Random Access Strategy in Hierarchical OFDMA Wireless Networks

Random Access Strategy in Hierarchical OFDMA Wireless Networks Young-June Choi and Saewoong Bahk School of Electrical Engineering and Computer Science Seoul National University, Shillim-dong, Kwanak-gu, Seoul, Korea TEL: +82-2-880-8434, FAX: +82-2-880-8198 {yjchoi, sbahk}@netlab.snu.ac.kr Abstract— As a promising multiple access technique for B3G cellular networks, OFDMA provides high resource granularity, but has a limitation that, in case of high mobility, it cannot perform coherent detection due to the long symbol transmission time. Therefore we present an advanced model of a hierarchical cellular system that combines multiple access techniques of OFDMA and FH-OFDMA with micro and macrocells, which handle various cases efficiently considering user mobility and traffic class. We consider the issue of designing an appropriate reuse factor for random access channels in order to overcome the intercell interference problem in hierarchical OFDMA environments. Our finding is that full sharing, i.e., a reuse factor of 1, performs best for given random access channels in flat multicell environments. Applying this result to hierarchical cellular systems, we can use random access channels for micro and macrocells separately without coordination among neighboring cells. To achieve load balancing between micro and macrocells, we devise a random access strategy combined with an admission control scheme.

I. I NTRODUCTION Heterogeneous wireless networks are popular in the next generation mobile communication systems. As a potential approach, hierarchical or interworking networks have been considered widely. Especially the hierarchical network architecture is designed to deal with high and slow mobile users effectively [1]. We design a hierarchical cellular network that consists of microcells and macrocells, each having their own multiple access mechanism, orthogonal frequency division multiple access (OFDMA) and frequency hopping (FH)OFDMA. Each mobile chooses cell type and multiple access method according to mobile speed and traffic type. OFDMA-based systems have been designed in the IEEE 802.16 group, and 802.16e-based WiBro systems, supporting maximum mobility of 60km/h, are currently under development in Korea. In order to support high mobility, the authors in [2] develop a hybrid multiple access scheme combining OFDMA and FH-OFDMA, where fast-moving users access the network via FH-OFDMA. Decoupling multiple access techniques for the hierarchical cell structure, we consider a new heterogeneous wireless network, comprised of OFDMA microcells and FH-OFDMA macrocells. OFDMA systems need to ensure an appropriate frequency reuse factor for multicell environments [3]. If two neighboring cells use the same subcarrier channels, the transmission in a cell interferes with that in the other. To overcome the

interference problem, the most widely accepted approach is to design the frequency reuse factor such that the two neighboring cells allocate subcarriers exclusively. In this paper, we design the reuse factor for random access channels in a hierarchical OFDMA system. We believe that designing random access based systems is an important issue to handle uplink scheduling as well as initial access and short message transmission effectively. Uplink scheduling, when adopted, inevitably incurs more random access attempts because each mobile with pending data packets should send short channel request packets to the base station (BS). In this work, we find that neighboring cells belonging to the same cell type can take advantage of sharing the same random access channels, while cells belonging to the different cell types should use separated random access channels. From this result, we devise a dual random access mechanism that takes advantage of load balancing between OFDMA microcells and FH-OFDMA macrocells. We organize the remainder of this paper as follows. Section II describes our new hierarchical cellular model. Section III investigates the principle on random access in flat multicell networks. Section IV proposes a dual random access mechanism that exploits load balancing of the heterogeneous cellular system followed by our conclusion in Section V. II. H IERARCHICAL OFDMA W IRELESS N ETWORKS Cells are categorized into macrocells, microcells and picocells depending on its size. Macrocells and microcells are usually deployed in rural and urban regions, respectively, while the picocells are in a building. In some region such as a hot-spot zone, a mobile station (MS) can access both macrocells and microcells like in Fig. 1. The authors in [1] designed a service model by mobility such that macrocells and micricells cover high speed and low speed MSs, respectively. This structure is effective because a high speed MS has to change cells frequently if covered by microcells. We extend the hierarchical cell structure by integrating multiple access techniques. Some systems under development are based on OFDMA that combines OFDM and frequency division multiple access (FDMA) [5]. Since OFDMA system has lots of channels in a frequency domain, it has higher allocation granularity than OFDM system. It also has the ability of taking advantage of adaptive modulation and coding

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(AMC), but its application is limited to low mobility. If an MS using OFDMA has high mobility, it cannot perform coherent detection properly due to the long symbol. Meanwhile, a FH-OFDMA system, which combines frequency-hopping (FH) and OFDMA, has the advantage of exploiting diversity [4]. Though it experiences a difficulty in supporting high data rates and AMC, it can overcome channel fading and multiuser interference through a FH pattern. Accordingly, it is a viable combination that microcells for low mobility use OFDMA that has fine granularity, while macrocells for high mobility use FH-OFDMA that is robust to channel fading and interference. Each cell plane can handle traffic classes differently as well. High rate data services are suitable for OFDMA that has high spectral efficiency and supports various data rates by AMC. In contrast, low rate services like voice are adequate for FHOFDMA that is easy to use diversity. Table I summarizes the scenario of cell selection combined with multiple access according to mobility and traffic class. If an MS has the capability of supporting dual modes, it can switch cells according to mobility and traffic type in a manner of using vertical handoff that offers an additional merit of load balancing. III. R ANDOM ACCESS IN F LAT N ETWORKS In FDMA cellular networks, the uplink transmission of a user near the cell boundary interferes with that of some other users in neighboring cells. The intercell interference hinders the BS from decoding corresponding signals properly. The same problem occurs in OFDMA networks since OFDMA is a kind of FDMA scheme. As a way of overcoming the interference problem without sacrificing efficiency, we design the frequency reuse factor in this section. Differently from

(c) Partial sharing

Fig. 2.

Channel reuse policies.

the previous work that has dealt with data channels, our work considers the reuse factor for random access channels. We describe here our solution for three different types of reuse: non-sharing, full-sharing, and partial sharing. Although the first two cases are special cases of the last, we present them separately for the purpose of illustration. Existing systems such as 2G and 3G cellular systems have adopted slotted Aloha for random access. Its appeal is in large part its simplicity of implementation. We will therefore also assume that the random access strategy being used is slotted Aloha. Our work does not consider the capture effect1 [8]. A. Non-sharing The traditional way of frequency reuse is to allocate a frequency band to a cell and to reassign the same frequency band to non-neighboring cells. Hence, each cell has its own random access channels. Fig. 2 (a) shows an example of a reuse factor of 7. Assuming that channels are independent, we obtain the total throughput of N channels by N times the throughput of a single channel [7]. Denoting the reuse factor by F , we can obtain the cell throughput for the non-sharing case, Tns , as following. µ ¶ λF Tns = λ exp − . (1) N Here λ is combined rate of new and backlogged arrivals. Assuming that the arrival rate is homogeneous for each cell, we obtain the user collision probability of the non-sharing case, pns , as pns = 1 − exp(−λ). (2) 1 A random access trial with a strong SINR is regarded as being successful even when collision occurs.

(d, d0 ∈ D, d 6= d0 ) contains each non-zero element of Γ precisely ζ times [10]. Lemma 2: Let D be a (ν, κ, ζ)-difference set in Γ. Then [10] ζ(ν − 1) = κ(κ − 1). (6)

Interference range

Target-cell range

Fig. 3.

A multicell model for full sharing and partial sharing.

B. Full sharing In the full sharing case, each cell shares random access channels fully with neighboring cells. Therefore, some arrivals generated at a cell can influence the performances of neighboring cells. To simplify the analysis, we model the interference range as in Fig. 3.2 Let B denote the number of neighboring cells and α (0 ≤ α ≤ 1) the average ratio of interference range to cell coverage per cell. The ratio α approaches 0 if the interference range shrinks, and the arrival rate induced by arrivals at neighboring cells is αBλ when users are uniformly distributed. So the combined total arrival rate λC is given by λC = λ + αBλ.

(3)

Then, we can express the throughput for the full sharing case, Tf s , as µ ¶ λC λC λ Tf s = exp − ·N N N λC µ ¶ 1 + αB = λ exp − λ . (4) N Accordingly we obtain the user collision probability for the full sharing case, pf s , as µ ¶ 1 + αB pf s = 1 − exp − λ . (5) N Comparing the results of full sharing with those of nonsharing, we obtain the following lemma. Lemma 1: If N channels are given for random access, the full sharing policy has higher throughput and lower user collision probability than the non-sharing policy. In this paper, we omit proofs due to the space limit. C. Partial sharing Partial sharing allows a cell to share a part of its channels with neighboring cells. To design a partial sharing policy, we propose a new method that uses a difference set [10]. To form a difference set, we allocate a subset of random access channels to each cell such that the number of shared channels at each cell is the same. Definition 1: Let Γ = {0, 1, ..., ν − 1}, and D(6= Ø) a κ−subset of Γ with 0 < κ < v. Then D is called a (ν, κ, ζ)difference set if it satisfies that the list of differences d−d0 6= 0 2A

similar multicell model is found in [6].

Lemma 3: Let D be a (ν, κ, ζ)-difference set in a group Γ. Then B = {D + g(mod v) : g ∈ Γ} is a symmetric set of (ν, κ, ζ) [10]. Lemma 4: There exist exactly ζ elements that are common between any two subsets X and Y such that X, Y ∈ B(X 6= Y ). Example 1: For a (7,3,1) difference set, we can select a subset (1,2,4) arbitrarily. Then (2,3,5), (3,4,6), (4,5,0), (5,6,1), (6,0,2), (0,1,3) are also the subsets that satisfy the difference set property in Lemma 3. From Lemma 4, there exists exactly one element that is common for any two subsets. Fig. 2 (c) shows an example of partial sharing by using the difference set (7,3,1). Each cell exploits three random access channels among seven and any two neighboring cells share one channel. When the network uses the difference set (N, Nd , v), we have v λC = λ + αB λ. (7) Nd With some manipulations, we can express the throughput for partial sharing, Tps , as µ ¶ 1 + αBv/Nd Tps = λ exp − λ , (8) Nd and the user collision probability for partial sharing, pps , as µ ¶ 1 + αBv/Nd pps = 1 − exp − λ . (9) Nd Comparing the results of full sharing and partial sharing, we obtain the following lemma. Lemma 5: When N channels are given for random access, the full sharing policy has higher throughput and lower user collision probability than the partial sharing policy. From Lemmas 1 and 5, we obtain the following proposition. Proposition 1: For given random access channels in multicell OFDMA environments, the frequency reuse factor of 1 is best. Additionally, comparing the performance among several partial sharing schemes, we have the following lemma. Lemma 6: For several difference sets with the same ν, the performance increases with the increase of κ or ζ. D. Numerical results We compare our analytical results with simulation results. In our simulations, we assume that users are distributed uniformly in each cell without mobility. Users in wireless communications experience different channel conditions according to path loss and fading in reality. However as we focus on the access failure due to the collision only, we assume that the BS receives a random access perfectly if there is no collision. This means that the channel model deciding the channel condition

IV. R ANDOM ACCESS IN H ETEROGENEOUS N ETWORKS A. Dual random access algorithm

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Fig. 5. Comparison of user collision probability for channel sharing policies.

is beyond the scope of this paper. In Figs. 4 and 5, we use the symbols to present simulation results, and the lines to present analytic results. We compare the throughput and the user collision probability for non-sharing, partial sharing (PS), and full sharing (FS) policies. For the non-sharing policy, we set F = 7 for a structure of seven hexagonal cells. Let N = 7, B = 6, and α = 1/6 for full sharing. Then the performance of the nonsharing policy is the same as that of single channel slotted Aloha. For the partial sharing, we use the difference sets (7,5,4), (7,4,2) and (7,3,1). Figs. 4 and 5 show the comparison results for T and p, respectively. As shown in Proposition 1, the full sharing policy performs best. Also, as proven in Lemma 6, the performance is improved when κ or ζ is high for several difference sets with the same ν.

By Proposition 1, a flat cellular network can use universal frequency bands for random access. However, this principle does not hold for hierarchical wireless networks because micro and macrocells have different cell radiuses. To reach the corresponding BSs, users in macrocells require more transmit power than in microcells. If they share the same frequency bands, the access signals for macrocells may dominate the whole frequency bands, interfering the access to microcells. Therefore, the non-sharing policy is suitable among heterogeneous cells, while the full sharing is still available among homogeneous cells. From this principle, we develop a new random access scheme combined with an admission control for heterogeneous networks. Suppose that MSs already have established connectivity to both a macrocell and a microcell. Usually MSs send a random access signal to the BS when it needs a channel assignment (whether dedicated or shared). Then the BS can accept or reject the request in the level of random access, without further MAC processing. MSs retry random access to another cell type if it does not receive any reply messages within a given number of retrials. That is, random access plays a role of admission control. We now classify the requested traffic type into three as marked in Table I: macrocell-only traffic (type 1), macrocellpreferred traffic (type 2), and microcell-preferred traffic (type 3). The macrocell-only traffic is generated by fast-moving users because OFDMA-based microcells cannot support them. The macrocell-preferred traffic is generated by voice users. Macrocells are suitable for them, but can be replaced to microcells in case macrocells cannot admit more sessions. The microcell-preferred traffic indicates general data traffic in that microcells are suitable for them because of their high capacity. The service is also available via a macrocell instead of a microcell when the microcell becomes a hot-spot. Hence we develop a dual random access mechanism (DRA) as shown in Fig. 6 in order that the requests of types 2 and 3 are accessible to both cell types when either cell is a hotspot. To request data channels by random access, random access channels 1, · · · , m are fully shared by macrocells and m + 1, · · · , n are fully shared by microcells. When MSs try random access, they do not know the power level to transmit, so increases transmission power step by step per trial, which is called a power ramping algorithm [11]. B. Numerical analysis For simple notation, we slightly change the denotation λi as the combined total arrival rate of type i. Let e be the probability of access failure, then it consists of the blocking probability by admission control q and consecutive collision probability p. In Fig. 6, the algorithm restricts the maximum number of retrials to maximum of k. Generally the probability collision occurs k times continuously is small, so the access blocking will dominate e, and we let e ≈ q. The total arrival

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rate for a macrocell, λ+ A , is comprised of successful access of types 1/2 and retrial of type 3 blocked by microcells, which is expressed by λ+ A

= (1 − eA )(λ1 + λ2 ) + eI λ3 ,

(10)

where subscripts A and I indicate macrocells and microcells, respectively. Similarly, the total arrival rate of a microcell, λ+ I , is given by λ+ (11) I = eA λ2 + (1 − eI )λ3 . From (4), we obtain the throughput of a macrocell, TA , as the following: µ +¶ λ TA = λ+ exp − A . (12) A m Similarly, the throughput of a microcell, TI , is given by µ ¶ λ+ I TI = λ+ exp − . (13) I n−m The blocking probability q is given by the admission control. For simplicity, assume that the service time of admitted users is exponentially distributed with mean 1/µ and the system allows maximum K users to be admitted, then the behavior follows the M/M/1/K queueing model. The blocking probability q is given by, [9] µ + ¶K 1 − λ+ λi i /µ qi = , (14) ¡ + ¢K+1 µ 1 − λi /µ where i is either A (macrocell) or I (microcell). To show throughput of the DRA scheme simply, we assume that a microcell is heavily loaded with λ3 = 18, µ = 20, and K = 10. Then we obtain qI = 0.051 from (14). Fig. 7 shows TA according to λ1 for m = 7 and λ2 = 0. When the macrocell is loaded normally, it exploits load balancing effectively, so the DRA scheme achieves larger throughput than the conventional random access scheme (RA). On the other hand, the throughput is reduced when the macrocell is also heavily loaded.

Fig. 7. Throughput comparison between conventional random access (RA) and dual random access (DRA) schemes.

V. C ONCLUSION In this paper, we designed a new heterogeneous wireless network consisting of OFDMA microcells and FH-OFDMA macrocells. Random access channels can take advantage of full sharing between neighboring cells if they belong to the same cell type. On the contrary, non-sharing between neighboring cells is recommended if they belong to different cell types. Hence we develop the dual random access scheme to achieve load balancing effectively, and it is confirmed by the analysis results. R EFERENCES [1] C.-L. I, L. J. Greenstein, and R. D. Gitlin, “A microcell/macrocell cellular architecture for low- and high-mobility wireless users,” IEEE Journal on Selected Areas in Communications, vol. 11, no. 6, pp. 885891, Aug. 1993. [2] W.-I. Lee, B. G. Lee, K. Lee, and S. Bahk, “An OFDMA-based next-generation wireless downlink system design with hybrid multiple access and frequency grouping techniques,” to appear in the Journal of Communications and Networks, 2005. [3] T. Rappaport, “Wireless Communications,” Prentice Hall, Second Edition, 2002. [4] Y. Kim et al, “Beyond 3G: vision, requirements, and enabling technologies,” IEEE Communications Magazine, pp. 120-124, Mar. 2003. [5] IEEE 802.16-REVd/D4-2004, “Part16: Air Interface for Fixed Broadband Wireless Acces Systems,” Mar. 2004. [6] S.-L. Su, J.-Y. Chen, and J.-H. Huang, “Performance analysis of soft handoff in CDMA cellular networks,” IEEE Journal on Selected Areas in Communications, vol. 14, no. 9, pp. 1762-1769, Dec. 1996. [7] M. A. Marsan and M. Bruscagin, “Multichannel Aloha networks with reduced connections,” in Proc. INFOCOM 87, pp. 268-275, Mar. 1987. [8] W. Yue, “The effect of capture on performance of multichannel slotted Aloha systems,” IEEE Transactions on Communications, vol. 39, no. 6, pp. 818-822, 1991. “Queueing Systems, volume1: Theory,” Wiley[9] L. Kleinrock, interscience, 1975. [10] A. Pott, P. Kumar, T. Helleseth and D. Jungnickel, “Difference Sets, Sequences and their Correlation Properties,” Kluwer Academic Publisher, 1998. [11] 3GPP TS 25.211-215, ver3.5.0, Technical Specification Group Radio Access Network, Mar. 2001.