Fine-Tuning the Femtocell Performance in Unlicensed Bands: Case of WiFi Co-existence

Transcription

1 Fine-Tuning the Femtocell Performance in Unlicensed Bands: Case of WiFi Co-existence Sima Hajmohammad Université du Québec à Montréal Montreal, Canada haj Halima Elbiaze Université du Québec à Montréal Montreal, Canada Wessam Ajib Université du Québec à Montréal Montreal, Canada Abstract Femtocell and WiFi play crucial roles in sustaining the continued growth in mobile traffic. Deploying Femtocells in WiFi hotspots would allow the access providers to provide more capacity for users and improve their quality of experience during mobility. Hence, the co-existence of Femtocell and WiFi carries critical importance for improving the total performance of the users and meeting the promised quality of service (QoS) satisfaction of Femtocell end users. In this paper, we propose and develop a framework allowing to make use of unlicensed band and to increase the total throughput of Femtocells while offloading the traffic of Femtocell users to unlicensed bands in case of severe interference with Macrocell. The channel access of both Femtocell and WiFi networks are analytically modeled and numerically verified. Moreover, the effects of WiFi channel access parameters on the performance of WiFi and Femtocell networks are investigated. Numerical evaluation of our proposed scheme show that by adequately tuning and giving priority, the throughput of small cells and utilization of unlicensed spectrum have been improved. I.INTRODUCTION Nowadays, it is largely acepted that most of the voice calls and data traffic are generated indoors. Hence, the users of cellular systems may experience difficulties in receiving quality service due to low quality signals from the Macrocell base stations. T o solve the dead zone problem in the indoor environments, Femtocell technology has been taken into consideration. Femtocell is a small cellular base station, typically designed to serve a little number of users in indoor environments such as small offices and homes. It supports 2 to 4 mobile phones in a residential setting, or 8 to 16 mobile phones in an enterprise setting [1]. Femtocell allows service providers to extend service coverage indoors, especially where access would otherwise be limited or unavailable. From a mobile operator perspective, the attractions of Femtocell include improvements to both coverage and capacity. End users, on the other hand, benefit from improved coverage and potentially better voice quality and battery life. Due to the increasing demands on coverage and capacity in cellular networks, the wireless industry has begun to embrace Femtocells [2], which are low-power, low-cost, user-deployed base stations (BSs). Typically, the Femtocell BS (FBS) connects to the network through a high-speed connection such as ADSL (Asymmetric Digital Subscriber Line) home backhaul connection. Because of their short range transmission-reception, Femtocells can easily reduce the transmission power, extend the phone battery life, and also achieve an increased signal-tointerference-plus-noise ratio (SINR), all of which consequently boost reception to higher capacity [3]. Beside Femtocell advantages, severe mutual interference amongst Femtocell and Macrocell users is the main challenge [4]. In the last decade, many cellular service providers have been offloading data traffic from their licensed bands to unlicensed ones over prevalent WiFi access points. Hence, given the Femtocells short-range coverage, they can be an ideal alternative platform for making the most of both licensed and unlicensed spectrum benefits. Specifically, licensed frequencies ought to be allocated to the tiers that can use them efficiently, and unlicensed bands ought to be used for higher levels of interoperability and capacity. As unlicensed bands provide excellent capacity and coverage [5], deploying Femtocells in WiFi hotspots would let access providers to provide more capacity to users who do not have WiFi on their device. On the other hand, offloading traffic to unlicensed band would be a solution to mitigate the interference with Macrocell. Femtocells resemble WiFi access points in many aspects, namely the small size, the cheap price, and the low-power base stations. Femtocells, along with WiFi offloading, are expected to carry over 60% of all global data traffic by 2015 [6]. Although, Femtocells can implement some existing mobility procedures, the mobility of Femtocell users present a number of unique challenges that require special consideration [4]. Standard bodies such as 3rd Generation Partnership Project (3GPP) have devoted considerable attention to these mobility issues. For example, in the specifications [7] and [8], EPS (Evolved Packet Core) has defined the support of connectivity over WiFi as part of the Non-3GPP access support. In EPS, ANDSF (Access Network Discovery and Selection Function) has defined mechanisms that enable devices to determine which access technology is preferable for certain IP traffic under specific conditions. In certain scenarios, such as severe interference situations, WiFi access will be preferred to certain 3GPP access technologies [9]. With the spreading use and the increasing playing role of WiFi in 3GPP operator network deployments, improving the use of WiFi in EPS would be beneficial to operators and to end users. In addition, different procedures like the Generic Access Network (GAN) framework are being developed for vertical handovers /14/$ IEEE 250

2 between Femtocells and non-cellular access technologies such as WiFi [10]. Unlicensed bands provide excellent capacity and coverage but if each of WiFi hotspots and Femtocells use the unlicensed bands alone, we will face inefficient use of overall spectrum. In [11], authors proposed a framework for a Femtocell to access both licensed and unlicensed bands. Partitioning spectrum resources carries critical importance for maximizing the total capacity and QoS satisfaction of end users. In [12], we investigated a fair and QoS-based unlicensed spectrum splitting strategy between WiFi and Femtocell networks. In the split spectrum approach, the allocated spectrum is partitioned between networks and each of them can use its own segment of resource. In this paper, we develop and propose a framework to fine-tune the Femtocell performance when it co-exists with WiFi network. The rest of this paper is organized as follows. Section II describes the analytical models for both Femtocell and WiFi to access the unlicensed band. Section III presents the methods of deriving the performance of Femtocell and WiFi. In Section IV, we present the performance evaluation of our model in improving the spectrum utilization. Section V concludes this paper. II. THE CO-EXISTENCE OF FEMTOCELL WITH WIFI In this section, we analytically model the co-existence of Femtocells and incumbent devices such as WiFi to access the unlicensed spectrum over a fully-utilized unlicensed band. Due to its widespread utilization, WiFi network is selected as the incumbent system for investigating the co-existence problem. Femtocell s self-organization function allows it to scan the air The first step in this study is to model a back-off mechanism for WiFi network to obtain the probability of channel being in idle state (not being occupied by WiFi). It is the probability that the time of an FBS channel access attempt is within a WiFi idle period. As Femtocell and WiFi devices have the same priority to access the unlicensed bands, it can adjust its channel usage and the impact on WiFi users by tuning its channel access parameters and therefore achieving a friendly coexistence with WiFi users. Therefore, in section II-A, an FBS channel access model has been proposed and developed. The following assumptions are considered in our analytical model: The number of WiFi nodes is greater than one. All nodes (WiFi and Femto) have infinitely backlogged data to transmit. WiFi transmissions have no channel errors. A WiFi transmission fails when it collides with either WiFi or Femto transmissions. All WiFi stations transmit at the same data rate defined by the standard. A. WiFi Backoff Model Our proposed WiFi backoff mechanism is associated with the exponential backoff mechanism of the MAC protocol. The contention window (CW) takes an initial value of CW min, and every unsuccessful attempt to transmit a frame increments CW (as CW i = 2CW i 1 ) until it reaches the value of CW max. This paper considers no retransmission limit and hence, CW can be reset to CW min just after every successful transmission. The value of the backoff counter is uniformly chosen in the range [0, W i 1 ], where W i = 2 i W with W = CW min. With reference to details of backoff mechanism of IEEE [13]-[14], the average backoff window would be: W = W [1 P P (2P )n ] 2 4P where n = log 2 (CW max /CW min ) and P is the probability that a transmitted packet collides at steady state. To find the value of P, we notice that the probability that a transmitted packet encounters a collision is equal to the probability that, in a time slot, at least one of the N 1 remaining stations transmit. P can then be calculated by (2), where N indicates the number of WiFi base access points and τ denotes the probability of each remaining station to transmit a packet at a steady state. (1) P = 1 (1 τ) N 1 (2) Fig. 1. Macrocell-Femtocell-WiFi Network interface and tune its parameters according to the dynamic behaviour of the network, traffic and channel. The FBS tries to access the channel only at preassigned periodic times. Upon the arrival of an access opportunity, the FBS starts sensing the unlicensed band. If the spectrum is idle for a predefined time, the FBS would access the channel and may use it for a fixed period. The fraction of idle channel time in a WiFi network can be obtained by analyzing the WiFi exponential backoff process, as done in [15][16]. By modeling WiFi exponential backoff with Markov Chain, we provide accurate probabilities for a WiFi network being in five transition states: (i) Defer (Successful) when it senses a successful transmission of other stations, (ii) Backoff when it stays in a back-off stage owing to the channel being idle, (iii) Defer (Collided) when it senses a collision occurred due to the simultaneous transmission of more than 251

3 Fig. 3. T trans and T collision for RTS/CTS mechanisms Let us now consider a system in which each packet is transmitted by means of the RTS/CTS (Ready To Send/Clear To Send) access mechanism. In such a case, collision can occur only at RTS frames (see Fig. 3). Therefore, T trans =RT S + SIF S + δ + CT S + SIF S + δ + H + E[P ] + SIF S + δ + ACK + DIF S + δ T collision =RT S + DIF S (8) Fig. 2. WiFi Transition states two stations, (iv) Transmission (Successful) when it is transmitting successfully, and (v) Transmission (Collided) when it transmits but collides due to the characteristics of Markov process (as shown in Fig.2). According to the characteristics of Markov process, the spent time in each state is exponentially distributed. Therefore, we assume that both successful and collided transmissions have exponential distributions while WiFi occupies the channel. Moreover, as time slot for backoff is considered constant (20 µsec), the possible error because of exponential distribution is negligible. If we consider τ 0 = 1 W as the probability that a station transmits in a time slot (successful transmission), then: P ds is the probability that the station senses a successful transmission which occurred in a time slot. P ds = (1 τ 0 ).(N 1)τ(1 τ) N 2 (3) P dc is the probability that the station senses a collided transmission which occurred in a time slot (means at least two stations transmit at the same time slot). P dc = (1 τ 0 ).(1 (1 τ) N 1 ) (N 1)(1 τ) N 2 (4) P idle is the probability that station is in back-off state for a time slot. P idle = (1 τ 0 ).(1 τ) N 1 (5) P tc is the probability that the station transmits but collides. P tc = τ 0.(1 (1 τ) N 1 ) (6) P ts is the probability that the station transmits successfully in a time slot P ts = τ 0.(1 τ) N 1 (7) where δ is the propagation delay, H = P HY hdr + MAC hdr is the packet header, and E[P ] is the average length of packet. The analytical model presented above is convenient to determine the maximum achievable saturation throughput, S as: E[p] S = T trans T collision + B. Femtocell Access Model T idle (1 P ts (9) ) P ts +T collision P idle In this co-existing model, FBS finds the channel in two states of transition: idle and non-idle. FBS senses the channel for a duration T s to access the channel. If FBS finds the channel idle, it will attempt to occupy the channel for the fixed duration of T Acc (Fig. 5). In addition, after FBS uses the channel for T Acc, it is not allowed to access it consecutively. It should wait at least T attempt in between other unlicensed band users attempts before it tries for a next channel access opportunity. Three key parameters governing the proposed channel access mechanism are ordered as follows: T s : Channel Sensing Duration T attempt : Channel Access Opportunity T Acc : Transmission Duration The sensing and transmission duration have been considered constant for FBS in our framework. The idle state is divided to Q idle time slots (Q = 1, 2,...) with the probability of P Q idle (1-P idle) and duration of QT idle. In order to satisfy the condition of successful FBS channel access, the attempt must occur within a period where all WiFi nodes are idle. Therefore, to guarantee this condition, we define G as the fraction of time that channel is idle out of all WiFi channel times. G = P idle T idle (P ds + P ts )T trans + (P dc + P tc )T collision + P idle T idle (10) 252

4 Fig. 5. Femtocell Channel Access Model probability that it successfully obtains the channel is: Fig. 4. Femtocell channel access Therefore, G is just the probability that an FBS attempt happens within a WiFi idle channel period. The length of the period and the relative location of the attempt time in the WiFi idle channel period are random and these factors cannot be disregarded. For example, if QT idle T s and the attempt time happens in the first QT idle T s QT idle portion of the WiFi idle channel period, the FBS will find that the channel is idle during the following T s channel sensing time and the attempt will be successful. The conditional probability of successful channel occupancy by the FBS is QT idle T s QT idle, if we consider the start time of the FBS access attempt to be within T idle period with Q(Q Ts T idle ) time slots (see Fig. 5). Consequently, we can say that if an FBS attempts to access the channel within a WiFi idle channel period, the conditional P SuccAcc = G.P SuccAcc P SuccAcc = P Q idle (1 P idle) QT idle T s (11) QT idle L=L 0 where L 0 = Ts T idle. III. NETWORK PERFORMANCE The states of WiFi and Femtocell networks are coupled with each other through the P SuccAcc, the probability that an FBS successfully gets the channel in a channel access attempt. The whole network analysis is comprised of the following parts: Deriving P SuccAcc by modeling the exponential backoff mechanism of WiFi nodes with a Markov chain. Deriving a Femtocell performance by measuring S F emto which is the fraction of channel time occupied by the Femtocell. Deriving WiFi network throughput in terms of S F emto and WiFi parameters. A. Femtocell Performance The states of WiFi and Femtocell networks are tightly linked together with a key parameter P SuccAcc, the probability that the FBS successfully obtains the channel in a channel access attempt. The FBS attempts to access the channel by sensing the carrier. If the channel successfully gets occupied considering the above conditions, the FBS transmits for a fixed duration T Acc. If the attempt is not successful, it will attempt again after a random time of T Attempt. The T Attempt will be considered randomly to give the FBS more access opportunity while the channel is in idle state. The success probability for each attempt is P SuccAcc and 1/P SuccAcc attempts are required in average for the FBS to successfully senses it for the fixed time of T s to get the channel and obtain it for a fixed duration of T Acc. Therefore, the fraction of channel time occupied by the Femtocell showing the performance of Femtocell is: 253

5 TABLE I VALUES OF ANALYTICAL MODEL PARAMETERS S F emto = Parameters values RTS 160 bits CTS 112 bits δ 1 SIFS 10 µsec DIFS 30 µsec Mean packet length 512,1024 bytes time slot 20 µsec T Acc ( 1 P SuccAcc )T Attempt + T Acc + T s (12) B. WiFi/Femtocell Coexistence Performance Our earlier given assumptions by the equations (9) and (10) show that WiFi network has the same fractions of idle, collision, and successful transmission time, respectively, in the WiFi/Femtocell coexistence scenario as in the WiFi-only scenario. Also, WiFi throughput is attained by the fraction of time in successful transmission instead of collision or idle. In this case, WiFi would have access to the channel for (1 S F emto ) period. The S W if i as the WiFi (WiFi/Femtocell coexistence) throughput in saturation condition is: S W if i = (1 S F emto ) P idle T idle E[P ] (P ds + P ts )T trans + (P dc + P tc )T collision + P idle T idle(13) IV. ANALYTICAL MODEL EVALUATION AND RESULTS In this section, we verify our analysis for the co-existence of WiFi/Femtocell in unlicensed bands. In our analytical model, we have just considered the performance of Femtocell in unlicensed bands and do not investigate its cellular performance in licensed bands. The system parameters for the analytical model are summarized in Table I. We study the impact of λ and τ on WiFi and Femtocell performances where τ designates the probability of our network being busy by WiFi stations and λ is a key factor of FBS channel access opportunity (T Attempt = 1/ λ). As predicted by equation (12), Femtocell performance S F emto is an increasing function of T Attempt and consequently a decreasing function of λ (as T Acc and T s have constant value). Equation (13) suggests that WiFi performance degrades as Femtocell performance improves. It is an obvious consequence of the channel contention between WiFi and Femtocell. Following results in Figures (6) and (7) indicate that different λ values lead to the same WiFi throughput and Femtocell channel usages. This demonstrates that λ is the main Femtocell parameter that impacts WiFi and Femtocell performance. In our model, we assume that WiFi network consists of 9 WiFi stations coexisting with one Femtocell. The WiFi RTS/CTS is enabled and all transmitters can sense each other. Fig. 6 shows the performance of WiFi and Femtocell for different values of τ and λ. Fig.6 (b) shows the importance of factor λ when both WiFi and Femtocell have equal chances of using the channel. Results in Fig. 6 indicate that our model gives priority to Femtocell while keeping the effect of this co-existence at a low level. Figure 7 illustrates that even by increasing the number of WiFi stations, the performance of Femtocell is kept at an acceptable rate. V. CONCLUSION AND FUTURE WORK This paper proposes a framework for the co-existence of Femtocell and WiFi to access the unlicensed band under a single radio access technology. In this method, we try to improve Femtocell performance while provisioning its interference with WiFi users. Analytical results of our scheme show that by tuning and giving priority, the throughput of small cells and utilization of unlicensed spectrum have been improved. As future work, we plan to develop our framework for the proposed Femtocells to assign traffic into both licensed and unlicensed bands to improve and assure Femtocell QoS for end users. REFERENCES [1] V. Chandrasekhar, J. Andrews, and A. Gatherer, Femtocell networks: a survey, IEEE Communication Magazine, vol. 46, pp , September [2] S. Ortiz, The wireless industry begins to embrace femtocells, Computer, vol. 41, pp , July [3] R. Kim, J. S. Kwak, and K. Etemad, WiMAX femtocell: requirements, challenges, and solutions, IEEE Communication Magazine, vol. 47, pp , September [4] Y. J. Sang, H. Gwang, and K. S. Kim, A self-organized femtocell for IEEE e system, in IEEE conference on Global telecommunications (GLOBECOM 09), pp. 1 5, December [5] P. Fuxjger, H. Fischer, I. Gojmeraca, and P. Reichl, Radio resource allocation in urban femto-wifi convergence scenarios, in Proceeding Next Generation Internet Conference (NGI), pp. 1 8, June [6] J. Publisher, Wifi and femtocell integration strategies , March, 2011@ March [7] 3GPP, Mobility procedures for home nodeb; overall description stage 2, ts (release 11), [8] 3GPP, UTRAN architecture for 3G Home NodeB Stage 2, ts (release 11), [9] 3GPP, Generic access network (GAN), ts (release 11), [10] S. Ghosh, K. Basu, and S. Das, An architecture for next-generation radio access networks, IEEE Network Magazine, vol. 19, pp , September [11] F. Liu, B. Erkip, and R. Yang, A framework for femtocells to access both licensed and unlicensed bands, International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), pp , May [12] S. Hajmohammad and H. Elbiaze, Unlicensed spectrum splitting between femtocell and WiFi, in IEEE International Communications Conference (ICC), Budapest, Hungary, pp , June [13] O. Tickoo and B. Sikdar, On the impact of IEEE MAC on traffic characteristics, IEEE Journal on Selected Areas in Communications, vol. 21, no. 2, pp , [14] O. Tickoo and B. Sikdar, A queueing model for finite load IEEE random access MAC, in IEEE International Communications Conference (ICC), vol. 1, pp , June [15] Y. Zhu and Z. Niu, A novel queueing model for finite load IEEE WLANs, in Proceeding Vehicular Technology Conference (VTC), vol. 2, pp , September [16] G. Bianchi, Performance analysis of the IEEE distributed coordination function, IEEE Journal on Selected Areas in Communications, vol. 18, pp , March

6 (a) τ = 0.3 (a) τ = 0.3 (b) τ = 0.5 (b) τ = 0.5 (c) τ = 0.7 Fig. 6. WiFi & Femtocell performance for various τ and λ while number of WiFi and Femtocell stations are fixed (N = 9). (c) τ = 0.7 Fig. 7. Femtocell performance for various τ, λ and number of WiFi stations. 255