QoS-aware handover algorithm in dense small cell heterogeneous networks

Bakhtiar A. Karim and Li ZhangSchool of Electronic and Electrical Engineering, University of Leeds, UK.

E-mail: bakhtiar.ali@spu.edu.iq and L.X.Zhang@leeds.ac.uk

Abstract - to improve the handover performance in heterogeneous networks (HetNets) with dense deployment of small cells, we propose a quality of service (QoS) aware handover (HO) algorithm. The proposed algorithm makes the HO decision based on the received signal to interference and noise ratio (SINR) from available networks. This paper evaluates the impact of interference on the proposed HO algorithm and the system throughput, by considering various transmit power and small cell density. The simulation results show that interference not only affects user throughput, but also probability of HO failure and unnecessary HO. It is shown that using SINR as HO metric provides higher mean throughput to mobile users than RSS based HO algorithms, and cuts down the overall number of HOs, probabilities of HO failure and unnecessary HO.

I. INTRODUCTION

Providing high data rate services for mobile users remains as a challenging question for telecommunication network operators. Several techniques have been proposed to enhance data rate and overcome the coverage issue in the hole zones which cannot be covered easily by macro BSs. One of the most promising techniques to deal with the user requirements in the next generation cellular systems is the deployment of small size, low power, and cost-effective BSs which cooperate with the existing macro BS in a complementary manner [1]. However, when the number of small cell base stations (SCBSs) increases in a given area, the inter-cell interference becomes a critical issue due to the spectrum sharing between the macro BS and SCBSs. Such interference can be significant and greatly affects mobile user throughput [2].

Most of the presented handover algorithms in literature rely on the Received Signal Strength (RSS) [3,4,5], in which the HO is triggered whenever the RSS of the serving network deteriorates below a pre-defined threshold. However, this approach has poor performance in HetNets due to ignoring the effect of interference as a result of the dense deployment of small cells. To solve this problem, the negative effect of interfering signals must be taken into account.

Using SINR as a primary metric of VHO in a heterogeneous environment composed of Wi-Fi and WCDMA has been widely studied in literature. In [3], the authors have made a comparison between SINR and RSS based VHO in terms of user throughput by taking different load factors and noise powers into account. The same work with a slightly improvement has also been presented in [4] for WLAN and WCDMA HetNets. In [5], the study of SINR and RSS based VHO has been conducted for WiFi-WiMAX HetNets. The authors have confirmed that SINR based handover has outperformed the RSS based handover algorithm in providing higher system throughput, and increment becomes more prominent at higher data rate. [6] has introduced a multi-criteria decision making (MCDM) algorithm for LTE-A network. Due to considering three more criteria, the residence time in the target cell, reference signal received power (RSRP), and the movement direction of the user, beside SINR, the effect of interference on user performance and handover algorithm has not been indicated

clearly, and MCDM algorithm could have relatively higher HO latency.

Our work is different from the previous ones because of the following reasons. First, there is a lack of research regarding how interference affects not only throughput but also HO failure and unnecessary HO in small cell HetNets. In the other words, none of the papers in literature has addressed the relationship between interference and probability of HO failure and unnecessary HO in such HetNets. Second, an analytical comparison between SINR and the traditional method has not been done yet for small cell HetNets. To this end, we develop a novel handover technique for small cell HetNet inspired by the work presented for WCDMA WLAN networks in [4]. In the proposed technique, the MT continuously measures and compares the received SINR from both serving and target BSs, then the user is connected the to a BS with higher SINR. Due to this feature, the proposed technique always guarantees higher throughput than the traditional method and lower call drop probability. By applying the proposed technique, we aim to evaluate the impact of interference on system throughput and handover algorithm in terms of HO failure and unnecessary HO in different scenarios considering different small cell transmitting powers and various density of small cell deployment.

This paper is composed of five sections and organized as follows. Section 2 explains the system model. While the strategy of the proposed algorithm is analyzed in section 3. The simulation results of the proposed technique and the traditional RSS based handover algorithm are presented and discussed in section 4. Section 5 concludes the main points of the paper.

II. SYSTEM MODEL

In this paper, we consider a two-tier HetNet. In such network, the small cells are deployed within the coverage area of the Macrocell in a random manner. Our system model consists of one central macro BS, and five outdoor SCBSs with a radius 50 metre are randomly distributed within the macro BS coverage area as shown in figure 1 below.

Fig. 1 System scenario

The proposed propagation model in [7] is used to calculate the path loss for Macrocell users. According to [8], when the carrier frequency is 2GHz, the path loss for small cell users is expressed as:

PL¿140.7+37.6 log10 (R )+log(F ), (1)

where

R is the distance between SCBS and the mobile user in km.

log(𝐹) represents the log–normal distribution shadowing with the standard deviation 10dB.

In small cell HetNets, there are two types of handovers. The first type is the handover from Macrocell to small cells and vice versa. The second type is the handover between small cells. In this paper, we only consider the handover between Macrocell and small cells.

III. SINR BASED HANDOVER ALGORITHM STRATEGY

From the Shannon capacity formula, the maximum obtainable data rate is represented as:

R=W log2 (1+γ )bps, (2)

where

W denotes the bandwidth of the signal

γ denotes the received SINR

Maximum data rate that users receive from the small cell (RSC) and Macrocell (RMC) can be calculated as:

RSC=W SC log2 (1+γ SC )bps, (3)

RMC=W MC log2(1+γMC )bps, (4)

where

W SC and W MC represent the carrier

bandwidths of the small cells and the Macrocell

γSC and γMC respectively

represents the received SINR from the small cell and the Macrocell.

In this research, we focus on the downlink traffic which requires higher bandwidth than uplink. Practically, the desired signal in downlink direction is affected by two types of interference, namely intra-cell interference and inter-cell interference. In this work, the inter-cell interference is considered. The received SINRfrom small cell j by the user i is calculated as follows:

γSC j ,i=GSCj , iPSCjPb+¿¿

(5)

where

PSCj is the transmitted power by small cell j.

GSCj ,i is the channel gain between small cell j and user i.

Pb denotes the power of the background noise

PSCk denotes the transmitted power by small cell k .

GSCk ,idenotes the channel gain between user i and small cellk .

GMC ,i represents the channel gain between user i and the Macrocell

PMC ,i represents the power transmitted by Macrocell to user i

Similarly, the received SINR from Macrocell j by user i is calculated as follow:

γBS j ,i=GMCj ,iPMCj, i

Pb+ ∑k∈SC

(G¿¿SCk ,i¿PSCk)¿¿

(6)

Table 1. Simulation Parameters

The total down link throughput ∅ for a user who

travels through the HetNets can be calculated as:

∅=∑¿ X 1

Xh

RSC×CRT SC+∑¿ X h

X 2

RMC×CRT MS(7)

where

¿ X1is the starting point of the user’ movement

X2 is the end point of the user’ movement

X h denotes the handoff point CRT SC∧CRT BS represent the cell

residence times inside the small cell and the Macrocell respectively.

Based on the measured SINR from the connected and the target BSs, the proposed method observes the obtainable throughput from both BSs. Handover is triggered immediately whenever higher throughput is achieved from the target BS.

IV. PERFORMANCE EVALUATION

In this section, we present and discuss the simulation results of the proposed algorithm in comparison with the traditional RSS based handover algorithm inside small cell HetNets in two different scenarios.

A. Scenario 1

Our aim in this scenario is to observe the response of the proposed algorithm and the traditional RSS based handover technique in terms of user throughput when interference level increases in the network which is mostly the case in the dense small cell HetNets. To achieve this goal, we introduce various interference levels to the network by increasing the transmit power of the small cells without exceeding allowable range which is 30 dBm [9]. In this scenario, co-channel deployment is considered, wherein both the Macrocell and small cells operate on the same carrier frequency which effectively improves spectral efficiency bps/Hz. MATLAB is used to perform the simulation, and the simulation parameters are listed in table 1.

Table 1. Simulation Parameters

Fig. 2. User mean throughput vs Small cells transmit power in HetNets.

Figure 2 shows that user mean throughput decreases by increasing transmit power of the small cells. This is because of the increasing interfering signals in the network particularity at the cell-edges. From the figure, it can be noticed that increasing small cells transmit power from 10 dBm to 28 dBm causes 100Mbps reduction in user throughput (from 210Mbps to 110Mbps) if RSS is used to make the handover decision. Whereas, the proposed algorithm limits this throughput degradation to 50Mbps (from 231Mbps to 181Mbps). Therefore, compared to the traditional technique, the proposed method reduces the negative impact of interference on user throughput considerably.

B. Scenario 2

To realize the concept of future 5G systems, ultra-dense HetNets (UDHNs) has become an attractive area for the current research. In UDHNs paradigm, the network is densified by deploying higher number of low power small cells under the coverage area of conventional Macrocell.

Consequently, in UDHNs the distance between users and BSs becomes even smaller which effectively reduces propagation loss. However, this improvement comes at the expense of stronger inter-cell interference in the network [10]. Our goal in this scenario is to evaluate the adaptability of the proposed technique to ultra-dense small cell

Parameters Values

System bandwidth 5 MHzCarrier frequency 2 GHz

Macrocell radius 1000 m

Macrocell transmit power 10 dB

Small cell transmit power Variable Small cell radius 50 m

Location of small cells inside Macrocell coverage area.

Random

Number of small cells 5

heterogeneous environments, in which interference level is comparatively high. To achieve this goal, we densify the network shown in figure 1 by increasing the number of small cells (from 1 up to 50) with smaller footprint (small cell radius is 20 m).

According to [2], adding small cells to a given area might increase or decrease user throughput, depending on their locations in the network and how to manage the increased interference. In this paper, we assume the worst case scenario, in which no interference cancelation technique such as inter-cell interference coordination (ICIC) is deployed and the small cells are added randomly.

Fig. 3 User mean throughput vs Number of small cells in absence of interference

management technique

Figure 3 shows that the user mean throughput reduces when the number of small cells increases in the network, due to the increased interference. It is noteworthy that when the network is densified by increasing the number of small cells, the performance of the RSS based handover algorithm reduces faster than that of the proposed scheme. This is a clear indicator that the proposed technique is more adaptable to the nature of UDHNs than RSS based handover algorithm by considering the impact of interference.

Due to the high number of small cells in dense HetNets, handover failure and

unnecessary handover are two challenging issues [10]. Handover to small cells is considered to be unnecessary if the traveling time inside the small cell ( T_smallcell ) is shorter than the sum of the handover latencies from Macrocell into small cell T_in and from small cell to the Macrocell T_out (i.e., T_smallcell< T_in + T_out). Whereas, handover failure occurs if the traveling time inside the small cell is even shorter than the handover latency from Macrocell into small cell (i.e., T_smallcell < T_in) [12]. From [13], the traveling time inside small cells can be represented as:

T smallcell=√ 2R2(1−cos x)v2 (8)

where R denotes the radius of the small cells, X is the difference between arrival angle (Xi) and the exit angle (Xo), and v is the velocity of the mobile user, as illustrated in figure4.

Fig. 4 Travelling time in small cell

Fig. 5 Probability of handover failure vs Velocity in UDHNs

Figure 5 shows that probability of handover failure increases proportionally with the user velocity. This result can be interpreted as follows: the UE continuously reports the measurements (RSS or SINR) from surrounding BSs to the serving BS in order to make the handover decision. When the user’s velocity is high, the reported data might not be finalized quickly enough by the serving BS. In other words, when the serving BS completes the handover process, the high-speed user has already left the coverage area of the target network [14], and this causes higher probability of handover failure. Based on our simulation result, the proposed algorithm reduces the probability of handover failure compared to the traditional RSS based handover algorithms.

Fig. 6 Probability of unnecessary handover vs Velocity in UDHNs

Figures 6 shows that increasing user velocity also increases the probability of unnecessary handover. This is due to the fact that traveling time inside the small cell for fast moving users is less than that for slow moving users. Compared to the RSS based algorithm, the proposed technique yields better performance in terms of the probability of unnecessary handovers in UDHNs.

V. CONCLUSION

HetNet is a promising candidate to deal with the ever increasing data requirements in 5G systems, current HO techniques in cellular systems need to be improved in order to adapt to the nature of that network. We have developed the QoS aware SINR based handover technique in a way that the challenging interference problem of such network is taken into account. Simulation results have shown that interference can have a big impact on user performance in terms of user throughput, probability of HO failure and unnecessary HO. The results also confirmed that the impact of interference can be reduced significantly by using SINR as HO criteria instead of the traditional RSS based method.

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