A Robust Handover Optimization Based on Velocity-Aware Fuzzy Logic in 5G Ultra-Dense Small Cell HetNets
Abstract
:1. Introduction
- Development of an intelligent FLC framework that separately handles TTT and HOM. The proposed framework utilizes critical system parameters such as RSRP, UE speed, and cell load to optimize the tuning of the TTT and HOM settings. These settings vary based on different input conditions, with the TTT being specifically set to longer for lower UE speed scenarios and shorter for higher UE speeds to ensure optimal performance.
- Optimization of the TTT and HOM settings involves applying the FLC rules based on previous expertise to determine their optimal values. This expertise includes an iterative process of applying different TTT and HOM values with varying ranges of input parameters to minimize RLF and HOPP. Specifically, different UE speed categories were considered for TTT optimization. It is crucial to set TTT and HOM in a manner that minimizes both RLF and HOPP levels simultaneously. Long TTT values may reduce HOPP occurrence but can lead to RLF due to serving signal deterioration and HO delay. Similarly, high HOM values can have the same effect. Conversely, a short TTT and low HOM may reduce the RLF but increase the overhead signaling and HOPP probabilities. This approach balances the mitigation of RLF and HOPP issues while ensuring the overall quality of service (QoS).
- We present an algorithm that facilitates efficient HO decision-making in ultra-densely deployed SC HetNets, resulting in optimized KPIs compared to existing methods in the literature, with a focus on simultaneously mitigating RLF and HOPP levels.
- Significantly, the UE speed thresholds considered in this study surpass those documented in prior literature. The carefully selected UE speeds are reflective of real-life scenarios, thus enhancing the practical applicability of this research.
2. Related Works
3. Proposed System Architecture
3.1. System Model
3.2. Fuzzy Logic Controller (FLC)
- During the Fuzzification stage, inputs are received, marking the beginning of the fuzzification process. Fuzzification involves converting imprecise input data into fuzzy values. This relates to a domain characterized by MFs. These functions can be in the form of a triangle, trapezoid, or Gaussian. In more complex systems, multiple MFs can be combined, and the choice depends on the specific requirements of the system. The triangular MF is the most commonly used option due to its simplicity, though its effectiveness may vary by implementation. The selection of MF shape is problem-specific, but extensive literature reviews indicate the triangular MF is generally more effective than other MFs [27]. If there is no preference for the shape of MFs, triangular or trapezoidal shapes are recommended for their simplicity and computational efficiency. To maximize performance, a design optimization process is often necessary, where the adjustable parameters of the fuzzy system are tuned to meet a specific performance criterion. The triangular and trapezoidal MFs are defined concisely using the following compact expressions and are visually depicted in Figure 3.
- In the inference/interpretation stage, rules are created using linguistic variables derived from system performance and experience. The interpreter is then used to make decisions based on the given input and the established rule base. There are two main categories of fuzzy inference systems (FIS): Mamdani-type FIS and Sugeno-type FIS, also referred to as Takagi–Sugeno–Kang fuzzy inference. The Mamdani and Sugeno FISs designed for a specific system have an equal number of input and output MFs. Their rules are also aligned, but the main difference lies in how they process fuzzy output through defuzzification. The primary distinction between the Mamdani and Sugeno FISs is their approach to generating precise output from fuzzy inputs. Prominent Mamdani defuzzification methods, such as the smallest of maxima (SoM), largest of maxima (LoM), and mean of maxima (MoM), stem from variations in the max criterion. These methods determine the smallest, largest, or mean output value on the basis of inputs whose membership values reach their maximum. Mamdani systems are commonly used in expert system applications because they benefit from their intuitive and easily understandable rule bases, which are often described on the basis of insights from human experts. In contrast, the Sugeno FIS uses a weighted average approach instead of the complex iterative process used in Mamdani systems. Unlike the Mamdani FIS, the Sugeno FIS does not include an output MF. Instead, it uses singleton output MFs that are either constant or linear functions of input values. This makes the Sugeno FIS less computationally complex than the Mamdani FIS. However, the interpretability and expressive capabilities of the Mamdani FIS are compromised in the Sugeno FIS because of its non-fuzzy rule consequents. As a result, the output is represented as a constant value rather than a fuzzy set during rule evaluation [28,29,30]. Ultimately, the choice between the Mamdani and Sugeno FIS depends on carefully weighing the advantages and limitations. This prompts the system designer to select an option that best suits the specific system requirements.
- During the Defuzzification stage, the decision of the inference block is converted into numerical values. This block serves as a bridge between the controller and the actual system, providing accurate values for further processing [31].
3.3. Proposed System and Algorithm
Algorithm 1 Pseudocode for calculating trigger timer. |
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4. Performance Evaluation
4.1. Handover Rate (HOR)
4.2. Handover Failure (HOF)
4.3. Radio Link Failure (RLF)
4.4. Handover Ping-Pong (HOPP)
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Inputs | Degrees | Scales |
---|---|---|
RSRP | Poor | to −95 dBm |
Fair | −95 to −75 dBm | |
Excellent | −75 to −10 dBm | |
Speed | Slow | 0 to 20 km/h |
Moderate | 20 to 80 km/h | |
Fast | 80 to 180 km/h | |
Very Fast | 180 to 220 km/h | |
Load | Low | 0 to 40% |
Medium | 40 to 80% | |
High | 80 to 100% |
Parameters | LTE-A | 5G |
---|---|---|
Carrier Frequency (f) | 2.6 GHz | 28 GHz |
System Bandwidth (B) | 20 MHz | 500 MHz |
Transmit Power (Tx) | 43 dBm | 30 dBm |
Cell Radius (R) | 1000 m | 100 m |
Antenna Height (H) | 25 m | 15 m |
Number of Cells | 21 | 280 |
Shadowing () | 8 dB | 10 dB |
Path Loss Model | ||
T310 | 1 s | |
Number of UEs for Evaluation | 20 | |
User Speeds (v) | [5, 20, 50, 80, 120, 180, 220] km/h | |
Reference Sensitivity (QRx) | −101.5 dBm | |
Noise Power Density (N0) | −174 dBm/Hz | |
Fast Fading Model | Rayleigh fading | |
UE Measurement Periodicity (MP) | 40 ms | |
Simulation Period | 1000 cycles |
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Riaz, H.; Öztürk, S.; Çalhan, A. A Robust Handover Optimization Based on Velocity-Aware Fuzzy Logic in 5G Ultra-Dense Small Cell HetNets. Electronics 2024, 13, 3349. https://doi.org/10.3390/electronics13173349
Riaz H, Öztürk S, Çalhan A. A Robust Handover Optimization Based on Velocity-Aware Fuzzy Logic in 5G Ultra-Dense Small Cell HetNets. Electronics. 2024; 13(17):3349. https://doi.org/10.3390/electronics13173349
Chicago/Turabian StyleRiaz, Hamidullah, Sıtkı Öztürk, and Ali Çalhan. 2024. "A Robust Handover Optimization Based on Velocity-Aware Fuzzy Logic in 5G Ultra-Dense Small Cell HetNets" Electronics 13, no. 17: 3349. https://doi.org/10.3390/electronics13173349