A Semantic and Knowledge-Based Approach for Handover Management

Abstract

Handover Management (HM) is pivotal for providing service continuity, enormous reliability and extreme-low latency, and meeting sky-high data rates, in wireless communications. Current HM approaches based on a single criterion may lead to unnecessary and frequent handovers due to a partial network view that is constrained to information about link quality. In turn, HM approaches based on multicriteria may present a failure of handovers and wrong network selection, decreasing the throughput and increasing the packet loss in the network. This paper proposes SIM-Know, an approach for improving HM. SIM-Know improves HM by including a Semantic Information Model (SIM) that enables context-aware and multicriteria handover decisions. SIM-Know also introduces a SIM-based distributed Knowledge Base Profile (KBP) that provides local and global intelligence to make contextual and proactive handover decisions. We evaluated SIM-Know in an emulated wireless network. When the end-user device moves at low and moderate speeds, the results show that our approach outperforms the Signal Strong First (SSF, single criterion approach) and behaves similarly to the Analytic Hierarchy Process combined with the Technique for Order Preferences by Similarity to the Ideal Solution (AHP-TOPSIS, multicriteria approach) regarding the number of handovers and the number of throughput drops. SSF outperforms SIM-Know and AHP-TOPSIS regarding the handover latency metric because SSF runs a straightforward process for making handover decisions. At high speeds, SIM-Know outperforms SSF and AHP-TOPSIS regarding the number of handovers and the number of throughput drops and, further, improves the throughput, delay, jitter, and packet loss in the network. Considering the obtained results, we conclude that SIM-Know is a practical and attractive solution for cognitive HM.

Brief Survey: Machine Learning in Handover Cellular Network

“…Furthermore, within the ML process, defining the specific task the algorithm aims to solve is crucial. In the context of ML in cellular network HOs, tasks may include self-organizing, which automates the HO process through self-organization of connections [9]; the management of HO control parameters (HCP), where parameters are automatically configured to enhance efficiency [10]; and self-optimizing networks, which achieve automatic system optimization [6].…”

“…The SIM-Known semantic information model aids in HO decisions by considering various cellular network contexts, such as network, application, user, device, and HO. Through semantic analysis, it categorizes HOs as network-controlled, mobile-controlled, network-assisted, or mobile-assisted [10].…”

“…Research efforts focus on developing algorithms for the adaptive adjustment of HCPs based on various network factors, including RSRP, SINR, and UE speed. Performance indicators such as HO failure rate (HOFR) are also considered [10].…”

SSHS: SDN seamless handover system among LAN access points

“…In A Semantic and Knowledge‐Based Approach for Handover Management , 43 the researchers introduced a SIM‐Know method to enhance handover management by employing the semantic information model (SIM), which allows context‐awareness and multiple criteria to regulate handover decisions. Abdulkarem and Audah 44 suggested administering the base‐station handover in a 5G cellular network by using the concentric method.…”

Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach

“…A very popular example is a Strongest Signal First method which utilizes Received Signal Strength Indication (RSSI) value to find an access device from which the mobile node receives the strongest radio signal. However, as we already mentioned, such solutions are practicable only in homogeneous networks [ 22 ] because the parameter used in comparison must be available for all access devices and impact the quality of communication in an uniform manner for them all. In heterogeneous environments, with their diverse access technologies, such a low-level parameter that could be directly used for link quality assessment and selection is most often impossible to find due to vastly different communication methods.…”