Mobility and Handover Management in 5G/6G Networks: Challenges, Innovations, and Sustainable Solutions
Abstract
1. Introduction
- A focused survey of HO and mobility challenges in 5G UD-HetNets, identifying key problem areas such as FHO, RLF, and signaling overhead;
- A performance-based comparison of state-of-the-art HO techniques and algorithms based on relevant KPIs;
- A discussion on sustainability, AI integration, and adaptive HO strategies for improved network resilience;
- A critical analysis of open research issues and potential directions for future mobility management in B5G/6G networks.
2. Literature Review
2.1. HO in 5G Networks
2.2. Surveys with a Technology-Specific Emphasis
2.3. Mobility-Specific HO
2.4. Advanced Architectures and Optimization-Oriented Studies
2.5. Research Gap
- Lack of critical cross-comparison between strategies using common performance indicators;
- Narrow focus on specific technologies without holistic integration;
- Limited discussion of crucial challenges, like LB, ICI, and HOFR, under high-speed or ultra-dense conditions;
- Absence of comprehensive taxonomies or unified evaluation frameworks.
3. 5G Architecture and Services
3.1. 5G Architecture
3.2. 5G Services
- eMBB delivers high-speed connectivity and increased capacity for data-intensive services such as UHD video streaming, AR/VR, and other multimedia-rich applications. Technologies like massive MIMO, beamforming, and broad frequency bands ensure reliable performance even in high-density environments [39,40].
- mMTC focuses on connecting large volumes of low-power IoT devices, supporting sectors like smart cities, agriculture, and healthcare. It emphasizes coverage, energy efficiency, and the ability to manage massive device densities under constrained conditions [41].
- URLLC is designed for mission-critical applications requiring ultra-low latency (as low as 1 ms) and high reliability, such as autonomous vehicles, remote surgery, and industrial automation. It achieves these goals through network slicing, edge computing, and optimized resource management [42].
3.3. 5G Technologies
3.3.1. 5G and HetNet
3.3.2. mmWave
3.3.3. Massive MIMO
3.3.4. Beamforming
3.3.5. 5G HetNet and FD Communication
3.3.6. D2D Communication
3.3.7. SDN and NFV
4. HO in 5G Networks
- UE continuously measures key parameters such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR) from both the serving cell (s-gNB) and neighboring cells (T-gNB). These measurements are reported to the serving gNB to assist in the HO decision-making process.
- Based on the measurement reports, network conditions, and predefined policies, the serving gNB determines whether a handover is necessary. Factors such as signal strength, cell load, and UE mobility patterns are considered to select the most suitable target cell (T-gNB).
- The serving gNB initiates communication with the target gNB to prepare for the handover. This involves exchanging necessary information, such as UE context and resource allocation, to ensure a seamless transition. The target gNB reserves resources and configures itself to accommodate the incoming UE.
- Once preparation is complete, the serving gNB sends a HO command to the UE, instructing it to switch to the target gNB. The UE detaches from the serving cell and synchronizes with the target cell, establishing a new connection.
- After successfully connecting to the target gNB, the UE sends a confirmation message to the network. The target gNB notifies the serving gNB of the successful handover, and the serving gNB releases the resources previously allocated to the UE.
- The core network updates the data path to route traffic through the target gNB instead of the serving gNB. This ensures that data transmission continues without interruption.
- Post-handover, the network may perform optimization procedures to fine-tune parameters and ensure optimal performance. This could include adjusting radio resources or re-evaluating handover thresholds based on network conditions.
5. HO Parameters in 5G
5.1. HCPs
5.2. HO Decision Parameters for HO
5.3. KPIs in HO Studies
6. HO Management in 5G Ultra-Dense Small Cell Networks
6.1. Measurement-Based and Experimental Studies
6.2. Optimization Techniques for HO and Mobility Management
- Fuzzy logic controllers: Utilized for their ability to handle uncertainty and imprecision in network parameters such as signal strength, user velocity, and load, enabling dynamic adaptation of handover thresholds and timers.
- Metaheuristic algorithms: Techniques like Bayesian Optimization, Ant Lion Optimization, and hybrid methods (e.g., combining Kinetic Gas Molecular Optimization with other algorithms) are leveraged to solve multi-objective optimization problems, balancing trade-offs like reducing handover rates while maintaining QoS.
- Bayesian and machine learning-based optimization: These approaches enable the proactive tuning of handover parameters by learning from network state and user behavior, improving decision accuracy with fewer trial-and-error cycles.
6.3. AI/ML-Based Approaches
- Sequence-to-sequence and time series prediction: These are used to forecast handover cells, dwell times, and user trajectories, helping to anticipate network resource requirements and optimize handover timing;
- Reinforcement learning and deep learning: These are applied to learn optimal handover policies in dynamic network environments, balancing trade-offs like handover frequency, latency, and throughput;
- Hybrid and ensemble models: Combining multiple ML algorithms to improve prediction accuracy and robustness in varying mobility scenarios.
6.4. SDN/NFV and Network Slicing-Based Solutions
6.5. Vehicular and High-Speed Mobility Management
6.6. Mobility Management in mmWave, THz, and Ultra-Dense Networks
6.7. HO Algorithms for D2D, MIAB, and Integrated Systems
6.8. Mobility Prediction Techniques in 5G Networks
6.9. Emerging Mobility Mechanisms in 5G-Advanced: Layer 1/Layer 2 Triggered Mobility (LTM)
7. Research Challenges
7.1. Network Complexity and Scalability
7.2. Energy Efficiency and Sustainability
7.3. Spectrum Management and Interference
7.4. Security and Privacy
7.5. Latency and Mobility Management
7.6. Future Challenges in 6G Mobility
7.7. Emerging Architectures: Non-Terrestrial and Digital Twin Networks
8. Performance Analysis
9. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
UE | user equipment |
3GPP | 3rd generation partnership project |
ITU | International Telecommunication Union |
IoT | Internet of things |
D2D | device-to-device |
V2V | vehicle-to-vehicle |
EC | edge computing |
MEC | mobile edge computing |
HetNet | heterogeneous network |
UD-HetNet | ultra-dense heterogeneous network |
mmWave | millimeter wave |
MIMOs | multiple-input-multiple-outputs |
B5G | beyond-5G |
QoS | quality of service |
QoE | quality of experience |
HO | handover |
RSS | received signal strength |
MBS | macro base station |
SBS | small base station |
BS | base station |
RLF | radio link failure |
CCI | co-channel interference |
KPIs | key performance indicators |
LTE-A | LTE-advanced |
5G-NR | 5G New Radio |
CoMPs | coordinated multiple points |
eNB | Node B |
ICIC | inter-cell interference coordination |
CN | core network |
EPC | evolved packet core |
RAN | radio access network |
NR | new radio |
SDN | software-defined networking |
ML | machine learning |
LB | load balancing |
LTE | long-term evolution |
UAVs | unmanned aerial vehicles |
IoE | internet of everything |
SA | standalone |
NSA | non-standalone |
eMBB | enhanced mobile broadband |
mMTCs | massive machine-type communications |
URLLCs | ultra-reliable low-latency communications |
M2M | machine-to-machine |
FD | full-duplex |
RATs | radio access technologies |
ICI | inter-cell interference |
SINR | signal-to-interference-plus-noise ratio |
SNR | extremely-high frequency |
SNR | signal-to-noise ratio |
NFV | network function virtualization |
H-HO | hard HO |
S-HO | soft HO |
HHO | horizontal handover |
VHO | vertical Handover |
HOIT | handover interruption time |
MR | measurement report |
HODPs | handover decision parameters |
HCPs | handover control parameters |
TTT | time-to-trigger |
RSRP | reference signal received power |
RSRQ | reference signal received quality |
RSSI | RSS indicator |
CSI | channel state information |
HOM | HO margin |
CIO | cell individual offset |
MRO | mobility robustness optimization |
LBO | load-balancing optimization |
HOPPs | handover probability problems |
HPPP | handover ping-pong probability |
HOR | HO rate |
HOFR | HO failure rate |
HOSR | HO success rate |
HOD | HO delay |
HOET | HO execution time |
HPSO | handover parameter self-optimization |
CDF | cumulative distribution function |
NTNs | non-terrestrial networks |
LEO | low Earth orbit |
HAPs | high-altitude platforms |
DTNs | digital twin networks |
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HODPs | HCPs |
---|---|
Signal Strength Thresholds | Signal Strength Thresholds (used for HO initiation) |
LB Parameters | LB Parameters (control HOs for traffic distribution) |
QoS Parameters | QoS Parameters (control HOs based on service requirements) |
Measurement Reporting Configuration | Measurement Reporting Configuration (controls frequency of measurement reporting) |
Ref | Focus | Methodology | Key Findings |
---|---|---|---|
[66] | 5G HO process and mobility impact | Extensive measurements during cross-country drive | Identified challenges in NSA deployments; Prognos HO prediction system improves QoE for 5G applications (e.g., 16K video) |
[67] | Energy efficiency in femtocell UDNs | Simulation of UL and DL HO mechanisms | New target cell algorithm based on UL-RSRP reduces HO rate and ping-pong rate; improves power consumption and packet loss |
[68] | HO decision algorithms in femtocell-macrocell setups | Algorithm development and simulation | Novel HO decision algorithm reduces unnecessary handovers and improves energy efficiency by over 85% |
[69] | Robust HO optimization with fuzzy logic | RHOT-FLC validated across mobility scenarios | Up to 95% reduction in HO probability, failure, ping-pong, latency, and interruption time |
[70] | Bayesian Optimization for HO thresholds | Multi-objective Bayesian Optimization for indoor factory | Efficiently identifies Pareto-optimal HO thresholds, minimizes early/late HOs |
[71] | Weighted function and trigger timer for HO ping-pong | Simulation with speed-aware weighted functions | Significant reduction in HO ping-pong probability, outperforming fuzzy logic controllers |
[72] | Metaheuristic optimization combining KGMO and ALO | Simulation in MATLAB environment | Improved throughput, delay, and HO rates compared to other metaheuristics |
[73] | Hybrid KGMO-ALO optimization algorithm | MATLAB simulations comparing metaheuristic techniques | Enhancements in throughput and HO efficiency for heterogeneous LTE networks |
[74] | HO optimization for high-speed trains and drones | Trigger timer and weighted algorithm based on network parameters | Reduced RLF, HO ping-pong, HO probability, and HO interruption time significantly |
[75] | Fuzzy logic controller for HO decisions in UD-SCN | FLC dynamically adjusting TTT and HOM | Significant improvement in HO rate, failure, RLF, and ping-pong compared to existing methods |
[76] | Sequence-to-sequence modeling for HO prediction | Predicts HO cells and dwell times using historical trajectory data | Achieved over 90% HO cell estimation accuracy and low mean absolute error for dwell time |
[77] | Proactive mobility management framework | Advanced Mobility Management and Utilization Framework (A-MMUF) using mobility prediction models | Significant improvements in HO process, mobility load balancing, and energy savings |
[78] | ML prediction of mobility management tasks | Compared baseline, linear regression, CNN, and LSTM models on HO and TAU message data | LSTM and CNN provided more accurate demand forecasts for LTE and 5G NSA architectures |
[79] | Early-scheduled HO preparation in 5G-NR | ML-based prediction of earliest HO trigger timing | Reduced channel quality degradation and improved HO robustness and efficiency |
[80] | DRL for HO minimization in network slicing | Deep reinforcement learning with Proximal Policy Optimization (PPO) | PPO approach significantly reduced HO numbers and improved network efficiency |
[81] | Network slicing and MEC for HO management | HO mobility management architecture leveraging network slicing for seamless HO between 5G and 4G | Reduced handover disruptions (HODs) and increased average throughput compared to RSS-based and CMaaS HO methods |
[82] | SDN-based dynamic mobility for high-speed railways | SDN controller with Kalman Filter-based user trajectory prediction for seamless service migration | Migration time reduced by 30%, end-to-end delay reduced by 40%, improved throughput |
[83] | VHO mechanism integrating IEEE 802.21 with SDN | Centralized SDN control combined with Media Independent Handover for optimized vertical handovers | Significantly fewer unnecessary handovers, improved resource utilization and QoS |
[84] | Fuzzy logic-based VHO with MIH and PMIPv6 integration | Proposed new VHO algorithm to reduce latency and signaling overhead | Significant reduction in handover delay, packet loss, HO blocking probability, and signaling overhead |
[85] | Energy-aware mobility management for smart cities and vehicular networks | Review of mobility management protocols for 5G-enabled vehicular networks with focus on sustainable energy usage | Identified design limitations and proposed future research for greener vehicular networks |
[66] | 5G HO process and mobility impact | Extensive measurements during cross-country drive | Identified challenges in NSA deployments; Prognos HO prediction system improves QoE for 5G applications (e.g., 16K video) |
[86] | Vehicular Frequency Reuse (VFR) for mmWave 5G CAVs | User-centric channel allocation with Distance-Threshold and Velocity-Threshold metrics; K-Means ML for velocity classification | Over 99% reduction in HO rates; improved link reliability and channel reuse |
[87] | VFR scheme for high-speed users in mmWave 5G | Similar to [86], with a focus on V2N services and minimal software update integration | Significant reduction in HO rates and control plane signaling; easy integration with existing 5G networks |
[88] | Energy-efficient mobility management protocol NEMa for vehicular networks | Protocol optimizing network and vehicle sensing for energy efficiency and packet delivery | Outperformed benchmarks in network overhead, latency, and energy consumption |
[89] | Blockage prediction and HO in mmWave/THz UDNs | Wireless signals combined with computer vision from on-road surveillance to predict blockages and trigger proactive HO | Achieved 40% improvement in connectivity and QoE by predicting and mitigating blockage events |
[90] | HO scheme for mmWave links using Game Theory and JMLS | Prediction of link deterioration via Game Theory and Jump Markov Linear Systems for optimal link selection | Improved throughput, energy efficiency, reliability, and dwell time, reducing link failures |
[70] | Multi-objective HO threshold tuning in indoor factory UDN | Bayesian Optimization to balance early and late HOs for service continuity | Efficient tuning of HO parameters to reduce unnecessary handovers |
[67] | Uplink HO in femtocell UDN | Target cell determination algorithm considering UL-RSRP, bandwidth, and user direction | Reduced HO rates, ping-pong, and energy consumption |
[68] | HO decision algorithm in macro-femto deployments | Incorporates user speed, RSS, duration of stay, and femtocell policy | Reduced unwanted handovers and improved energy efficiency by 85% |
[91] | Mobility challenges in 5G HetNets | Analysis of RRC challenges due to dense small cells, HO failures, delays, and ping-pong effects | Highlighted critical challenges for efficient mobility management in dense HetNets |
[92] | HO in D2D communication | Ping-Pong effect Reduction (PPR) algorithm; Reference Point Group Mobility (RPGM) model; mode selection algorithm | Reduced unnecessary HOs by up to 80%; stable throughput of 12 Mbps; lower power consumption |
[93] | HO in MIAB networks | Probabilistic modeling of HO scenarios; RACH-less HO procedure; low-latency uplink control plane transmission | Significantly reduced HO delay and overhead; improved QoS in dense mobile environments |
[94] | Seamless HO in HetNets among macrocells, small cells, femtocells | Self-optimization algorithm balancing MRO and LBO optimization objectives | Reduced HO ping-pong and handover failures; improved network performance and user experience |
[95] | Predictive HO strategy based on user profiles | Profile-Based Predictive HO Strategy (PBPHS) using mobility and resource utilization data | HO reduction rate improved by 13–26% over existing methods; enhanced QoS |
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Saoud, B.; Shayea, I.; Alnakhli, M.A.; Mohamad, H. Mobility and Handover Management in 5G/6G Networks: Challenges, Innovations, and Sustainable Solutions. Technologies 2025, 13, 352. https://doi.org/10.3390/technologies13080352
Saoud B, Shayea I, Alnakhli MA, Mohamad H. Mobility and Handover Management in 5G/6G Networks: Challenges, Innovations, and Sustainable Solutions. Technologies. 2025; 13(8):352. https://doi.org/10.3390/technologies13080352
Chicago/Turabian StyleSaoud, Bilal, Ibraheem Shayea, Mohammad Ahmed Alnakhli, and Hafizal Mohamad. 2025. "Mobility and Handover Management in 5G/6G Networks: Challenges, Innovations, and Sustainable Solutions" Technologies 13, no. 8: 352. https://doi.org/10.3390/technologies13080352
APA StyleSaoud, B., Shayea, I., Alnakhli, M. A., & Mohamad, H. (2025). Mobility and Handover Management in 5G/6G Networks: Challenges, Innovations, and Sustainable Solutions. Technologies, 13(8), 352. https://doi.org/10.3390/technologies13080352