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An Enhanced Algorithm for Load-based Handover Decision-making in 5G Wireless Networks.
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An Enhanced Algorithm for Load-based Handover Decision-making in 5G Wireless Networks.
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 858 An Enhanced Algorithm for Load-based Handover Decision-making in 5G Wireless Networks. Lama Douba1, Ahmad Mahmoud Ahmad2, Ahmad Saker Ahmad3 1PhD Student, Dept. of System and Computer Networks Engineering, Tishreen University, Latakia, Syria. 2Assistant Professor, Dept. of System and Computer Networks Engineering, Tishreen University, Latakia, Syria. 3Professor, Dept. of System and Computer Networks Engineering, Tishreen University, Latakia, Syria. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The handover process is one of the most important aspects of mobility management in 5G networks, it ensures a user's network connection continuity while moving from one location to another. It is also the cornerstone of achieving mobility load balancing under the name of the offloading process. The decision-making phase is one of the handover stages that need improvement to choose the appropriate station and accurately determine the handover parameters to select the proper time to execute the handover. In this paper, we proposed a Load-based Handover Decision- making algorithm based on fuzzy logic, considering the cell's load before implementing the handover decision to determine whether the candidate cell is suitable forthehandoverprocess to avoid problems resulting from handover toaheavilyloaded cell. The results showed that the proposed algorithm provided an improvement in terms of the standard deviation of theload in the network, average throughput, packet loss rate, and handover failure rate. Key Words: 5G Networks, Handover, Handover Control Parameters, Cell Load, Fuzzy Controller. 1.INTRODUCTION Fifth-generation networks include many technologies that offer features for users. Still, at the same time, they may cause new problems, which refer to the deterioration of the service provided by the network due to a failure in a particular stage,suchaswhensettingspecific parameters[1]. Despite the significant benefit of small cells, they cause difficulties that lead to a deterioration in the quality of service, such as interference, unnecessary and frequent handovers, failed handovers, and Ping-Pong events[2]. Failure management [1] is one of the basic concepts in cellular networks, and given that the handover process is critical, especially in high-density networks that are accompanied by high mobility speeds, it is necessary to reduce failurecasesduringitsimplementationtomitigatethe negative impact on the quality of service provided by these networks. The heavily loaded target cells are one of the reasons for the failure of handover operations since they are either unable to serve a new user or limitedserviceprovided. This will result in unsuccessful handoveroperationsandlead to a deterioration in the quality ofservice in the network due to the loss of packets resulting from the connection interruption. 2. THE IMPORTANCE OF THE RESEARCH AND ITS OBJECTIVES The importance of this research lies in proposing a solution that ensures the target station has sufficient resources to serve a new userbefore executing the handover decision, thereby ensuring that the user moves to a cell that can serve them first and provide convenient service for their requirements secondly.Thisultimatelyachievestheresearch goal of improving service quality using the enhanced algorithm. The research addresses the study of handoverproblems in 5G, shedding light on the concept of cellloadanditsimpact on handover performance,and then proposes a modification to the handover decision-making algorithm that ensures not moving to a congested cell. We then follow with practical implementation, involvingchoosing the most suitable toolto simulate the required network, building the network, specifying the parameters, executing the simulation, and analyzing the results. Finally, the research concludes with recommendations that define the future direction of the research. 3. RELATED WORK Several existing works were done in research to enrich the field of improving the handover process in the 5G wireless networks. In [3], anFL-based handoverschemewas proposed for5GUDNs,theproposedschemecandynamically adjust two handover parameters, HOM and TTT, byusingthe SINR and horizontal moving speed of UE as inputs to a Fuzzy logic controller. While the authors of [4] investigated various MRO (Mobility Robustness Optimization) algorithms for the 5G network at different mobility speeds and system setting scenarios to address Mobility Management (MM) issues during user mobility between cells to ensure a smooth connection.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 859 On the other hand [5] presented the common types of Machine Learning and the techniques used from each typeto optimize the HCPs of the MRO functions. Moreover, high- mobility-aware and network topologies are presented in the MRO function forfurther system enhancements. Besides,the survey further highlights several potential problems for upcoming research and provides futuredirectionstoaddress the issues of next-generation wireless networks. In the paper[6] the effect of different HCP settings on 5G network performance was verified by analyzing fixed HCP settings in various scenarios to explain the need for applying more advanced technology in 5G networks. The proposed algorithm in this paper was used to assess the HCP settings. These optimization values were estimated according to the speed of UE’s and loads of cells. 4. Background 4.1. Handover problems (handover failure): The increaseindeploymentdensityofsmallbasestations (SBS) is a solution to obtain high data traffic mobility in 5G wireless networks. However, it causes an increase in ping- pong events, accompanied by increased delay and handover failure. Ping-pong events occur when the serving cell hands over control of the mobile device to the target cell. Then, the target cell hands back control to the previous serving cell within alimited timeafter the last handover [7],causinghigh signaling due to the messages exchanged between network elements resulting from unnecessary handover operations. Therefore, reducing frequent and unnecessary handover operations saves network resources and improves overall system performance [8]. Mobility failure occurs when the user equipment fails to establish a radioconnectioninthesourcecellorcompletethe handover process in the target cell. There are different reasons for handover failure, either due to a problem in synchronizing the necessarysignaling messages to complete the handover process between the source and target base stations or due to the loss of one of these signaling messages caused by a problem in the radio link. Moreover, attempting to move the phone to an already congested cell [2] can also result in call failure due to resource limitations, making the handover operation unsuccessful. If the call is completed later, this process will reduce the service quality provided to the existing users and the new user. The timing of the handover process is critical. Therefore, the main challenge of Mobility Robustness Optimization (MRO) functions, which is the automatic adjustment of handover control parameters, is to avoid failure cases as much as possible. For example, reducing the values of TTT and HOM can cause an early and unnecessary handover, as there are types of errors that occur due to the user moving at different speeds, such as the Too Early Handover problem, which occurs when the signal strength of the target station is still weak [9], The early handover occurs due to the low values of the handover control parameters (HCP), which are then adjusted to high values due to poor connection, this causes failureto connect quickly when the handover process begins. As a result, the device will return to the previously served station, resulting in additional handover operations. In addition to the Too Late HO problem, at high speeds, very late handover occurs due to the user gnsssap through several cells within a short period. In this case, adjusting low values for the handover control parameters is essential to reduce the failure rate of the radio link [10]. 4.2. Radio resources in the cell: Where: Is,j(τ) isa binary indicator that takes the value of 1 if user j is served by cell s. Ns,j (τ) is the number of reserved physical resource blocks (PRBs) by cell s for user j during the period t. PRB refers to the total number of available resource blocks in cells, where all cells have the same maximum limit. Therefore, the total number assigned by a cell at any given moment cannot exceed the value of . When the RBUR value reaches 1, the cell resources are exhausted, and any new user enteringthecellwilleitherhave their connection dropped or will be served at a lower data transmission rate than needed. 5. Proposed Algorithm: Load-based Handover Decision-making algorithm (L-HD): This study proposes a modification to a previously introduced algorithm [13], called (RHOT-FLC) , designed for handoverdecision-makingandcontrolparameterassignment based on fuzzylogic. The basicalgorithmwasobservedtonot consider the load value of thebasestationsbeforemakingthe handover decision, which could result in user issues that affect service quality. (1) The smallest unit of radio resources is the physical resource block (PRB), which consists of 12 subcarrier frequency slots. The cell load is measured by the Resource Block Utilization Ratio (RBUR) [11], defined as the ratio of the reserved physical resource blocks (PRBs) by the cell s to the total number of available blocks in the same cell. The RBUR ratio directly determines the use of the resource block by the number of devices that can be served at a specific data transmission rate and with specific latency constraints. Equation (1) [12] expresses the average RBUR in the cell.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 860 Also, the algorithm does not consider the case of a failed connection establishment with the target station. Therefore, it is necessary to add a condition to test the load value of the cell before deciding while also ensuring the possibility of connecting to an alternative station in case of a failed connection with the target station. The following steps represent the stagesofimplementingthe proposed algorithm (L-HD) , as shown in Figure 1: 1. Both the Radio Signal Quality (RSRQ), Radio Signal Power (RSRP), User Equipment (UE) velocity, and Cell Load are considered inputs to this algorithm. 2. The RSRP value for neighboring cells is extracted from measurement reports, sorted,andcomparedtotheRSRP value of the serving cell. 3. The neighboring cell values are arranged according to the Cell Load value, which is calculated based on Equation (1) and takes values ranging from [0,1], where a higher value indicates a heavier load on the cell. 4. The condition for triggering the handover process is tested based on the RSRPs < RSRPt , where if it is met, the handover process is not executedsincethemobilephone has not approached the cell boundaries, and the signal strength received from the serving station is still higher than that received from the target station. On the other hand, if the condition is not met, then there is a need to trigger the handover process, and since the A3 event is used in this algorithm, the fuzzy controller is reliedupon to obtain the TTT & HOM values. 5. RSRQ, RSRP, and UE velocityareconsideredinputstothe fuzzy controller, which are divided into levels indicated in Table (1), where the values of these parameters are transformed into fuzzy sets, and the degree for each rule is calculated, resulting in obtaining the controlleroutput which is both HOM & TTT values. 6. If the condition of equation (2) is not met the handover process is not executed, and the current cell remains the serving cell. However, if the condition is met, the Cell Load value of the candidate cell is tested, and if it is capable of serving a new user (Meaning the value of BRUR did not exceed 0.9), the handover decision is executed. Otherwise,thehandoverprocessisexecutedat the second-best base station. Fig -1: flowchart of the proposed algorithm (2)
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 861 Table (1) shows the number of levels of input and output[13]: Table -1: input and output levels Input Degree Range UE Velocity Slow 0 to 30 km/h Moderate 25 to 70 km/h High 65 to 135 km/h Very high 130 to 160 km/h RSRP Weak -160 to -95 dBm Medium -100 to -73 dBm strong -80 to -20 dBm RSRQ Poor -60 to -18 dB Good -22 to -12 dB Very good -14 to -6 dB Excellent -10 to +20 dB output TTT v. small 0 to 220 ms Small 210 to 380 ms Average 370 to 520 ms Large 510 to 640 ms HOM v. small 0 to 0.3 dB Small 0.2 to 0.5 dB Average 0.4 to 0.8 dB Large 7.0 to 1 dB 6 . Performance Evaluation 6.1. Simulation Setup: It was decided to implement the practical section using MATLAB 2022a, relying on the 5g toolbox [14] [15] [16], which provides functions compatible with the established standards forfifth-generationwirelessnetworks.Inaddition, it includes reference examples for modeling, simulation, and verification of communication systems. The network was built in a standalone mode based on previous studies showing better handover performance in standalone architecture [17]. In addition, the Fuzzy Logic Toolbox [18] was relied upon as it provides functions and applications for analyzing, designing, and simulating fuzzy logic systems. The fuzzy logic controller was designed as shown in Figure 2, Fig -2: fuzzy logic controller and the parameters shown in Table (2) were adopted, which were chosen based on [13] and according to standards. Table -2: Simulation parameters settings Parameters values # of gNB 50 Cell radius 150 m Tx Power of UE 23 dBm Tx Power of gNB 46 dBm Number of Measured UE 10 UEs Maximum Number of UE per Cell 10 Channel bandwidth 400 MHZ Path loss mode Log-normal path loss model (path loss exponent=3.0) Fading Model Friis spectrum propagation loss model Mobility model Constant velocity mobility model Carrier frequency 25 GHz UE velocity Up to 160 km/h TTT range Adaptive: 0 ms … 640 ms HOM range Adaptive : 0db … 1 dB Simulation time 30 s 6.2. Result analysis: The performance of the proposed algorithm (L-HD) was evaluated by comparingitwiththe(RHOT-FLC)algorithmfor three different values of user equipment velocity in terms of several metrics, including quality of service and handover performance. Because improving the handover performance contributes to improving the network's overall performance [19]. 6.2.1. Standard deviation of load: The standard deviation of the load indicates the load balance level in the network cells and iscalculated as follows in equation (3): Where PBURNet(t): the averageload in the networkconsistingof S cells during a period t . S: the number of active cells in the network. RBURs(t): the average load incell s at time t. The value of σ ranges from 7 to 1, and the closer the value is to zero, the more balanced the network is. Chart 1 shows the performanceevaluationofthealgorithmin terms of the standard deviation of the load. The proposed (3)
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 862 algorithm (L-HD) performed better than the (RHOT-FLC) algorithm since it ensures that the target cell's average load does not exceed 0.9 or it transfers the user tothesecond-best station, thus contributing to load distribution between cells and reduces the difference between them. Chart -1: standard deviation of load 6.2.2 Average throughput : The comparison of the two algorithms in terms of average throughput is shown in Chart 2, for three different user equipment velocities (10, 60, and 100 km/h). The proposed algorithm (L-HD) gave a higher throughput than the baseline algorithm since it guaranteestheuser'stransfer to a cell capable of serving itwith sufficientresources.Incase of failure to transfer to the candidate cell, there is an alternative celltocompletethehandoverprocess,resultingin a reduced probabilityofconnectioninterruptionbetweenthe user equipment and the cell. Moreover,sinceitcontributesto load balancing between cells, it increases the utilization of resources, resulting in higher throughput. Chart -2: average Throughput 6.2.3 Packet Loss Rate (PLR) : Chart 3 shows the evaluation of the proposed algorithm (L- HD) in terms of packet loss rate, which showedimprovement over the baseline algorithm (RHOT-FLC). The improvement ensures a reduction in the probability of connection interruption resulting from the inability of the target cell to serve the user. Chart -3: Packet Loss Rate 6.2.4 Handover failure : Chart 4 shows the evaluation of the proposed algorithm in terms of the failure rate of handover processes. The improvement in (L-HD) contributed to a decrease in the handover failure rate due to a reduction in the unsuccessful handover process. The improvement isevidentatlowspeeds since high speeds cause an increase in the handover rate per second, which increases the likelihood of handover process failure. Chart -4: Handover failure 7. CONCLUSIONS This research focuses on improvingapreviouslyproposed algorithm for making handover decisions and selecting control parameters for handover. Theimprovementincludes a test that is applied before making the handover decision, in which it is verified whether the candidate cell can serve a new user. If it is unable to do so, the handover operation is executed to the second-best base station. This improvement
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 863 reduces the probability of establishing a failed connection with a congestedcellandimprovesnetworkbalancesincethe phone involved in the handoveroperationwillbetransferred to the second-best cell, thus distributing the burden among cells. The results have shownthat theproposedalgorithm(L- HD) improves the standard deviation of the load in the network, average throughput, packet lossrate,andhandover failure rate. Based on the results obtained, the focus will be on improving the solution to increase the network load balancing ratio with the shortest possible latency. ACKNOWLEDGEMENT The authors wish to acknowledge the Faculty ofInformation Engineering at Tishreen University for their support of this research. REFERENCES [1] Tarrias A, Fortes S, Barco R. Failure Management in 5G RAN: Challenges and Open Research Lines. IEEE Network. 2023;1–7. [2] Gures E, Shayea I, Alhammadi A, ErgenM,MohamadH.A Comprehensive Survey on Mobility Management in 5G Heterogeneous Networks: Architectures, Challenges, and Solutions. IEEE Access. 2020;1–1. [3] Hwang WS, Cheng TY, Wu YJ, Cheng MH. Adaptive Handover DecisionUsingFuzzyLogic for5GUltra-Dense Networks. Electronics. 2022 Oct 12;11(20):3278. [4] Saad WK, Shayea I, Hamza BJ, Azizan A, Ergen M, Alhammadi A. Performance Evaluation of Mobility Robustness Optimization (MRO) in 5G Network With Various Mobility Speed Scenarios. IEEE Access. 2022;10:60955–71. [5] Tashan W, Shayea I, Aldirmaz-Colak S, Aziz OA, Alhammadi A, Daradkeh YI. Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions. IEEE Access. 2022;10:111134–52. [6] Saad WK, Shayea I, Hamza BJ, Mohamad H, Daradkeh YI, Jabbar WA. Handover Parameters Optimisation Techniques in 5G Networks. Sensors. 2021 Jul 31;21(15):5202. [7] Tashan W, Shayea I, Aldirmaz-Colak S, Ergen M, Azmi MH, Alhammadi A. Mobility Robustness Optimizationin Future Mobile Heterogeneous Networks:ASurvey.IEEE Access. 2022;10:45522–41. [8] Gures E, Shayea I, Ergen M, Azmi MH, El-Saleh AA. Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks:ASurvey.IEEEAccess. 2022;10:37689–717. [9] Tashan W, Shayea I, Aldirmaz-Colak S, Ergen M, Azmi MH, Alhammadi A. Mobility Robustness Optimizationin Future Mobile Heterogeneous Networks:ASurvey.IEEE Access. 2022;10:45522–41. [10] Alhammadi A, Haslina Hassan W, A. El-Saleh A, Shayea I, Mohamad H, Ibrahim Daradkeh Y. Conflict Resolution Strategy in Handover Management for 4G and 5G Networks. Computers, Materials & Continua. 2022;72(3):5215–32. [11] Addali KM, Chang Z, Lu J, Liu R, Kadoch M. Mobility Load Balancing with Handover Minimizationfor5GSmall Cell Networks.2020International WirelessCommunications and Mobile Computing (IWCMC). 2020 Jun. [12] Addali KM, Bani Melhem SY, Khamayseh Y, Zhang Z, Kadoch M. Dynamic Mobility Load Balancing for 5G Small-Cell Networks Based on Utility Functions. IEEE Access [Internet]. 2019 [cited 2022 Mar 24];7:126998– 7011. Available from: https://ieeexplore.ieee.org/iel7/6287639/8600701/08 826267.pdf [13] Alraih S, Nordin R, Abu-Samah A, Shayea I, Abdullah NF, AlhammadiA.RobustHandoverOptimizationTechnique with Fuzzy Logic Controller for Beyond 5G Mobile Networks. Sensors. 2022 Aug 18;22(16):6199. [14] 5G Toolbox User’s Guide available at https://www.mathworks.com/help/pdf_doc/5g/index.h tml [15] 5G Toolbox Getting Started Guide available at https://www.mathworks.com/help/pdf_doc/5g/index.h tml [16] 5G Toolbox Reference available at https://www.mathworks.com/help/pdf_doc/5g/index.h tml [17] Douba L, Ahmad Mahmoud Ahmad, Ahmad Saker Ahmad. “A Study of Handover Performance inBothSA& NSA Architectures as a Step to Achieve Load Balancing in 5G Wireless Networks” Vol. 44 No. 5 (2722):Tishreen University Journal of Research and Scientific Studies - Engineering Sciences Series. [18] Fuzzy Logic Toolbox™ User's Guide, available at: https://www.mathworks.com/help/pdf_doc/fuzzy/fuzz y_ug.pdf [19] Tanveer J, Haider A, Ali R, Kim A. An Overview of Reinforcement Learning Algorithms for Handover Management in 5G Ultra-Dense Small Cell Networks. Applied Sciences. 2022 Jan 3;12(1):426.
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