ADHOC RELAY NETWORK PLANNING FOR IMPROVING CELLULAR DATA COVERAGE

ADHOC RELAY NETWORK PLANNING FOR IMPROVING CELLULAR DATA COVERAGE Hung-yu Wei, Samrat Ganguly, Rauf Izmailov NEC Labs America, Princeton, USA 08852, {hungyu,samrat,rauf}@nec-labs.com Abstract Non-uniform coverage is a major concern in cellular data networks based on HSDPA/HDR access technologies. Poor coverage lowers the overall utilization of the cell and results in location-dependent downlink throughput for mobile users. We focus on the planning of Ad hoc Relay Network (ARN) in providing an improved cellular coverage. Specifically, we present and discuss issues and approaches for relay node placement in cellular space. Through extensive simulation modeling, we provide the evaluation of the improvement in the location dependent cellular data rate by employing the ARN. Keywords HSDPA, Ad hoc network, Multihop cellular, 4G wireless, Node placement, Optimization. I. INTRODUCTION To cope up with the growing demand in downlink data rate, UMTS specifications recently defined the "high speed downlink packet access" (HSDPA) [1]. HSDPA can provide up to maximum 10Mb/s downlink data rate. However, poor coverage of a cell caused by path-loss and fading is the main reason why a user may not be able to get the maximum 10Mbps data rate that HSDPA specifies. Proper cell dimensioning by adjusting the cell size or base station location may be solution for alleviating the coverage problem albeit has its own drawbacks. Smaller cells can significantly increase inter-cell interference; they also require high cost of backhaul connection (between base stations (BS) and the Radio Access Network). Further, the coverage cannot be reconfigured based on change in traffic/user concentration. A most promising solution to the above problem is the use of Adhoc Relay Network (ARN). The goal of ARN is to provide a uniform downlink data rate across the cellular space. Each node in the ARN is capable of relaying 3G data traffic to the mobile client through a multi-hop relay. The relay network is created using inexpensive 802.11a/b/g based WiFi technology providing high intra-relay throughput of up to 56Mb/s on an unlicensed channels noninterfering with 3G spectrum. Adhoc Relay Network can connect two points in cellular space with unequal data rates and relay traffic from a better coverage area (high data rate) to a poor coverage area (low data rate). We refer the above application as spatial capacity filling of the cellular data network, which results in a improved downlink data rate across the cell. Relay node Relay node BS Figure 1: ARN in cellular space. Mobile client Figure 1 shows a deployment of relay nodes forming an ARN. It also shows how relay nodes (RN) are used to relay data from the BS to the mobile clients. In order to use the ARN, the mobile handset needs to have dual (3G and WiFi) interfaces. Several architectures have been proposed to integrate ad hoc relay with cellular systems. ODMA (Opportunity Driven Multiple Access) [3] is a 3GPP proposal to add relay functionality to 3G cellular systems. Nevertheless, ODMA targets on conducting relay in TDD-WCDMA instead of applying WiFi relay over 3G systems. In Multihop Cellular Network [4], multihop relay is integrated with cellular systems to lower transmission power and to reduce the number of base stations. SOPRANO [5] introduces a selforganizing ad hoc overlay architecture for CDMA-based cellular network to enhance network capacity and to provide system adaptivity and scalability. Load balancing voice traffic between cells with relay stations is considered in icar system [6]. Recently, architecture of ad hoc relay networks that integrate with high-speed dynamic-rate cellular data access (such as HDR [8] or HSDPA) was proposed in the UCAN architecture [7]. However, the authors in [7] did not consider the ARN design and planning problem for deployment as the authors assumed relay by typically mobile users.

In this paper, we focus on the problem of how to deploy the relay nodes in a cellular space. We assume that information about long-term data rate on HSDPA is known as a function of location (given as grid points). In the relay node (RN) deployment problem we consider the optimisation problem with the objective of minimizing the number of relay nodes required to provide uniform coverage. The proposed solution to the above problem also provides the locations where relay nodes needs to be deployed. Past research on coverage problems related to various wireless and sensor networks (such as in [9]) was focused on how to cover the given space. In our case of ARNenabled cellular data relay, the problem is about how to improve the reception in a spot with poor coverage by relaying data from a spot with good coverage. The rest of the paper is organized as follows. In the next section we outline the premise and system model for the node placement problem. In section III, we present and discuss the node placement algorithm. In section IV we demonstrate location dependent HSDPA rates with and without ARN. Evaluation of the coverage is studied in section V. Finally in section VI, we draw some important conclusions. II. ASSUMPTIONS AND SYSTEM DESCRIPTION We consider an HSDPA cell with a coverage area that is mapped into grid points. The long-terms HSDPA rates at each of these points are assumed to be known. The rates depend on path-loss, inter-cell interference/fading etc. The relay nodes (RN) are deployed only at the grid points. Each relay node is associated with a radius R of receiving and transmission. For a relay node RN-A to receive traffic from RN-B, radius of RN-A must cover the location of RN- B as shown in Figure 2. Figure 2: Covering a grid area with relay nodes. A B This figure also shows a shaded part representing the region of poor coverage where the HSDPA data rate is below some pre-specified threshold. As shown in the figure, one can place the relay nodes represented by the black dots to cover the shaded region. The dotted curve shows a possible way of reaching A from a good location B. III. RELAY NODE PLACEMENT ALGORITHM A. Overview We are interested in the optimal relay node placement under two scenarios. In the first scenario, we would like to find out how many relay nodes are needed to make data rate at every location within in a HSDPA cell greater than some minimum threshold, and where should we place those relay nodes. In the second scenario, we are given a certain number of relay nodes, and we would like to find out where should we place these relay nodes so that we could maximize the minimum rate in the HSDPA cell. Due to fading and the difference in distance between mobile terminals and the HSDPA base station, mobile terminals suffer from non-uniform data rate across a HSDPA cell. The above two problems aim at providing an improved coverage of the cell and enhance Quality of Service (QoS) at locations with poor signal reception. The first scenario minimizes the number of nodes with the constraint on minimum prespecified rate. The second maximizes the minimum rate through placement with a limited number of relay nodes. B. Minimizing number of relay nodes In the first scenario, we are given r(p) (the HSDPA rate function at location p) and the target rate threshold constant γ. Our goal of the first algorithm is to find out N γ (the minimum number of relay nodes) and M γ (the deployment locations of these relay nodes) to achieve the target rate within the cell. The optimal placement of the relay nodes is an NP-hard problem. We propose a heuristic algorithm with low computational overhead for the relay node placement problem. This problem is modeled in a discrete way. We have a finite set P that includes all possible location p to deploy ad hoc relay nodes. The raw HSDPA data rate at location p is denoted as r(p). We initialize the set M γ as an empty set and add the relay node placement location k into the set M γ in each step of iterations until the data rate at all locations x will be greater than the target rate threshold γ. The first algorithm is illustrated in Figure 3. First, we compare the raw data rate at each location with the minimum rate threshold γ. Then, we partition all of these discrete locations into two sets that are served with good HSDPA rate and poor HSDPA rate, respectively. The good set is denoted as P +. The poor set is denoted as P -. Then, we consider the good nodes as possible locations for initial relay node deployment. In each iterative step, we compute a function α(p), the neighboring poor locations of p. We

compute the size of α(i) for each good node i. This heuristic algorithm is a greedy algorithm that places relay node at the good location with the greatest number of poor neighbor nodes so that placing relay node at this location can fill the most HSDPA spatial capacity gaps. The location k will be selected if it has the largest number of poor neighboring nodes. If there is more than one location have the largest number of poor neighboring nodes, we could use the service rates at p as the tiebreaker. After deciding to place a relay node at location k, we update the good location set and poor location set. This procedure will run iteratively until there is no more poor location in this HSDPA cell to be covered. This approach extends good coverage area in each iterative step. Choosing a good location that covers the maximum number of points at each step generally reduces the required number of relay nodes, even though this may not always lead to a global optimum solution. Given target rate γ bps Initialize M γ = Define Set P+ = { z r( z) > γ, z P} Define Set P = { z r( z) γ, z P} While { P } { depends on the results of Algorithm 1. If the Algorithm 1 indeed provides the optimal solution, Algorithm 2 results in optimal solution by placing the limited number of relay nodes at the best locations. Given n relay nodes Objective: Find optimal relay node placement so that max min ri ( ) Find γ so that N γ = Place n relay nodes at location M γ n i P Figure 4: Algorithm 2 Find the maxmin rate of the cell and optimal placement given n relay nodes. IV. LOCATION DEPENDENT USER RATE IN HSDPA In Figure 5, we show a typical HSDPA cell with location dependent user rates. For the sake of clarity, only a quarter of the HSDPA cell is shown in this figure. The base station is located at the location (1200,1200), the far end of the figure. Both path-loss and long-term lognormal shadowing is considered in the radio propagation model. The standard deviation of lognormal shadowing is equal to 8dB. } End while α() i = { z dist( z,) i d, z P } k = arg max( α( i) ) i P+ Add k to M γ Add α( k) to P + Remove α ( k) from P tx N γ = M γ Figure 3: Algorithm 1- Find minimum number of relay nodes to achieve target rate γ. C. Maximizing minimum HSDPA rate In the second scenario, we are given a fixed number of relay nodes n to maximizing the minimum rate in all locations within the HSDPA cell. The algorithm shown in Figure 4 gives the optimal placement of these relay nodes to achieve the maxmin rate. While using Algorithm 1, we can create a mapping table of target rate threshold γ and the corresponding N γ and M γ.. We could simply find the N γ that is closest to n and the corresponding M γ.. Placing these relay nodes at M γ. will result in the best maxmin rate. Algorithm 2 Figure 5: Location dependent user rate in a HSDPA cell without relay. To alleviate the low data rate due to poor signal reception, we place the relay nodes to improve the supported data rate in the poorly covered regions. In Figure 6, we demonstrate the location dependent rate with ARN deployment. The target rate threshold is 3000kbps. In the figure, we use dots to indicate the good locations (data rate is higher than the target threshold) and x s to indicate the poor locations (data rate is lower than the target threshold) before placing any relay node. The deployment locations are depicted with circles in the figure. With the heuristic algorithm 1, it requires 10 relay nodes to cover the low data rate area. On

the other hand, Figure 7 presents the optimal relay node placement with the same 3000kbps target rate. In the optimal placement, only 8 relay nodes are needed to achieve the 3000kbps minimum rate. Note that the HSDPA user data rates shown in Figure 6 are higher in general because more relay nodes are deployed in the Figure 6 case than the Figure 7 case. performance is close to the optimal placement. Only when γ = 2000kbps, 3000kbps, and 3500kbps, the greedy algorithm does not perform as well as the optimal placement. In other cases, given the target rate, the required number of relay node computed by greedy algorithm is the same as the case in optimal placement. Relay Node Good Poor Figure 6: Location dependent user rate in a HSDPA cell with greedy relay node placement (target rate = 3000kbps). Figure 8: Comparison of greedy algorithm and optimal placement. Relay Node Good Poor Figure 7: location dependent user rate in a HSDPA cell with optimal relay node placement (target rate = 3000kbps). V. COMPARISON OF GREEDY ALGORITHM AND OPTIMAL SOLUTION A. Required number of relay nodes We compare the greedy relay node placement algorithm to the optimal placement with different target rate threshold values. We use the same location dependent HSDPA rates shown in Figure 5. Figure 8 shows that the greedy algorithm B. Capacity enhancement with ARN 1) Methodology We implement a discrete-time simulator to evaluate the HSDPA system performance with or without ARN. The 3G HSDPA model is implemented based on the 3GPP specification [1]. Fifteen channelisation-spreading codes with spreading factor 16 are used for HSDPA data channel with frame length equals to one Transmission Time Interval (TTI=2ms). Radio propagation models suggested in [2] are adopted. The path-loss model for vehicular environment with 15-meter base station antenna and 2000MHz carrier frequency is used. Long-term shadow fading is modeled with lognormal distribution with standard deviation of 8dB. Rayleigh fading is used for short-term multipath fading modeling. Adaptive modulation and coding schemes are applied according to the average value of instantaneous SINR during one TTI. The peak rate of 10.6Mbps is achieved while applying 64-QAM modulation with rate 3/4 turbo coding in good SINR condition [1]. The base station allocates the time slot to a mobile terminal at the beginning of a TTI slot. Three scheduling algorithms are implemented. The C/I scheduler allocates time slots to the users with the best instantaneous signal quality. The Round Robin (RR) scheduler allocates time slots to mobile terminals alternatively regardless of their radio signal quality. The proportional fair (PFair) scheduler tracks instantaneous SINR values as well as the previous allocation

history. The proportional fair scheduler allocates time slot to the mobile terminal with the maximum r i /µ i value, in which r i represents the instantaneous achieve user rate of mobile terminal i and µ i represents the previously allocated rate to mobile terminal i. Table 1 System throughput with different schedulers Mbps PFair RR C/I Optimal 9.508 7.856 10.58 Greedy 9.536 8.079 10.58 Random 8.458 6.786 10.58 HSDPA 8.108 5.409 10.58 2) Simulation results We simulated the HSDPA system under the three scheduling algorithms with and without ARN. The overall throughput in Mbps is shown in Table 1. There are 7 relay nodes deployed in the 1200-meter radius HSDPA cell with different deployment strategies. 49 mobile terminals are uniformly placed in the cell. When relay nodes provide high-speed connection from the HSDPA base station, mobile terminals that are covered by ARN service will connect through those high-speed relay nodes; otherwise, mobile terminals will connect directly to the HSDPA base station. Three relay node placement strategies: (1) optimal placement, (2) greedy placement, and (3) random placement are investigated. The base HSDPA cellular system without ad hoc relay is denoted as HSDPA in the table. As shown in the table, both optimal RN placement and greedy RN placement effectively improves the system throughput, compared to the base HSDPA system. The greedy algorithm performs equally well as the optimal algorithm. The average throughput with greedy relay node placement is even slightly higher than the optimal case, which is due to the fact that criteria of optimality is based on minimizing the number of relay node deployment rather than maximizing the overall throughput. While selecting the possible relay node deployment locations, the overall throughput improvement is hard to predict unless conducting several simulation runs. This is also the main reason that we choose maxmin as the optimal placement criteria. The proposed greedy algorithm not only provides a straightforward method to determine relay node placement, but its capacity improvement also achieves the same level of improvement in the case of optimal placement. On the other hand, the randomly placed relay nodes improves HSDPA throughput slightly but is far from the performance of greedy algorithm and optimal algorithm. VI. CONCLUSIONS In this paper, we proposed a solution for improving cellular data coverage using adhoc relay network (ARN). Specifically, we focussed on the relay node placement algorithms. The ARN architecture deploys dual-mode ad hoc relay nodes on high-speed cellular system to provide better system coverage and enhance location dependent user throughput. The relay node placement algorithm is the core component of our ARN network-planning tool that facilitates the ARN deployment. Since the optimal relay node placement algorithm is NP-hard, we propose a heuristic relay node placement algorithm that requires less computational complexity and achieve a comparable performance as the optimal algorithm. In terms of both the minimum number of relay node to achieve a given rate threshold and the overall HSDPA throughput, the heuristic algorithm performs almost as well as the optimal algorithm. REFERENCES [1] 3GPP, "TR25.848 V0.5.0, Physical Layer Aspects of UTRA High Speed Downlink Packet Access(HSDPA)," January 2001. [2] ETSI, "TR 101 112 V3.2.0 Universal Mobile Telecommunications System (UMTS) Selection procedures for the choice of radio transmission technologies of the UMTS (UMTS 30.03 version 3.2.0)," April, 1998. [3] 3GPP, "TR 25.924 V1.0.0. TSG-RAN Opportunity Driven Multiple Access," 1999. [4] Y.-D. Lin and Y.-C. Hsu, "Multihop cellular: a new architecture for wireless communications," in IEEE INFOCOM, 2000. [5] A. N. Zadeh, B. Jabbari, R. Pickholtz, and B. Vojcic, "Self-organizing packet radio ad hoc networks with overlay (SOPRANO)," IEEE Communications Magazine, vol. 40, pp. 149-157, 2002. [6] H. Wu, C. Qiao, S. De and O. Tonguz, An integrated Cellular and Ad hoc Relaying system: icar, in IEEE Journal on Selected Areas in Communications (JSAC), Vol. 19, No. 10, Oct. 2001. [7] H. Luo, R. Ramjeey, P. Sinha, L. Li, S. Lu, UCAN A Uni ed Cellular and AdHoc Network Architectur, In proceedings of Mobicom, SanDeigo 2003. [8] P. Bender, P. Black, M. Grob, R. Padovani, N. Sindhushyana, and A. Viterbi, "CDMA/HDR: a bandwidth efficient high speed wireless data service for nomadic users," IEEE Communications Magazine, vol. 38, pp. 70-77, 2000. [9] K. Kar and S. Banerjee, Node placement for connected coverage in sensor networks. In Proceedings of the second WiOpt Workshop, 2004.