Velocity-Aware Adaptive Handover Control for Mobility Management in 5G Ultra-Dense Networks

Mobility-Aware Handover Optimization Using Adaptive Time-To-Trigger Mechanisms in 5G Networks

In 5G networks, handover management is critical for ensuring seamless mobility, low latency, and high-quality user experiences. However, traditional handover mechanisms suffer from frequent handover failures and ping-pong effects, especially when users move at high speeds or across densely deployed small cells. This paper proposes a mobility-predictionbased handover approach with an adaptive Time-To-Trigger (TTT) mechanism that adjusts dynamically to user speed and mobility patterns. The system comprises three key components: a Mobility Prediction Module utilizing recurrent neural networks (RNNs) to analyze historical movement data and real-time parameters including signal strength and user speed; Dynamic TTT Adaptation that reduces TTT for high-speed users to enable faster handovers while increasing TTT for slow-moving users to prevent ping-pong effects; and a Handover DecisionAlgorithm that integrates predicted mobility with real-time signal quality measurements. Simulation results demonstrate that the approach improves handover success rates, reduces ping-pong effects and enhances overall network performance. Additionally, the adaptive framework contributes to better resource utilization and overall network efficiency, The proposed system achieved 94.2% success rate, 67% fewer ping-pong events, 42ms average delay, 15.8% throughput gain, and 89.4% prediction accuracy with low computational cost.

Integration of Named Data Networking (NDN) in 5G Architecture for Handover Optimization and QoS Enhancemen

— The increase in mobile data traffic in Fifth-Generation (5G) networks means that new handover management and content delivery solutions are needed to keep the network running smoothly and the user experience high. This paper introduces an innovative integration of Named Data Networking (NDN) into the 5G architecture, incorporating an Enhanced Popularity-Based Caching mechanism at the Multi-access Edge Computing (MEC) layer of the 5G user plane. Our design is different from previous ones because it changes how content is replicated based on how mobile and dense the User Equipment (UE) and gNB are in real time. Using Python-based models, we ran a lot of simulations to compare baseline 5G, edge-caching, and full NDN configurations. The proposed solution had a Handover Success Rate (HSR) of over 90%, a Cache Hit RAtio (CHR) of between 78% and 80%, an average latency of about 20 ms, and a packet loss rate of less than 1.0% across a wide range of network scenarios. The NDN integrated architecture cuts latency by up to 35%, boosts throughput by 40%, makes fallback efficiency improved by 36.8%, and raises average HSR by 25 – 40%. All of these changes improve the Quality of Experience (QoE) in environments with a lot of movement. The research we conduct aims to facilitate seamless, scalable, and resilient content delivery for next-generation 5G edge networks.

Predictive Handover Control with Mobility and Future Signal Quality Awareness for 5G Vehicular Networks

Frequent handovers remain a challenge in vehicular fifth-generation (5G) networks, especially in dense urban areas with small cells and intersections. Conventional handover decisions mainly follow the current radio condition and may select a target that is not stable along the vehicle's future route. This paper proposes a mobility- and radio-aware handover control method for 5G vehicular networks. The method keeps the default received-signal trigger as the initial detector, predicts a short future route from lane position, heading continuity, road topology, and transition-support information, and validates the candidate using a historical signal-to-interference-plus-noise ratio (SINR) grid map. It suppresses candidates that indicate a ping-pong return to the serving next-generation NodeB (gNB) or a short stay before another gNB becomes preferable, while a current SINR-based rescue rule avoids keeping the user equipment (UE) on a weak serving link. In a real-map Innsbruck urban microcell scenario with 85 vehicular UEs and 24 gNBs, the proposed method reduces total handovers by 28.9%, ping-pong handovers by 55.9%, and short-stay handovers by 69.0% compared with the default policy. The post-simulation reference signal received power (RSRP) evaluation also remains strong, with 99.46% of the proposed-method samples in the Excellent category and 0.54% in the Good category. These results show that mobility prediction with future SINR-map validation improves vehicular handover stability while preserving serving-link quality.

An efficient software-defined WiFi-based multi-objective access point load balancing

SDN-Assisted Low-Latency Resource Management for V2X Communication in Heterogeneous Cellular Networks

The proposed framework separates network control from forwarding, maintains a global view of vehicular network state, classifies V2X flows by service criticality, and dynamically selects routes and bandwidth allocations using delay, congestion, handover, and priority constraints.

A novel deep reinforcement learning-based cell selection algorithm for efficient mobility management in 6G networks

Simulation results position D3QN-PER as a strong candidate for deployment as a near-RT RIC xApp within the O-RAN architecture, advancing the vision of AI-native mobility management for 6G.