A Framework for Femtocells to Access Both Licensed and Unlicensed Bands

Transcription

1 Indoor and Outdoor Femto Cells A Framework for Femtocells to Access Both Licensed and Unlicensed Bands Feilu Liu, Erdem Bala, Elza Erkip and Rui Yang ECE Department, Polytechnic Institute of NYU, Brooklyn, NY 1121 InterDigital Communications, 2 Huntington Quadrangle 4th Floor, South Wing, Melville, NY Abstract Cellular operators have been offloading data traffic from their licensed bands to unlicensed bands through a large number of WiFi hotspots over the past years. Although this approach improves the cellular network capacity to some extent, it falls short of getting significant throughput gains. In this paper, it is argued that femtocells, covering a short range, can be a perfect platform to jointly exploit the merits of both licensed and unlicensed frequency bands. In particular, a framework is proposed for a femtocell to simultaneously access both licensed and unlicensed bands. The performance of coexisting femtocell and WiFi networks operating over a fully-utilized unlicensed band are analytically modeled and are verified via simulations. Impact of femtocell channel access parameters on the performance of WiFi and cellular networks is also investigated, shedding light on how a femtocell can best adjust its channel access parameters to coexist with incumbent unlicensed spectrum users like WiFi networks. Index Terms throughput analysis, femtocell, cognitive radio. I. INTRODUCTION Cellular operators have been offloading more and more data traffic from their overloaded networks to a large number of WiFi hotspots over the past years [1], [2]. AT&T, for instance, currently owns more than 23, WiFi hotspots [2]. The approach of utilizing both licensed bands (cellular networks) and unlicensed bands (WiFi hotspots) has helped cellular operators to narrow the gap between the limited capacity of cellular networks and the fast growing demand of mobile broadband [2]. Femtocells provide another way to deal with the challenge of quickly increasing user requirements on mobile broadband [3], [4]. Femtocells are formed by small-size, low-power base stations that are typically installed by cellular users at their homes. Femtocell base stations (fbs) use the existing broadband internet connections (e.g., cable, DSL) as backhaul. The air interfaces of femtocells and macrocells are exactly the same, which results in a seamless network coverage. However, this in return creates severe interference among femtocell and macrocell users [4]. In this paper, we show that each of the above two approaches (WiFi hotspots and femtocells), if used alone, suffers from inefficient use of overall spectrum. To improve the overall throughput, we propose a framework for a femtocell to access unlicensed bands (e.g., ISM bands) in addition to the licensed Part of this work was done during Feilu Liu s internship in InterDigital Communications, LLC. This work is supported by the New York State Center for Advanced Technology in Telecommunications (CATT) and the Wireless Internet Center for Advanced Technology (WICAT), an NSF Industry/University Cooperative Research Center at Polytechnic Institute of NYU. cellular band, using a single cellular PHY layer protocol. In contrast, the current approaches described earlier use only one band, either licensed or unlicensed. As a result, our approach not only improves the throughput in cellular small cells (i.e., femtocells or cellular WiFi hotspots) but also increases the sum throughput of cellular (femto and macro) and existing unlicensed band users. In fact, under a practical deployment scenario, our simulations show that the throughput of cellular small cells (i.e., femtocells or WiFi hotspots) in our scheme is 4 and 1.6 times of those in current WiFi hotspot and femtocell approaches, respectively. In addition, the total throughput of all cellular and non-cellular users under the proposed scheme is 2.5 and 1.16 times of those in current WiFi hotspot and femtocell approaches, respectively. Femtocells accessing unlicensed bands have to coexist with a large number of incumbent devices like WiFi and Bluetooth in the unlicensed spectrum. To study the coexistence problem, WiFi network is chosen in this paper as the incumbent system due to its abundance in number. We analytically model the performance of coexisting femtocells and WiFi networks operating over a fully-utilized unlicensed band, respectively. The analytical results are validated through simulations. Our results reveal how a femtocell can adjust its impacts to the coexisting WiFi nodes by tuning its channel access parameters. Our work is related to a few recent papers [5] [7], which apply cognitive technology to femtocells. In these papers, macrocells are the primary users in the licensed cellular band while femtocells are the secondary users. Femto BSs use cognitive technology to access the radio resources that are not utilized by the macrocell. However, these femtocells only operate in cellular licensed spectrum; while in our work, femtocells access unlicensed bands as well. In addition, in our framework, femtocells are not secondary users in the licensed or unlicensed bands; they are equal players compared with other users. II. THE PROPOSED FRAMEWORK Two key assumptions in our proposed framework and the unique femtocell features that they are based on are as follows: Femtocell control messages, during both channel access and data transmission phases, are exchanged only in the licensed cellular bands. Unlicensed bands only convey data traffic. This assumption is based on the fact that the licensed band is more reliable than the unlicensed band /11/$ IEEE 47

2 Fig. 1. Femtocell channel access mechanism for unlicensed band. III. MANAGING THE COEXISTENCE OF FEMTOCELL WITH WIFI In this section, we mathematically model the coexistence of a WiFi WLAN and a femtocell operating over a fully-utilized unlicensed band to derive the performance of each network in the unlicensed band. A femtocell can adjust its channel usage and the impact to WiFi users by tuning its channel access parameters using the analytical tools to be developed in this section, therefore achieving a friendly coexistence with WiFi users. In our analysis, WiFi performance is measured by the total throughput of all WiFi nodes. Femtocell performance is measured by the fraction of time that a femtocell occupies the unlicensed band. Femtocell performance in licensed spectrum is not considered in this analysis, but will be presented in the simulation results in the next section. Only the femto base station (fbs) accesses the channel; upon obtaining the channel, the fbs, through the control channel in licensed bands, informs user equipments (UEs) to turn on their unlicensed band radios and operate on the assigned channels. The proposed channel access mechanism is illustrated in Fig. 1. The fbs attempts to access the channel only at preassigned periodic time instants denoted as access opportunities. Upon the arrival of an access opportunity, the fbs starts sensing the unlicensed band. If the spectrum is idle for a predefined time T sensing, the fbs accesses the channel and may use it for a fixed duration T celltx ; otherwise, the fbs waits for the next access opportunity. In addition, an fbs is not allowed to access the channel immediately after a channel use, in order to leave at least T attempt seconds between two consecutive transmissions for other unlicensed band users. After obtaining the channel, the femtocell will follow LTE-A air interface in the subsequent transmissions. Three key parameters governing the proposed channel access mechanism are as follows: Attempt Interval T attempt the period of the access opportunities. T attempt is used to control how frequent an fbs accesses unlicensed bands. Transmission Duration T celltx the maximum duration that an fbs can keep the channel after a successful channel access attempt. T celltx is introduced so that femtocells do not keep the channel too long to starve other systems operating in unlicensed bands. Channel Sensing Duration T sensing the pre-defined time duration that the fbs senses the unlicensed band in a channel access attempt. The unlicensed frequency band obtained by an fbs can be used in different ways. It may be solely used for communications in downlink (DL) or uplink (UL), or shared by DL and UL in FDD or TDD fashion. In the rest of this paper, we assume that the obtained unlicensed frequency band is solely used for DL. However, the analysis and conclusions can be easily generalized to the UL and the shared DL/UL cases. A. System Model We consider the scenario where a single-ap WLAN overlaps with a femtocell. The fbs accesses the unlicensed bands in the way described in Section II. We assume that WiFi and the fbs contend to access the same unlicensed band. Each contending node, WiFi or the fbs, can sense the other contending nodes. The other assumptions are listed below: 1) The number of WiFi nodes is much larger than one. 2) All nodes (WiFi and femto) always have data to transmit. 3) All WiFi stations transmit at the same data rate, which can be any one defined by the standard. 4) No channel errors in WiFi transmissions. 5) A WiFi transmission fails when colliding with other transmissions, either WiFi or femto. B. Overview of the Analysis The states of WiFi and femtocell networks are tightly coupled with each other through a key parameter P cellsucc, the probability that a fbs successfully obtains the channel in a channel access attempt. The whole network analysis is comprised of the following parts. First, we derive P cellsucc by modeling the exponential backoff mechanism of WiFi nodes with a 2D Markov chain. Second, we express a femtocell s performance, measured by t cellfrac, the fraction of channel time occupied by a femtocell, in terms of P cellsucc and femtocell channel access parameters. Finally, we express WiFi network throughput in terms of t cellfrac and WiFi parameters. C. Derivation of P cellsucc When an fbs attempts to access the channel, it senses the channel for T sensing seconds. If channel is idle during the sensing period, the attempt is successful and the femtocell will occupy the channel for a fixed duration of time; otherwise, the fbs will make another attempt at the next predefined time instant. Therefore, two conditions must be satisfied to guarantee a successful fbs channel access attempt. First, the attempt time (i.e., the start time of the channel sensing) must be located within a period where all WiFi nodes are idle. We denote Q the probability that this condition is satisfied. Second, the channel must be idle for at least T sensing seconds following the fbs 48

3 attempt time. We denote P cellsucc the conditional probability that this condition is satisfied given that the first condition is satisfied. The derivation of P cellsucc is then decomposed into finding Q and P cellsucc, respectively. We will first derive Q, the probability that the time of an fbs channel access attempt is within a WiFi idle period. WiFi users access the channel in a random fashion, resulting in random channel states (idle, collision, or successful transmission) at any given time instant. Hence Q equals to the fraction of time that the WiFi channel is idle, out of all of WiFi channel time composed of idle backoffs, collisions and successful transmissions. The fractions of time that WiFi channel is idle, in collision state and in successful transmission state, are mainly determined by WiFi exponential backoff parameters (the larger the backoff window size, the longer idle periods will be), WiFi load and number of contenders (both WiFi nodes and fbs). Since we assumed that the fbs and all WiFi nodes always have data to send, introducing a fbs to contend with WiFi nodes does not change WiFi load and WiFi network remains saturated. In addition, as we assumed that there are many WiFi nodes, the presence of a fbs increases the number of contenders only by a very small fraction. Therefore, we assume that a WiFi network has the same fractions of idle, collision and successful transmission time, respectively, in the WiFi/femtocell coexisting scenario as in the WiFi-only scenario. Hence finding Q for the WiFi/femtocell coexisting scenario is equivalent to finding the fraction of idle channel time in the WiFi-only scenario. The fraction of idle channel time in a WiFi-only network can be obtained by analyzing the WiFi exponential backoff process, as done in [8] [1]. Out of the existing studies, Foh and Tantra s analysis [9] provides very accurate probabilities for a WiFi network being in the channel state of idle, collision and successful transmission, respectively. These probabilities will be used here. As in [8] [1], the concept of time slot is extended in this work to refer to any continuous time period that a station observes. The duration of a time slot in our model can be one the following: α: the duration of a successful transmission; β: the duration of a collision period; δ: the duration of an idle backoff time slot. By modeling WiFi exponential backoff process with a 2D Markov chain, Foh and Tantra [9] obtain the probabilities P idle, P success and P collision, the probabilities that in a particular time slot the channel carries no transmission (idle), a successful transmission or two or more transmissions (collision), respectively. From these probabilities, we can obtain the fraction of time that WiFi channel is idle as follows: P idle δ Q = P idle δ + P success α + P collision β. (1) The WiFi channel can be viewed as alternating idle and non-idle (i.e., collision or successful transmission) periods. A WiFi idle channel period is a channel period between two nonidle periods and consists of a random number of WiFi idle backoff time slots. A WiFi idle channel period is composed of exactly L (L = 1, 2, 3,... ) idle time slots with probability Pidle L (1 P idle) and its duration is Lδ. In fact, (1) is just the probability that an fbs attempt happens to be located within a WiFi idle channel period; but both the length of the period and the relative location of the attempt time in the WiFi idle channel period are random. If Lδ T sensing and the attempt time happens to be located in the first (Lδ T sensing )/(Lδ) portion of the WiFi idle channel period, the fbs will find that the channel is idle during the following T sensing channel sensing time and the attempt will be successful. Given that the start time of an fbs access attempt is located in a WiFi idle channel period with L (L T sensing /δ ) idle time slots, the conditional probability for the fbs to successfully obtain the channel is (Lδ T sensing )/(Lδ). Consequently, given that an fbs attempts to access the channel within a WiFi idle channel period, the conditional probability that it successfully obtains the channel is: P cellsucc = idle(1 P idle ) Lδ T sensing, (2) Lδ where L=L P L L T sensing /δ. (3) Here x denotes the smallest integer that is no less than x. Then the probability that the fbs successfully obtains the channel is: P cellsucc = Q P cellsucc. (4) D. Femtocell Network Performance The fbs attempts to access the channel by sensing the carrier. If the channel is successfully obtained, the fbs transmits for a fixed duration T celltx ; otherwise, it will attempt again after a fixed time T attempt. The success probability for each attempt is P cellsucc. If T attempt is large compared to the DIFS and SIFS, it is reasonable to assume that the attempts will be statistically independent. As such, 1/P cellsucc attempts are needed on average for the fbs to obtain the channel and occupy it for a fixed time T celltx. Therefore, the fraction of channel time occupied by the femtocell can be expressed as where t cellfrac = = E. WiFi Performance T celltx (1/P cellsucc ) T attempt + T attempt 1/P cellsucc +, (5) = T celltx /T attempt. (6) The femtocell occupies the channel for t cellfrac fraction of the time, leaving the rest (1 t cellfrac ) fraction of time for WiFi nodes. The WiFi throughput is then determined by the fraction of time within the total WiFi time (1 t cellfrac ) that the channel state is in successful transmission instead of collision or idle. Recall our earlier assumption in Section III-C that a WiFi network has the same fractions of idle, collision and successful transmission time, respectively, in 49

4 Throughput (Mbps) Aggregate WiFi Throughput Analysis Simulation (no femto) Fraction of channel time (Percentage) Femto Channel Usage Analysis Simulation (no femto) Fig. 2. Respective performance of coexisting WiFi and cellular networks. WiFi data rates are fixed at 54 Mbps. the WiFi/femtocell coexistence scenario as in the WiFi-only scenario. Therefore, WiFi aggregate network throughput can be written as: P success E[Payload] S = (1 t cellfrac ) P idle δ + P success α + P collision β, (7) where E[Payload] is the expected number of bits in the payload of a WiFi packet and the probabilities P idle, P success and P collision are based on the analytical results for WiFi-only network [9]. IV. PERFORMANCE EVALUATION AND MODEL VALIDATION In the following simulations, we only consider downlink transmissions in cellular networks. However, the obtained results can be easily generalized to the uplink case. A. Model Validation in single-ap/single-femto Networks In this subsection, we verify our analysis for the coexistence of WiFi/femtocell in unlicensed bands with simulations. The simulations in this subsection are performed under the same assumptions as those in the analysis (Section III). We consider a network with a single WiFi AP and a single fbs in the simulations. In addition, we are only interested in the performance of WiFi and femtocell in unlicensed bands and do not investigate the cellular performance in licensed bands. 1) Simulation Setup: The system parameters for the simulations and the analytical model are summarized in Table I. The WiFi network consists of 9 WiFi stations. The AP does not initiate transmissions, while the other stations transmit UDP packets with payloads of 15 bytes to the AP at the same data rate. The WiFi RTS/CTS is enabled. All transmitters can sense each other and always have data to send. TABLE I PARAMETERS USED IN THE SIMULATIONS AND THE ANALYTICAL MODEL Slot Time 9 µs CWMin 31 SIFS 1 µs CWMax 123 DIFS 28 µs T sensing 18 µs 2) Simulation Results: We study the impact of on WiFi and femtocell performances. Attempt interval T attempt is fixed to 1ms. By increasing transmission duration T celltx, we effectively increase and obtain Fig. 2. As we can see, our analytical results match simulation results well. As predicted by equation (5), femtocell performance t cellfrac is an increasing function of ; while equation (7) suggests that WiFi performance degrades as femtocell performance improves, and vice versa, which is a very natural consequence of the channel contention between WiFi and femto. Additional simulations with different T attempt values lead to the same WiFi throughput and femto channel usages as shown in Fig. 2 and are not shown here. This demonstrates that is the main femto parameter that impacts WiFi and femtocell performance, which is consistent with our analytical results given in (5) and (7). B. Simulations of a Practical Deployment Scenario 1) Simulation Setup: In order to comprehensively study the coexistence of femtocells with WiFi in practical deployments where the unlicensed band is fully utilized, we consider the following network topology where femtocells are close to fully-loaded WiFi WLANs. There are two coexisting networks, one is cellular and the other is WiFi WLAN. The WiFi network consists of 4 APs. The cellular network is two-tiered, with one macrocell using licensed band at the top layer and 4 small cells at the bottom layer. The small cells use either the same licensed band as the macrocell or the same unlicensed band as the WiFi WLANs. The macro base station (mbs) is at the center of the macrocell with a radius of 3m. Within the macrocell, WiFi APs are randomly placed on 2D grid points with a distance of 6m between two closest grid points. For each WiFi AP, we randomly drop a femto BS within a range of 1 meters from the AP. Each WiFi AP and femto BS connects to two WiFi stations and two cellular UEs, respectively, and covers a range of 2m. There are also 5 UEs associated with the mbs. In both licensed and unlicensed bands, interference from all other nodes is taken into account in SINR computations. The bandwidth in both licensed and unlicensed bands are 2MHz, respectively. 41

5 Throughput Per Cell (Mbps) Throughput Per Cell Macrocell Small cell Throughput Per WLAN(Mbps) Throughput Per Existing WiFi WLAN Total Throughput (Mbps) Total WiFi WLAN & Cellular Throughput (a) Cellular Network (b) Existing WiFi WLANs (c) The Whole System Fig. 3. Performance of three different schemes. LTE-A is adopted as the cellular air interface; while 82.11n is adopted by WiFi nodes with a frame aggregation level of 64K Bytes (i.e., maximum MAC layer payload length is 64K Bytes). The highest PHY layer data rates in WiFi and LTE-A are 72.2Mbps and 78Mbps (after taking into account various overheads and assuming that a user uses the whole bandwidth), respectively. We fix attempt interval T attempt to 1ms and transmission duration T celltx to 2ms (i.e., = 2). The macrocell always have data to send. We consider three different ways for cellular small cells to use the licensed and unlicensed bands: (WiFi offloading): Cellular WiFi hotspots operate on unlicensed band only with 82.11n air interface. Note that we use cellular WiFi hotspots to refer to the WiFi networks which are used for offloading cellular traffic and WiFi WLAN to refer to the coexisting WiFi networks that are accessed by non-cellular users. (traditional femtocell): Femtocells operate on licensed band only with LTE air interface. (our proposed scheme): Dual-band femtocells operate on both licensed and unlicensed bands with LTE air interface. At the MAC layer, packets are assigned to the band which is not in transmission; if no bands is transmitting, packets are randomly assigned to one band. 2) Simulation Results: We compare the performance of our scheme () with two current approaches (Cases 1 and 2) in Fig. 3. As we can see, under high traffic load in cellular small cells (i.e., femtocells or cellular WiFi hotspots), the throughput of cellular small cells (Fig. 3(a)) in our scheme is 4 and 1.6 times of those in Cases 1 and 2, respectively. The throughput of the macrocell in is similar to that in Case 2 where cellular small cells also access the licensed band. Fig. 3(b) demonstrates that the performance degradation caused by the cellular small cells to the WiFi WLANs is reduced by our scheme () compared with which also uses the unlicensed band. This is due to the fact that the traffic in the small cell is diverted into two bands, instead of concentrating in the unlicensed band. In addition, the sum throughput of cellular and WiFi networks (Fig. 3(c)) under the proposed scheme is 2.5 and 1.16 times of those in and Case 2, respectively. V. CONCLUSIONS AND FUTURE WORK This paper proposes a novel framework for licensed and unlicensed coexistence under a single radio access technology, in an effort to improve femtocell performance and reduce interference in macro/femto networks. Our scheme is shown to improve the throughput of small cells and the total throughput of cellular and non-cellular users, compared with existing approaches. As future work, we plan to design a scheduler for the proposed femtocells to assign traffic into licensed and unlicensed bands by considering the QoS requirements of the traffic type (e.g., video streaming, file transfer). REFERENCES [1] D. Darlin, Cellphone carriers are turning to Wi-Fi, too, The New York Times, [Available online: nytimes.com], Sep. 11, 21. [2] AT&T Inc., Third-quarter Wi-Fi connections on AT&T network exceed total connections for 29, AT&T News Releases, [Available Online]: Dallas, Texas, October 22, 21. [3] V. Chandrasekhar, J. Andrews, and A. Gatherer, Femtocell networks: a survey, IEEE Communications Magazine, pp , Sep. 28. [4] S. Rangan, Femto-macro cellular interference control with subband scheduling and interference cancelation, arxiv, [Online] abs/17.57, 21. [5] G. Gur, S. Bayhan, and F. Alagoz, Cognitive femtocell networks: an overlay architecture for localized dynamic spectrum access, IEEE Wireless Communications, vol. 17, pp. 62 7, Aug. 21. [6] N. Sung, J. Torregoza, W. Hwang, S. Lee, and H. Yoon, A joint power control and converge scheme in a cognitive-femtocell architecture for wireless networks for throughput maximization, in Proc. of IEEE Int l Conf. on Industrial Informatics (INDIN), July 21. [7] J. P. M. Torregoza, R. Enkhbat, and W.-J. Hwang, Joint power control, base station assignment, and channel assignment in cognitive femtocell networks, EURASIP Journal on Wireless Communications and Networking, 21, Article ID [8] G. Bianchi, Performance analysis of the IEEE distributed coordination function, IEEE Journal on Selected Areas in Communications, vol. 18, pp , Mar. 2. [9] C. H. Foh and J. W. Tantra, comments on IEEE Saturation Throughput Analysis with Freezing of Backoff Counters, IEEE Communications Letters, vol. 9, pp , Feb. 25. [1] Y. Lee, D. H. Han, and C. G. Park, IEEE saturation throughput analysis with freezing of backoff counters, in Proc. of ICCOM 5, Stevens Point, Wisconsin, USA,