities and Hooke–Jeeves local search precision to enable accurate target base station selection using multi RSSI, SINR, RTT, mobility pattern, and QoS metric thresholds. Navigation experiments conducted within a 6G-SDN simulation testbed with Mininet-WiFi and Ryu controller showcase dramatic increases in handover success rates and decreases in latency and packet loss. Moreover, the experimental data demonstrate a strong STA density and average handover count relationship where rising STA numbers trigger increased handover rate due to greater network competition. Test cases were run with different values for the convergence threshold (δ) interval (0.05, 0.1, 0.15) along with various configurations of objective function weights including RSSI, Latency, Jitter, Bandwidth, Speed, and Packet Loss, where the algorithm showed handover counts of 9700 to 43 for 60 STAs. This showcases the model’s extreme flexibility and precision agility. In summary, the tailored model design explored in this study presents a comprehensive, flexible, and efficient mobility management framework cognizant of QoS parameters for emerging 6G mobile networks.