Abstract
Ensuring a reliable and stable communication throughout the mobility of User Equipment (UE) is one of the key challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. One of the main issues is the use of suboptimal Handover Control Parameters (HCPs) settings, which are configured manually or generated automatically by certain self-optimization functions. This issue becomes more critical with the massive deployment of small base stations and connected mobile users. This will essentially require an individual handover self-optimization technique for each user individually instead of a unified and centrally configured setting for all users in the cell. In this paper, an Individualistic Dynamic Handover Parameter Optimization algorithm based on an Automatic Weight Function (IDHPO-AWF) is proposed for 5G networks. This algorithm dynamically estimates the HCPs settings for each individual UE based on UE's experiences. The algorithm mainly depends on three bounded functions and their Automatic Weights levels. First, the bounded functions are evaluated, independently, as a function of the UE's Signal-to-Interference-plus-Noise-Ratio (SINR), cells' load and UE's speed. Next, the outputs of the three bounded functions are used as inputs in a new proposed Automatic Weight Function (AWF) to estimate the weight of each output bounded function. After that, the final output is used as an indicator for optimizing HCPs settings automatically for a specific user. The algorithm is validated throughout various mobility conditions in the 5G network. The performance of the analytical HCPs estimation method is investigated and compared with other handover algorithms from the literature. The evaluation comparisons are performed in terms of Reference Signal Received Power (RSRP), Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The simulation results show that the proposed algorithm provides noticeable enhancements for various mobile speed scenarios as compared to the existing Handover Parameter Self-Optimization (HPSO) algorithms.
Highlights
The Handover Parameter Self-Optimization (HPSO) is one of the significant Self-Optimization Network’s (SON) functions that have been introduced by the 3rd Generation Partnership Project (3GPP) in Fourth Generation (4G) and Fifth Generation (5G) mobile technologies [1,2,3,4,5,6,7,8,9]
In the 4G system, HPSO is known as Mobility Robustness Optimization (MRO) function, and it is heading to be more advanced in 5G mobile systems [3,4,5,6,7,8,9]
The suboptimal settings of Handover Control Parameters (HCPs) may contribute to high rates of Handover Probability (HOP), Handover Ping-Pong Probability (HPPP) or Radio Link Failure (RLF), which will collectively produce increased redundancy leading to wastage of network resources
Summary
The Handover Parameter Self-Optimization (HPSO) is one of the significant Self-Optimization Network’s (SON) functions that have been introduced by the 3rd Generation Partnership Project (3GPP) in Fourth Generation (4G) and Fifth Generation (5G) mobile technologies [1,2,3,4,5,6,7,8,9]. In [15], Enhanced Mobility State Estimation (EMSE) was introduced to optimize HCPs (i.e. Time-To-Trigger (TTT) and HOM) based on handover types and speed of users. The HOM level is adjusted by the FLC based on two control input metrics: the Drop Call Probability (DCP) and the Handover Ratio (HOR) Another gradient method and cost function-based MRO scheme was proposed for LTE femtocell [17]. No efficient handover SON algorithms were in the papers mentioned can estimating the optimal HCP settings. The algorithm adjusts HCP settings at the Base Station (BS) for the entire system and the handover for all mobile users is controlled by utilizing the same HCP settings This central optimization may lead to increased handover issues for some users. Self-Optimization functions and their relationship with network control parameters [34]
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