Optimization Of User Behaviour Based Handover
Using Fuzzy Logic
Sukhmandeep kaur, Dr.Sonia
Abstract :In order to enhance the customer satisfaction, handover optimization is necessary in LTE networks. Particularly, users using real time traffic need to experience continuous connectivity. Thus, various researches have considered two parameters i.e. RLF and HOM along with fuzzy interference system, for the handover process. The handover process in existing system is performed effortlessly but it still lacks in the overall performance of the handover. In the present paper, fuzzy logic control system is proposed that takes in to account three inputs such as RLF, HOM and the speed. It is ana-lyzed from the study that the speed also has significant effect on the user connectivity and handover and it gave the output i.e. delta HOM. Performance parameters are taken into account and comparison analysis is performed between existing [15] and proposed approach which demonstrates that pro-posed approach is the efficient approach than existing ones.
Index Terms : Radio link failure (RLF), Fuzzy logic controller (FLC), Handover Margin ( HOM), Quality of service (QoS).
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I.
INTRODUCTION
Mobile data continuous growth emerges efficient technologies to satisfy the required quality of service (QoS) services. Mo-bility has the significant role to play in the present and next generation cellular communication system which makes it easier for the user to change their location while being con-nected to the network. Increasing the speed of vehicles on one side and the need to use Internet almost everywhere at any time emphasizes on the necessity and the importance of mobility in wireless networks. Mobility with the high speed is a major challenge, and LTE as long term evolution has prom-ised more than former technologies to overcome this chal-lenge. To accomplish this purpose, minimum possible delay and packet loss in voice transmission and reliability in data transmission are desired. So choosing the optimum proce-dure for the handoff is one of the major concerns. While mov-ing user face various propagation conditions and different interference levels. It might happen that the cell which serves the user equipment (UE) is not the best cell anymore and the UE needs to be handed over to another cell. For this purpose, while the UE is connecting to the serving cell, the UE needs to continuously monitor neighbouring cells. The cell that serves the UE is called the source cell or the serving cell and the cell that the UE is handed over to is called the target cell. In LTE, two different mobility modes can be distinguished: mobility in IDLE mode and mobility in CONNECTED mode. When the UE is in IDLE state and changes the cell, the pro-cess is called cell reselection, and while the UE is in CON-NECTED mode and changes cell, the process is called hand-over. The network controls the UE transitions from IDLE mode to CONNECTED mode and vice versa.
The handover used in the LTE is hard handover, instead of soft handover in WCDMA networks, because the UE connec-tion to the old (source) eNB breaks before the new connecconnec-tion to the new (target) eNB is established; there will be therefore a handover interruption time in the user plane. As data loss should be avoided in handovers, data forwarding is developed for LTE networks.
As mentioned earlier, there is no soft handover in LTE, as well as any centralized controller node. Therefore the respon-sibility of handling data in handover is assigned to the eNB. In more detail, data buffering and protection during handover due to user mobility in the Evolved-UMTS Terrestrial Radio Access Network (E-UTRAN) is assigned to the Packet Data Convergence Protocol (PDCP) layer in the eNB, which is a sub layer of the data link layer.
Figure-1: Handover in the LTE Network [1]
Handover is one of the procedures followed by the UE to pro-vide seamless mobility. The handover is beneficial for the end-user although it increases the complexity of the network and the UE. Radio interface resource consumption and net-work topology can be considered as netnet-work complexity. Without handover the end-user cannot experience seamless mobility and may lose connectivity while leaving a cell and entering another cell. Figure 1 is demonstrating the detailed process of handover taking place in the LTE network.The ob-jective in networks like LTE is providing seamless mobility for the user, and at the same time keeping the network manage-ment simple. However, there are different types of the hando-vers operated in LTE network. These networks are mentioned below:
Types of Handover in LTE network:
Intra-LTE Handover: the source and target in this handover scheme are present in the same LTE
net-_____________________
work which means that handover occurs in the same cell.
Inter-LTE Handover: in the inter-LTE, the handover occurs along the LTE nodes. This involves the dif-ferent eNB (Inter-MME (Mobility Management Enti-ty)and Inter-SGW (Serving Gateway))
Inter-RAT: the handover which occurs between dif-ferent radio technologies is known as Inter-RAT handover, e.g. the handover from the LTE network to the WCDMA network which is similar to the inter Vendor Handover.
The optimization of HO(Handover) parameters are crucial in many aspects of the network, as it not only affects the mobility aspect, but can also affect coverage, capacity, load balanc-ing, interference management, and energy consumption to name a few. Furthermore, the tuning of handover parameters also has an influence in several other metrics used by opera-tors which are important to determine if the network is per-forming well, such as ping-pong rate, call dropping probability, call blocking probability, and early or late handovers [2].
II.
LITERATURE REVIEWWithout need of the human intervention the LTE network needs to be independent and must have capability of self-organizing. The fuzzy system is similar to the human nature and natural language rather than the traditional programming languages. The handover which involves the self-optimization based on the fuzzy logic approach are the efficient systems. Fuzzy logic control can be used in the handover for the opti-mization of the self-optimizing parameter like handover mar-gin and time-to-trigger (TTT) depending on the multiple crite-ria namely handover ping-pong (HOPP), user equipment (UE) speed and the handover failure (HOF). There are various studies present which have emphasized the enhancement of the self-optimizing parameter of the handover mechanism. The technique involving FLC is used for self-optimizing mechanism for adjusting the network parameters automatical-ly. In [3-7], the FLC was used to balance the loads and in the [8-12] the FLC has been used for optimization of the hando-ver parameters. From both the research implication it is clear that the FLC is a powerful tool for setting the parameters au-tomatically and it has potential to change the dialect into the simple terms in the form of logical rules. Fuzzy Q-learning is used to solve various problems in the fuzzy system. In [13], the traffic sharing in GSM EDGE Radio Access Network (GERAN) is managed by the FLC which repeatedly tune the handover margins to reduce the call blocking during the communication in the network. The dynamic load balancing in the actual natural urban area situation is managed by the im-plementing the FLC [14]. From these references it can be concluded that the FLCs are quite useful in optimization of the network parameters automatically. In [14] the load balancing concept with the help of FQL was further applied in the LTE networks. In the different works, there is not much information about the implication of the FQL algorithm and its various functioning and the components used. After analysing the systems it was discovered that the FLC was not largely capa-ble of managing the uncertainties. The system performance is reduced significantly when the uncertainties enter the system. One more drawback of the conventional work is that it ac-counts only two parameters for FLC due to which FLC can’t be used for more applications and makes it versatile. So there
overcome the problems faced in the earlier approaches. Fol-lowing these concerns the FLC is substituted with the type 2 FLC system in this paper. In [15], author used fuzzy Q-learning for optimizing the two handover problems: RLFs and ping-pongs. Firstly, Handover Margin (HOM) is required to be reduced in order to reduce too late handover, and eventually there is requirement of increasing the HOM to decrease the unnecessary signalling. The proposed algorithm classifies the users into four classifications in relevance to the speed and the data traffic. It leads to increase the some user satisfaction by maintaining the overall handover problems within accepta-ble limits. Author applied fuzzy based q-learning for every type of users. The results of this system proved to be very effective.
III.
PRESENT
WORK
Various researchers have considered the two parameters i.e. the radio link failure (RLF) and handover margin (HOM) along with the fuzzy inference system, for the handover process. HOM is a combination of the hysteresis, the A3-offset and the difference of cell individual offsets of the target and serving cells. The radio link failure (RLF) is the major problem in the network. When the RLF occurs the user doesn’t remain con-nected to the network. It is divided into three categories i.e. handover too late, handover too early and handover to wrong cell. The RLF rate is determined by the ratio of total number of RLFs to the sum of the numbers of RLF and handovers. The formula to compute RLF is:
From the literature study it is concluded that various other parameters can also be considered for the handover optimi-zation and it was found that the speed also has significant effect on the user connectivity and handover. From the survey it was found that when the user moves with fast speed the connectivity from the network is weakened and handover is also affected. So in the present paper along with the fuzzy logic control, RLF and HOM the speed is also taken into ac-count. The formula for speed is:
Here d is the distance in the network user moving along and t is time period interval. So speed is the distance travelled by the object per unit time.
Figure-2: Proposed Fuzzy handover system.
In this system the mamdani is the type of fuzzy inference sys-tem. It was firstly used by the Ebrahim Mamdani in 1975 to control the steam engine using the linguistic set of rules. Mamdani fuzzy system uses 12 set of if-else-then fuzzy rules. Three inputs used in the present system are RLF, HOM, and speed. These three inputs have three membership functions namely high, low, and medium. The total output of the system is known as delta HOM. It is expressed as shown below:
∑
∑ Where i is the rule number and is the input for the corre-sponding rule.
In order to apply the proposed system, the network architec-ture with the area of is defined. The entire network is divided into different cells. 20 cells are considered for the present system.
Figure-3: Network Architecture
Users can be allocated dynamically in the network. User range from 10 to 40 was taken into consideration for the anal-ysis of the system.
Figure 4. Type-2 fuzzy logic system.
The proposed system shown in figure 2 represents the com-plete system to operate the handover. As, Fuzzy type 2 is implemented in the novel system due to its advantageous nature, the developed fuzzy system is shown in the figure 4.In the table the network architecture of the proposed system is shown. Here area is chosen. The entire net-work has been divided into 20 different cells. The cell radius of 0.5 km has been chosen. The total bandwidth used for the system is 10 MHz. The Handover Margin (HOM) was in the range of [-6, 6] dB. Experiment was performed first by keep-ing the 10 users in cell and then 20 to 40 users. Durkeep-ing this experiment the movement of the users in the network was kept random so as to measure the results against the high speed mobility.
Table.1: Parameters for the network architecture Sr.
No.
Parameters Values
1 Area 100x100
2 Cell count 20
3 Users In Each Cell 10-40
4 Mobility model Random direction
5 HOM Range [-6, 6] dB
6 Carrier frequency 2 GHz
7 Total bandwidth 10 MHz
8 Radius of macro-cell 0.5 km
Type-2 fuzzi-fier Input
Type-2 fuzzifier Output
Type -Reducer Rules
Inference
De-fuzzifier
Fuzzifier
Type-1 Fuzzy reduced Output Crisp Input
(a)
( b)
(c)
Figure 5: Membership functions obtained for (a) RLF, (b) HOM, (c) speed and (d) Delta HOM
The membership functions (MFs) for different inputs and out-put are attained through the fuzzy logic system. Figure 5 shows the graphs of MFs for inputs (RLF, HOM, speed) and output (delta HOM.) There are three different levels for the inputs whichare used to generate the fuzzy rules. The output of the system, say delta HOM has different phases in mem-bership functions such as extremely low, very low, low, nor-mal, high, very high, and extremely high. The rules acquired from the membership functions are explained in table 2.
Table 2: Rule block for different inputs Sr.
No.
INPUT OUTPUT
RLF HOM SPEED DELTA HOM
1 Low Low Low Normal
2 Low Low Medium Normal
3 Low Medium High Normal
4 Low Medium Low Low
5 Medium High Medium Low
6 Medium High High Very low
7 Medium Low Low Very low
8 Medium Low Medium Extremely low
9 High Medium High Extremely low
10 High Medium Low Extremely low
11 High High Medium Extremely low
12 High High High Extremely low
IV.
V.
RESULTS
AND
DISCUSSION
Figure-6: Too late HO for SR User
Table3: Rate Percentage for Too Late HO For SR Users
Users WO OL OFQ Proposed
10 14 4.7 6.25 0
15 10.5 4.45 4.25 1.1
20 7 4.25 2.2727 2.1277
25 9 4.26 4.15 1.41
30 11 4.25 6.0606 0.7246
35 9.5 3.97 5.82 0.36
40 8 3.7 5.5838 0
Too late value of handover for the SR (shown in figure 6) with our proposed system is 2.1277% while without optimization (WO), according to literature (OL), and with optimized fuzzy Q-learning technique is nearby 7%, 4.25%, and 2.27% re-spectively for the 20 users. For the increased number of users also the value of late handover percentage with proposed scheme is 0.7246% and 0% for the 30 and 40 users respec-tively. This verifies that our system per forms better than other systems for handling the late handover in real time user envi-ronment. The tabular chart (table 3) for real time users at-tained the values which are compared with the existing works (OL, WO, OFQ).
Figure-7: Too late HO for HR Users
Table 4: Rate Percentage for Too Late HO For HR Users
Users WO OL OFQ Proposed
10 7.9 1.5 0 0
15 7.9 1.55 0.99 0
20 7.9 1.6 1.9802 0
25 7.85 1.3 1.325 0
30 7.8 1 0.6667 0
35 7.9 1 0.34 0.245
40 8 1 0 0.4926
Figure-8: Ping-pong for HN users
Table 5: Rate Percentage for Ping Pongs For HN Users
Users WO OL OFQ Proposed
10 0 0 0 0
15 0.1 0.45 0.1 0
20 0.2 0.9 0.2 0
25 0.2 0.95 0.2 0
30 0.2 1 0.2 0
35 0.25 1 0.35 0
40 0.3 1 0.5 0
Figure 8 reveals the ping pong (PP) for HN users with pro-posed scheme is 0% for the users ranging from 10 to 40 (fig-ure-8). With the WO and OFQ system the PP is 0.2% while for OL, it is 0.9%. the corresponding values for other cases are shown in the table 5 in a comparative view.
Figure-9: Ping-pong for SN users
Following to this, the results obtained for SN users are repre-sented in the graphical view in figure-9. The PP is zero per-cent for our proposed scheme and OFQ is also able to main-tain the zero PP level. The PP for the OL and WO is 3 % and 4.5% respectively. To verify the robustness of the system the number of users was increased from 20 to 30 and then 40. Even then also the proposed system gives better results for both the HN and SN users. So it can be seen that our pro-posed system not only reduces the PP effect but also mini-mize the problems faced during handover. The table 6 shows the comparative values of the users to number of users in each cell.
Table 6: Rate Percentage for Ping Pongs For SN Users
Users WO OL OFQ Proposed
10 0 0 0 0
15 1.5 2.28 0 0
20 3 4.5 0 0
25 2.5 3 0 0
30 2 1.5 0 0
35 1.45 1.6 0 0
40 0.9 1.7 0 0
VI.
CONCLUSION
AND
FUTURE
SCOPE
lysed on the simulation software MATLAB. The evaluation of the proposed system is performed in terms of, too late HO and ping pong. The results for the real time users and non-real time users in the network are shown. The results have verified that our system performs better than other systems for handling the late handover in real time user environment. Similarly for the non real-time users, the simulation results represents that proposed approach reduces the PP effect and also minimize the problems faced during handover in an effi-cient way as compared to other existing approaches. Thus, it all clearly shows that proposed approach is the efficient one.
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