Dynamic Backhaul Sensitive Network Selection Scheme in LTE-WiFi ...

Dynamic Backhaul Sensitive Network Selection Scheme in LTE-WiFi Wireless HetNet Alvin Ting, David Chieng, Kae Hsiang Kwong Wireless Communication, MIMOS Berhad, K.L, Malaysia. {kee.ting, ht.chieng, kh.kwong}@mimos.my

Ivan Andonovic CIDCOM, Dept. EEE, University of Strathclyde, Scotland, UK [email protected]

Abstract—Small-cell deployment within a wireless Heterogeneous Network (HetNet) presents backhauling challenges that differ from those of conventional macro-cells. Due to the lack of availability of fixed-lined backhaul at desired locations and due to cost saving reasons, operators may deploy a variety of backhaul technologies in a given network, combining available technologies such as fiber, xDSL, wireless backhaul and multihop mesh networks to backhaul small-cells. As a consequence, small-cells capacity may be non-uniform in the HetNet. Furthermore, some small-cells backhaul capacity may fluctuate if wireless backhaul is chosen. With such concerns in mind, a new network selection strategy considering small-cell backhaul capacity is proposed to ensure that users enjoy the best possible user experience especially in terms of connection throughput and fairness. The study compares performance of several common Network Selection Schemes (NSSs) such as WiFi First (WF) and Physical Data Rate (PDR) with the proposed Dynamic Backhaul Capacity Sensitive (DyBaCS) NSS in LTE-WiFi HetNets. The downlink performance of HetNet is evaluated in terms of average throughput per user and fairness among users. The effects of varying WiFi backhaul capacity form the focus for the evaluation. Results show that the DyBaCS scheme generally provides superior performance in terms of fairness and average throughput per user across the range of backhaul capacities considered. Keywords - Hetrogeneous Network, Network Selection, LTE, WiFi, Traffic Offload, Backhaul Capacity

I.

INTRODUCTION(HEADING 1)

The increasing pressure for mobile operators to offload data traffic from their 3G, LTE or WiMAX networks to small-cell networks indicates that future mobile broadband networks will largely going to be heterogeneous. This migration is further fueled by the availability of multi-RAT (radio access technology) which allows user devices to connect to 3G, LTE, WiMAX, WiFi, etc, either one at a time or simultaneously. Deploying small-cells such as WiFi raises new challenges for operators especially on backhauling them. There may not be copper or fiber available at desired locations. Furthermore, in the case of a relatively large number of WiFi hotspots, it may be very expensive for operators to backhaul all of them with wired backhaul which provide optimal capacity. In such a situation, an operator may choose the cheapest and most convenient means to backhaul WiFi access points (APs) regardless of technology (Figure 1). Currently, WiFi APs are typically backhauled by optical fiber, xDSL or wireless backhaul links with throughput varying from several to tens of megabits per second. Besides, the maturing multi-hop mesh networks also serve as a candidate to backhaul WiFi APs [1].

K. D. Wong Daniel Wireless LLC Palo Alto, CA, USA [email protected]

Backhauling APs using mixed technologies causes nonuniform AP capacity distribution within the HetNet. Furthermore, with the latest IEEE802.11n technology providing peak physical data rates of 600Mbps, even existing fixed backhaul services are unlikely to offer sufficient capacity for WiFi APs to realize their full potential. Thus consideration of WiFi backhaul capacity is inevitable during traffic offload decision making.

Figure 1: LTE-WiFi Heterogeneous Network with various possible ways of backhauling WiFi cells.

As discussed in [2], to reduce data traffic pressure over mobile networks, alternative networks such as WiFi are used whenever possible for transporting data. The report in [3] also states that most existing smartphones with WiFi capability are configured by default to give higher priority to WiFi over the cellular interface for data transmissions. Research in [4] focuses on providing max-min fairness for multicast in Orthogonal Frequency Division Multiple Access (OFDMA)based wireless heterogeneous networking. A proportional user rate based radio resource management strategy is investigated in [5] for a LTE-WiFi HetNet, where a suboptimal network selection algorithm is introduced to improve the minimum normalized user rate and fairness. In [6] and [7], new network selection strategies are introduced for WiMAX-WiFi networks where network selection is predominantly driven by data rate. The work in [8] and [2] study access network selection for optimal service delivery and QoS to users respectively. To our best knowledge, none of the works above take into consideration backhaul capacity when performing network selection. We propose a Dynamic Backhaul Capacity Sensitive (DyBaCS) NSS which is not only considering access link throughput but is also aware of the available backhaul capacity to preserve fairness among the users. We compare our proposed scheme with commonly used WiFi First and Physical Data Rate based NSSs. Due to space limitation, we only look into fairness performance and average throughput per user in this paper. Hence existing NSSs which look into service specific offloading are not considered here. We are particularly interested in the impact of different WiFi backhaul throughputs ranging from 1Mbps to 20Mbps.

(a)

(b) (c) Figure 2: Network coverage plot based on data rate with x axis and y axis showing coverage size in meter; a) LTE networks, b) WiFi network and c) HetNet (LTE + WiFi) with users

The rest of the paper is organized as follows. Section II describes the simulation approach and network selection algorithms used. In Section III simulation parameters and assumptions are detailed. Results are discussed in Section IV followed by conclusions in Section V. II.

SIMULATION APPROACH AND NETWORK SELECTION SCHEMES

A. WiFi Access Point and User Placement We have investigated the performance of DyBaCS versus other NSSs over a range of parameters and found that it has a number of advantages. Due to space limitations, here we focus our discussion of our simulation results on one of the scenarios, where we have 4 APs with almost the same coverage as a Macro LTE cell (Figure 2a). Matlab is used to model the network where WiFi APs are placed as shown in Figure 2b. As regards the WiFi access, three non-overlapping channels i.e. 1, 6 and 11 are considered for APs’ channel assignment. The channel of each AP is indicated in Figure 2b represented by the channel number on top of each AP. The simulated area is divided into 5-by-5 square meter grids. Co-channel interference among the WiFi APs is calculated and the effective SINR is used to map the data rate or throughput for WiFi networks. The overlay of both WiFi and LTE networks that forms the HetNet for study scenario is illustrated in Figure 2c. As for user placement, a stochastic node location model as in [9] is used to tabulate users’ locations. Users are distributed around points that are cluster centers which are randomly generated as shown by pink dots in Figure 2c. B. WiFi and LTE backhaul WiFi backhaul throughput is varied from 1Mbps up to 20Mbps at which point the throughput of APs saturates, (explained in Section IV). Two backhauling strategies are used to evaluate the NSSs, the first scenario using uniform WiFi backhaul capacity throughout the HetNet and the second scenario using non-uniform WiFi backhaul capacity. LTE backhaul throughput is assumed to be always sufficient. C. Network Capacity Model 1) Single WiFi AP with Max-min Fairness In the coverage area of any single AP with rate adaptation, users are distributed in the network differentiated by their 1,2, . . , where is the user index and physical rate ,

is the total number of users in network. Since the WiFi Distributed Coordinated Function (DCF) allows all users to have fair medium access, it behaves likes max-min fairness in multi-rates system when all users have similar amount of traffic. Therefore within a single WiFi network, we assumed fair capacity sharing for all users. For max-min fair capacity sharing, the average throughput per user assuming simultaneous access can be derived [10] as: (1)

∑

where indicates the throughput efficiency; hence the gives the application layer throughput product of corresponding to a particular link speed. 2) LTE with Max-min Fairness In LTE, resource is being assigned to users in the form of physical resource blocks (PRB) that consists of many resource elements. Since the objective of the study is to provide fair throughput to all users, the same max-min fair bandwidth sharing approach is used where in the LTE case more resource blocks will be assigned to users with worse channels to ensure fairness. This is reasonable, since the scheduler is not fully specified in the standards. In order to simplify the study, the resource element is assumed to be a time fraction allocation parameter as modeled in [5] and the average user throughput is modeled as in Eqn (2), a straightforward modification from Eqn (1): ∑

where and

(2)

is maximum link throughput achievable by LTE user is the total number of LTE users.

D. Network Selection Scheme (NSS) We compare our proposed DyBaCS with two well-known NSSs call WiFi First (WF) and Physical Data Rate (PDR) based NSS. With the max-min fair capacity sharing of WiFi and LTE network as explained above, fairness is only achievable amongst users under the same AP or BS. Fair capacity sharing across the entire HetNet is not guaranteed and is totally up to the NSSs implemented. The characteristics of each NSSs are detailed as follows:

1) WiFi First (WF)

(3)

WiFi First (WF) connects users to WiFi APs whenever WiFi coverage is available. When WF is implemented, a new user will always search for a WiFi network to join and only joins a LTE network when no WiFi coverage. This approach is generally adopted by current mobile operators [11][3]. 2) Physical Data Rate (PDR) based The selection criterion of the PDR algorithm is purely based on the physical data rate of the RAT(s) available to the user. It compares the PDR of LTE and WiFi in multi-rate operation and chooses the RAT with higher PDR. 3) Dynamic Backhaul Capacity Sensitive (DyBaCS) scheme We assume there is only one LTE network and 1) is total number of WiFi APs in the HetNet. Where denotes the index of the network, i.e. network 0 is LTE and network 1 is the i-th WiFi network. While, 1 represents the -th user in the entire HetNet. Before explaining DyBaCS in detail, we first discuss User Throughput Estimation Flow (UTEF) which will be used in DyBaCS algorithm later. We focus on how WiFi user throughput is affected by backhaul capacity. Users Throughput (

,

) Estimation

Flow (UTEF);

WiFi UTEF: 1) Let x = {1,2,…, } is number of WiFi users in Network i ; Eqn 1 2) Average user capacity is : ∑

3) System capacity for network i is: 4) For 1 A) if ,

i) B)

,

else if i) ii) if

,

/

, ,

,

a) iii) if

,

,

,

a)

,

,

,

LTE UTEF: 1) 2)

Let x = {1,2,…, } is number of users in LTE Network ; Eqn 2 Average user capacity is : ∑

3)

Average user capacity consider OF is: , ,

,

In short, Step 4 consists of logical considerations that determine the effective average throughput , of users taking into consideration of backhaul capacity, user link speed and OF. Although WiFi user throughput estimation flow is only used to estimate the user throughput in the study, a similar backhaul capacity aware concept can also be adopted in practical system during capacity assignment, but this falls beyond the scope of this study. Similarly, LTE user throughput is also estimated as shown in LTE UTEF. LTE backhaul capacity is assumed to be always sufficient. Therefore in Step 3, the only constraint for user throughput on LTE network is access link speed , .

, ,

is also the required backhaul throughput for AP . As in Eqn 1, is affected by link speed of all users in network , therefore it is a dynamic value depending on the , all distribution of users. If the backhaul throughput users can enjoy a throughput of . However, if the backhaul is less than , users will have to be throughput contented with an average throughput less than as: , / (4) , It is important to note that users will not receive a even with higher backhaul throughput higher than due to the limitation of the WiFi physical throughput than and MAC layer capability. Under simultaneous channel access, or is the average user throughput with sufficient , and limited backhaul capacity respectively. In practice, OF is normally considered as not all the users access the channel at the same time [12]. Taking the OF into consideration, although the natural conclusion of effective or with average throughput , , is sufficient and limited backhaul capacity scenario respectively. The maximum throughput of a user is however limited by its link speed , . Hence Step 4A(i) places a constraint to will not exceed ensure that the value of , . Similarly, with limited backhaul capacity, Step 4B(ii) and Step is bound by both 4B(iii) also ensure that , , and , whichever value is smaller.

,

In the WiFi UTEF above, calculation of effective user throughput , is based on user link speed , , AP and Overbooking Factor (OF). Step 1 backhaul capacity and Step 2 in the flow are used to estimate the average WiFi user throughput considering only access link speed without taking backhaul capacity into account. As per Eqn 1, when all users in AP access the channel simultaneously, average user . Multiplying the average user throughput is given as throughput by the total number of users under AP , as in Step 3, provides the total system throughput of AP :

The DyBaCS NSS as shown below is initiated with no user connected to the HetNet. Users are admitted one by one to their servicing network determined by DyBaCS. Step1 initializes all variables. In Step 2, the link with higher throughput between user to LTE ( , ) or to WiFi ( , ) is chosen and place them into set Ω . Step 3 assigns all users with no WiFi access to the LTE network and excludes those 0,1 denotes the connectivity of user users from set . ‘1’ means connection is possible, ‘0’ to network and means the opposite. All users are able to connect to at least LTE, whereas the possibility of WiFi connectivity depends on WiFi coverage. Since multi-homing is not considered, every user is allowed to connect to one network at a time. Network 0,1 is used to indicate the choice of selection parameter ‘1’ indicates that user is connected to network user ; and zero means the opposite. The remaining users within set

without a network assignment are addressed in Step 4. Step 4a ensures the user with highest link throughput is considered first. In Step 4b and Step 4c, the achievable throughput of user and on WiFi and LTE is estimated and represented by respectively. Step 4d and Step 4e assign the user to the network that offers higher throughput. Finally set is updated in Step 4f and the process is repeated until all users are serviced. DyBaCS Algorithm: 1)

2) 3)

4)

Initialization , and 1,2, … , a) Ω ; b) For 1 a) Ω 0, 1 … , , , For 1 a) if user satisfying 0, 1, 2, … 1 i. ii. while Ω for all and a) find where |Ω | b) Estimate user throughput in serving WiFi Network with total users; Using WiFi UTEF i. , c) Estimate user throughput in serving LTE Network with users; Using LTE UTEF i. , d) If ≥ i. 1 ii. < e) If 1 i. ii. f)

III.

SIMULATION PARAMETERS AND ASSUMPTIONS

A. WiFi Model TABLE I. WIFI PARAMETERS Index

MCS

1 2 3 4 5 6 7 8

BPSK1/2 BPSK3/4 QPSK1/2 QPSK3/4 16-QAM1/2 16-QAM3/4 64-QAM2/3 64-QAM3/4

Receiver Sensitivity (dBm) -94.0 -93.0 -91.0 -90.0 -86.0 -83.0 -77.0 -74.0

WiFi IEEE802.11g Throughput Data Efficiency Rate (Mbps) ( ) 6 0.70 9 0.64 12 0.61 18 0.54 24 0.49 36 0.41 48 0.35 54 0.32

10

)

22.234

C. General Parameters and Assumptions TABLE II.

GENERAL WIFI AND LTE SIMULATION PARAMETERS Units WiFi 11g LTE Frequency band GHz 2.4 2.6 Channel bandwidth MHz 20 20 Max EIRP dBm 27 36 Rx antenna gain dBi 3 0 Antenna Diversity Gain dB 3 3 Noise Figure dB Not required 10 Packet Size Bytes 1000 WiFi Backhaul Capacity Mbps 1 – 20 LTE Backhaul Capacity Mbps Sufficient Capacity Assumed User Information Overbooking Factor, OF 10:1 User Density Users/sqkm 100

Table II summarizes the simulation parameters used. Only the downlink performance is investigated in this paper. At the LTE User Equipment (UE), a fixed interference margin of 3dB is assumed. Receiver antenna gain for a WiFi and LTE user is assumed to be 3 dB and 0 dB respectively. Uniform packet size of 1000 bytes is chosen to match the WiFi throughput efficiency metric based on 1000 bytes packet, as in Table I. The study of any other packet size or non-uniform packet size is possible, but to simplify the simulation fixed packet size is adopted. From [12], the OF factor is a design choice driven by actual user behavior in the deployed area. An OF factor varying from 4:1 to 100:1 has been reported by various ISPs; the lower the value the better the service guarantee. We choose a factor of 10:1 in our study to represent a relatively high usage scenario. User density is arbitrarily chosen as 100 users per square km. IV.

Throughput (Mbps) 4.50 6.21 7.68 10.08 12.00 14.76 16.32 17.28

Table I presents the parameters used in the WiFi IEEE802.11g model. The receiver sensitivity values per modulation and coding scheme (MCS) for WiFi receivers are taken from [13]. The throughput efficiency, which is the ratio of actual IP layer throughput against physical data rate and the actual IP layer throughput of each MCS scheme are obtained from the QualNet simulator. The path loss model in Eqn 5 is derived from field a measurement detailed in [14] : 30.2

receiver is used to calculate the achievable throughput. In order to calculate the received power, the ITU recommended LTE Urban Macro (UMa) non line of sight (NLOS) path loss model is used [16].

(5)

B. LTE Model LTE throughput is calculated based on the spectral efficiency plot in [15]. In the simulation, SINR ratio at a

RESULTS AND ANALYSIS

The performance of all NSSs as a function of WiFi backhaul throughputs varying uniformly from 1Mbps to 20Mbps is evaluated (Figure 3). The average user throughput of PDR and DyBaCS are close to each other over the entire range of backhaul capacity. WF has the lowest average user throughput. The plots plateau at 20Mbps for all NSSs mainly bounded by the WiFi (IEEE 802.11g) maximum access throughput. The total WiFi access throughput also depends on user distribution as slower users can slow down the network. Figure 4 evaluates the fairness in terms of achievable throughput per user. The Jain fairness index is adopted in the / ∑ [17]. Result shows that form of ∑ , , DyBaCS outperforms the rest in term of fairness; WF is the worst in term of fairness. In another scenario, the backhaul throughput of APs is arbitrary non-uniformly assigned with AP1, AP2, AP3 and AP2 equal to 10Mbps, 0.5Mbps, 20Mbps and 3Mbps respectively. The plot of average user throughput for this scenario is presented in Figure 5, where PDR has the highest value (4.75Mbps) followed by DyBaCS (4.22Mbps) and WF (2.62Mbps). PDR is a greedy algorithm that chooses the network based on highest physical data rate with the intention

Figure 3: Average user throughput as a function of WiFi backhaul throughput. (Uniform WiFi backhaul capacity)

Figure 4: Fairness on bandwidth sharing as a function of WiFi backhaul throughput. (Uniform WiFi backhaul capacity)

Figure 5: Average user throughput with non-uniform WiFi backhaul capacity.

Figure 6: Fairness on bandwidth sharing with non-uniform WiFi backhaul capacity. [3] A. Handa, Mobile Data Offload for 3G Networks: A White Paper, http://www.intellinet-tech.com/Media/PagePDF/Data%20Offload.pdf, Oct. 2009. [4] P. Li , J. Kong, S. H. Kim, H. B. Jung, X. L. Niu, D. K. Kim, “ Power allocation with max-min fairness for multicast in heterogeneous network” 14th International Conference on Advanced Communication Technology (ICACT), 19-22 Feb. 2012 [5] P. Xue, P. Gong, J. H. Park, D. Y. Park, and D. K. Kim, "Radio resource management with proportional rate constraint in the heterogeneous networks," accepted and to be published in IEEE Transactions on Wireless Communications, 2011. [6] J. Nie, J. Wen, Q. Dong, Z. Zhou, “A seamless handoff in IEEE802.16a and IEEE802.11n hybrid networks”, in proc. Int. Conf. Communi. Circuits Syst., Vol. 1, pp.383-387, May 2005. [7] J. Roy, V. Vaidehi, and S. Srikanth, “Aways best-connected QoS integration model for the WLAN, WiMAX heterogeneous network”, in proc, Int. Conf, Ind. Info, Syst, pp. 361-366, Aug 2006. [8] H. Hu, W.Zhou ; S. Zhang ; J. Song, “A Novel network selection algorithm in next generation heterogeneous network for modern service industry”, IEEE Asia-Pacific Services Computing Conference, 2008. APSCC '08, 9-12 Dec. 2008 [9] M. Petrova, P. Ma ho nen, and J. Riihija rvi, "Connectivity analysis of clustered ad hoc and mesh networks," in Proceedings of IEEE GLOBECOM 2007, Martinique, April 2007. [10] P. Bemder et al, “CDMA/HDR: A Bandwidth Efficient High-speed Wireless Data Service for Nomadic User”, IEEE Communication Magazine, 38(7), 70 – 77 (2000). [11] Yongmin Choi; Hyun Wook Ji; Jae-yoon Park; Hyun-chul Kim; Silvester, J.A. , "A 3W network strategy for mobile data traffic offloading," Communications Magazine, IEEE , vol.49, no.10, pp.118123, Oct. 2011. [12] Tranzeo Wireless Technologies Inc, “Example Community Broadband Wireless Mesh Network Design”, Version 1.1, 20thJune 2007. [13] NanoStation2, Ubiquiti Networks, www.ubnt.com/products/nano.php [14] A. Ting, D. Chieng, K. H. Kwong, I. Andonovic, “Optimization of Heterogeneous Multi-radio Multi-hop Rural Wireless Network” ICCT 2012, ChengDu China, 9th-11th Nov 2012. [15] 3GPP, "Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Frequency (RF) system scenarios (Release 10)," 3GPP TR 36.942 V10.2.0 2010 [16] ITU-R Report M.2135-1: "Guidelines for evaluation of radio interface technologies for IMT-Advanced," December 2009. [17] R.Jain, A.Durresi, and G.Babic, “Throughput fairness index: an explanation”, ATM Forum/99-0045, Feb. 1999.

of maximizing system throughput. However, the fairness in terms of capacity distribution amongst users is degraded as depicted clearly in Figure 6 where the fairness index of PDR is only 0.59 as compared to 0.86 by using DyBaCS. Although the average throughput of DyBaCS is 11% lower than PDR, the tradeoff seems to be worthwhile as fairness is greatly improved by 45%. WF is the worst providing a 0.43 fairness index. V.

CONCLUSIONS

The performance of the proposed DyBaCS and two other NSSs is evaluated as a function of WiFi backhaul capacity in a LTE-WiFi heterogeneous network. NSSs performance is compared under the scenarios of uniform and non-uniform WiFi backhaul capacity distribution. Results show that the proposed DyBaCS scheme provides the best fairness while preserving the average user throughput over the range of WiFi backhaul capacity considered. The PDR scheme which is based on physical data rate achieves high system throughput but provides poor fairness in terms of user capacity distribution. WF is worst in terms of average user throughput and fairness. Due to page limitations, only 4AP-1BS scenario is investigated in this paper. Additional results, including different AP:BS ratios, will be reported in future publications. Consideration of more realistic proportional fairness capacity sharing schemes, the use higher capacity LTE base stations and IEEE802.11n AP and evaluation under different traffic types will also be addressed. ACKNOWLEDGMENT This work is supported by Ministry of Science, Technology and Innovation of Malaysia under the Science Fund project code 01-03-04-SF0011. REFERENCES [1] [2]

Firetide Wireless Mesh Solutions for TELCOS: Access & Small Cell Backhaul, https://www.winncom.com/manufacturer/firetide Karam F.W, Jensen T, “Performance analysis of ranking for QoS handover algorithm for selection of access network in heterogeneous wireless networks”, 21st International Conference onComputer Communications and Networks (ICCCN), 2012.