The massive growth of mobile users will spread to significant numbers of small cells for the Fifth Generation (5G) mobile network, which will overlap the fourth generation (4G) network. A tremendous increase in handover (HO) scenarios and HO rates will occur. Ensuring stable and reliable connection through the mobility of user equipment (UE) will become a major problem in future mobile networks. This problem will be magnified with the use of suboptimal handover control parameter (HCP) settings, which can be configured manually or automatically. Therefore, the aim of this study is to investigate the impact of different HCP settings on the performance of 5G network. Several system scenarios are proposed and investigated based on different HCP settings and mobile speed scenarios. The different mobile speeds are expected to demonstrate the influence of many proposed system scenarios on 5G network execution. We conducted simulations utilizing MATLAB software and its related tools. Evaluation comparisons were performed in terms of handover probability (HOP), ping-pong handover probability (PPHP) and outage probability (OP). The 5G network framework has been employed to evaluate the proposed system scenarios used. The simulation results reveal that there is a trade-off in the results obtained from various systems. The use of lower HCP settings provides noticeable enhancements compared to higher HCP settings in terms of OP. Simultaneously, the use of lower HCP settings provides noticeable drawbacks compared to higher HCP settings in terms of high PPHP for all scenarios of mobile speed. The simulation results show that medium HCP settings may be the acceptable solution if one of these systems is applied. This study emphasises the application of automatic self-optimisation (ASO) functions as the best solution that considers user experience.

https://www.mdpi.com/1424-8220/21/15/5202/pdf

Handover Parameters Optimisation Techniques in 5G Networks

An adaptive and distributed network selection mechanism for 5G networks

The mobility of user equipment (UE) in cellular network is a challenging issue in terms of its management. Current traditional handover in Long-Term Evolution (LTE) network is managed by evolved Node B or eNodeB (eNB) which is a decentralized solution. In contrast to existing technology, software defined network (SDN) has the capability of serving the packets of the switching equipment without involving the SDN controller except for the first one. We have proposed an SDN-based centralized solution for handover management in LTE network. Based on our solution, the handover is being managed by SDN controller which keeps track of the overall network management and dictates the flow entries to the OpenFlow switches in the network. In our testbed, two UEs are connected to two eNBs, and one of the UE performs a handover from one eNB to other eNB. It enhances the performance of the network in terms of reducing the delay while performing the handover and increasing the data rate of the running application. The initial delay will be bit higher due to the initial flow entry absence in the flow tables of the switches; later, the delay will be reduced. It is evident from the results that our approach keeps track of the overall network at centralized controller with improved performance.

The Handover and Performance Analysis of LTE Network with Traditional and SDN Approaches

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.

https://www.mdpi.com/1424-8220/21/12/4234/pdf

A Semantic and Knowledge-Based Approach for Handover Management.

— In this work, we investigate the end-to-end performance of the uplink (U/L) non-orthogonal multiple access (NOMA) system based on the novel Bi-directional long short-term memory (Bi-LSTM) algorithm over the frequency flat independent and identically distributed (i.i.d.) Rayleigh fading channel conditions. When compared to conventional successive interference cancellation (SIC) MIMO-NOMA-based detection systems, the suggested deep learning (DL) based technique integrates the conventional multiple-input multiple-output (MIMO) and power-domain NOMA schemes to improve the symbol error rate (SER) performance. To this end, an optimal power allocation problem for the MIMO-NOMA scheme has been developed that maximizes the data throughput of the end-to-end system. In this work, both imperfect and perfect SIC schemes are considered, and performance comparison is provided between the Bi-LSTM based MIMO-NOMA and LSTM MIMO-NOMA schemes. The SIC NOMA system achieves 15 dB for 10 6 iterations, but the DL-based MIMO-NOMA scheme achieves 15 dB for 100 iterations. By a factor of four, Bi-LSTM MIMO-NOMA schemes outperform SIC MIMO-NOMA methods. Rather than utilizing conventional SIC systems to determine fading channel coefficients and decode signals, the suggested scheme estimates the relevant data symbol using the more efficient Bi-LSTM algorithm. There is a 4 dB difference, indicating that DL-based MIMO-NOMA outperforms conventional SIC MIMO-NOMA approaches. Furthermore, when the channel estimation error is enhanced from 0 to 1, the performance of DL is considerably decreased. Even with perfect channel state information (CSI), the DL detector outperforms the SIC detector for channel estimate errors of less than 0.07. When differences between the real and predictable channel states occur, the DL detector's performance suffers significantly, yet it can still maintain its majority within a predetermined tolerance range.

Examination of the DL Based Ubiquitous Multiple Input Multiple Output U/L NOMA System Considering Robust Fading Channel Conditions for Military Communication Scenario

Traditional multi-criteria decision making (MCDM) algorithms are used in the handover of user equipment (UE) in an Ultra Dense Network (UDN). UDN refers to the increased density of the Radio Access Technologies (RATs) in a region which leads to the overlapping of the areas covered by individual RATs. MCDM algorithms such as TOPSIS, PROMETHEE and SAW are used to initiate handovers between these RATs based on the parameters obtained by the UE from each of the overlapping networks. However, initiating a handover abruptly and frequently, in case of availability of a new RAT without any thresholding technique proves to be unfriendly to the system resources. This can degrade the performance of the system. In this paper, a thresholding approach to the handover procedure is integrated to the MCDM process for the selection of RATs. First, an application-specific approach has been used in the selection of weights using the analytical hierarchy process which, is depending upon the application being used by the user. Then the ranking of the available RATs is done using the various MCDM algorithms and depending on the threshold specified for a handover, a decision is made whether to perform the handover process or not. In the case of streaming class of network traffic, the proposed method improves the performance of the system and reduces the handover by 13.14%, 19.35% and 8.62% of RAT modifications for TOPSIS, PROMETHEE and SAW respectively.

Gopalji Gaur

Papers

Application specific thresholding scheme for handover reduction in 5G Ultra Dense Networks