A Heuristic Relay Positioning Algorithm for Heterogeneous Wireless Networks Chong Shen Microelectronics Applications Integration Tyndall National Institute Cork Ireland
[email protected]
Abstract— In this paper we investigate the impact of a Relay Node (RN) placement plan on a heterogeneous wireless network called Hybrid Wireless Network with dedicated delay nodes (HWN*) in terms of system capacity, transmission delay and Quality of Service (QoS). A novel HWN* Heuristic RN Placement (HHRP) algorithm, which considers RN assistance & support for radio resource sharing such as inter-network traffic balancing, TD/CDMA soft handover, routing and user mobility has been implemented. A system level simulation is used to quantify the benefits of HWN* using HHRP algorithm over HWN* with other RN placement plans and multi-hop cellular networks.
I. I NTRODUCTION A heterogeneous wireless network refers to a wireless network that is comprised of wireless nodes with different radio access technologies or equipment with different operating systems. For example, it can be a wireless sensor network including bluetooth based radio access and zigbee 802.15.4 based radio access or a hybrid wireless network including cellular access and 802.11b/g based radio access. Specifically for a hybrid wireless network, the increase of cellular communication intensity results in a reduction in cell size, thus much denser clusters are required resulting in increased infrastructure cost and system complexity. Meanwhile, the bursty nature of communication traffic and path loss results in traffic prediction difficulty. A solution must be found to provide effective resource management, congestion control and routing. The hybrid wireless network which incorporates cellular and MANET interfaced RELAY nodes, provides a practical approach for this. Nevertheless, the dedicated relay infrastructure approach can be also be adapted to the emerging wireless sensor network in order to maintain node connectivity, support sensor data aggregation and optimise power consumption. Suppose the path loss between wireless nodes at points A and B separated by a distance D is given by PAB = kD4 , where k is a frequency dependent constant. If a RN is introduced between A and B, and R is the point the RN is placed, at a distance DA from A and DB from B, then the combined path loss in the two paths A and R, and R and B 4 4 + DB ). As D = DA + DB , it is is given by P RAB = k(DA 4 4 + DB ) obvious that PAB = k(DA + DB )4 > P RAB = k(DA given D, DA and DB > 0. The reduction in transmit power leads to a reduction in interference and hence an improvement
Dirk Pesch Centre for Adaptive Wireless Systems Cork Institute of Technology Cork Ireland
[email protected]
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Fig. 1. A simplified RN assited HWN* system with a cellular network BS, a MANET and position fixed RNs
in capacity and spectrum efficiency are expected. The HWN* management framework [10] for hybrid networking includes a Based Station Oriented Cellular Network (BSON) and a Mobile Ad hoc Network (MANET). It integrates dedicated and location fixed RNs, which creates a mesh structure using ad hoc frequencies. The major contribution of the HWN*, other than the obvious savings on node transmitted power, is that the proposed solution has minimal impact on existing cellular and MANET infrastructure. Furthermore, dual interfaced RNs have functionalities to support intra- and inter-network handover management, inter-network resource balancing, infrastructure node based MANET routing and differentiated QoS services [1]. Figure 1 presents a simplified RN assisted HWN* system. MTs use the Attractor Point Mobility Model (APMM) [1], which simulates mobile node mobility patterns in a semirealistic manner. N attractors such as a sport stadium or train station are defined and distributed at points where nodes will originate from or move to. Each RN is associated with one or more BSs, collects cellular radio resource availability data from its associated BSs, receives data from MTs and forwards data either to cellular BSs or other RNs using ad hoc
978-1-4244-2517-4/09/$20.00 ©2009 IEEE
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communications. Therefore the MT has four communication modes to choose from, which are multi-hop ad hoc mode, RN assisted multi-hop ad hoc mode, cellular mode and RN assisted cellular mode. The links between RN and RN, and RN and BS are wireless thus wiring cost for communications is saved. In order to identify relay network node locations, we formulate the RN site placement strategy as a two dimensional dynamic mapping problem, between MTs and RNs, and between RNs and BSs. A heuristic RN placement algorithm is therefore devised and the algorithm operation includes three sub-steps described as: (1) Identify ideal RN locations based on the current resource management plan and MT mobility behaviour, then generate a set of RN position candidates. (2) Formulate the RN positioning as a constrained optimisation problem, of which the goal is to maximise the overall network throughput and minimise delay, so that guaranteed QoS is met for more MTs. (3) Test RNs’ site locations recursively and update each RN’s position based on the performance result. The three procedures are executed recursively until the algorithm converges. In rest of the paper, we the state of the art for RN placement proposals for hybrid wireless networks in Section II. We describe and discuss the HHRP algorithm in Section III. Section IV provides simulation models and HHRP based HWN* performance results and their discussion. We conclude the paper in Section V and provide an outlook towards future research. II. RN P LACEMENT R ELATED W ORK The HWN*, along with other hybrid wireless networks proposals such as SOPRANO, iCAR and WINNER [2] are attempts to provide RELAY based infrastructure towards 4th generation mobile network. All of those proposals have some fundamental differences making it difficult to compare RN site placement criteria. The WINNER proposal is based on the use of only one new ubiquitous radio access interface. The iCAR project proposes to borrow cellular medium access resource
from neighbouring cells to serve cells through MANET node relay. Therefore, in iCAR RN positions are not fixed and the capabilities of RN assisted multi-hop communication are greatly reduced due to relay mobility. The SOPRANO project uses both cellular and IEEE802.11 radio accesses with focus on the inter-connection between IP technology and cellular systems using RN. Several planning approaches have been proposed to select RN sites for the multi-hop iCAR and SOPRANO cellular infrastructures. For example, the network spectral efficiency was used by [8] as the driver to optimise RN positions. The proposal made an assumption that the quality on the BS ↔ RN link is always better than the link between RN ↔ RN. This assumption can be satisfied by establishing Line of Sight (LOS) links between BS and RN or by designing links with enhanced antenna gains. The proposed HHPR algorithm also assumes good quality links between BS ↔ RN but also between RN ↔ RN. Nevertheless, it considers how to achieve spectral efficiency and stable links for ad hoc based radio access, which was not covered by [8]. Two other parameters are also popular RN selection criteria which are Physical Distance (PD) between transmitter and sink, and Signal to Interference Ratio (SIR) [9]. However, choosing PD as the criteria is not as effective as SIR but PD based RN selections are more attractive because the task can be carried out quite simply with the support of geographic location techniques and suits the available sites issue of mobile operators better too. A PD based approach is adopted by our proposal but also includes design for a RN virtual backbone mesh structure, multi-hop distance and service disruption considerations. III. RN P LACEMENT S TRATEGY FOR HWN* The cellular component of the HWN* system uses Time Division Multiple Access (TDMA). Time Division Duplex (TDD) is deployed for peer to peer communications and packet relaying as fixed RNs can be equipped with more advanced hardware and signal processing techniques [6] than
mobile nodes. However, our research considers RN cellular and MANET interface integration at layer 2 and layer 3, but does not address layer 1 or upper layer applications. From an engineering perspective, we consider a heuristic RN placement algorithm that may not provide the exact optimum but an adequate sub-optimal at an acceptable computational expense sufficient. The initial RN placement site candidates and possible topology scenarios are presented in Figure 2. The scenario I, II, and III explore the inter-network and intranetwork mobility management issues, scenario IV discusses the routing problem and scenario V is concerned with the relay structure. As RNs participate in cellular resource sharing and traffic handover along with BSs and MTs, we first propose to place RNs in positions within the coverage of several BSs, e.g. the shaded area presented in Scenario I where the RN is located within the coverage area of both BS1 and BS2 . The RN can assist with communication mechanisms such as cell breathing and TD/CDMA soft handover without the need for techniques such as RAKE receivers. For example, suppose in Scenario I the RN is associated with both BS1 and BS2 . If BS1 reduces radio signal coverage to improve interference and capacity in its area, the RN will lose its association with BS1 but can transmit data to BS2 . BS2 may at this stage reduce or increase its coverage in response to current traffic condition. The support for soft handover provides load balancing since a RN provides a data relay function. Suppose a MT is moving from BS1 to BS2 and it currently receives data from the RN. If the received signal from BS2 becomes larger than the signal from BS1 , the MT performs an inter-cell handover without changing the serving RN. Scenario II places two RNs at the coverage edge of BS1 and BS2 to facilitate the communication between two cells (It is assumed each cell has only one BS). From the received SIR value and Shannon’s channel capacity formula - we know if the bandwidth allocation ratio λB for BS transmissions and the bandwidth allocation ratio λR for RN transmissions together is 1 - Scenario II can have a slightly better capacity performance compared to Scenario I because the average received signal power of Scenario I is lower than Scenario II. However, the use of Scenario II introduces can introduce large latencies, service interruption time and equipment cost where most cells overlap. To conclude, the shaded area in Scenario I is considered an ideal RN placement location compared to Scenario II where two RNs are placed. Scenario III is also considered as an ideal location candidate. The RN at this position extends the cellular coverage without base station infrastructure support. Based on the attractor point mobility model is used on MTs, the MTs eventually converge around the attraction point. In order to mitigate resource contention, reduce service disruption and latencies for ad hoc based communication, a RN should be positioned where more MTs can associate to the RN within one or two hop distance as illustrated in Scenario IV. It is also important to place RNs with respect to traffic density prediction. Scenario V presents possible the RN network topologies. Here it is preferred that RNs compose only one
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mesh layer based on ad hoc communications rather than than several separate mesh networks to avoid extra protocol overhead thus we prefer the right topology rather than the left topology in Scenario V. The next step in the algorithm is to decide on the number of RNs needed to assist traffic relaying. The number used for simulation studies is actually calculated through network dimensioning analysis discussed in [1]. As shown in Figure 3, we first update system traffic information and initialise the HWN* in our computer simulation based evaluation. MTs then start moving using our attractor point mobility model. BSs and attraction sites are pre-defined by the system so that only MTs trajectories are needed for Scenario IV analysis. A system performance for the RN site layout is recorded and compared with theoretical analysis. The algorithm changes RNs’ locations, where results are not satisfactory. The algorithm runs recursively until an optimal solution is found. In the final site selection, a hard distant limit δ is introduced to prevent BSs and RNs coverage to overlap too much. If the distance between one RN site and any BS is smaller or equal to δ, this point will be eliminated from the final candidate list. IV. S IMULATIONS OMNeT++ [3] has been used for the HWN* system level simulation. Differentiated service classes are evaluated including voice, web and video. Packet voice traffic uses the standard on-off voice model with call arrivals modeled by a Poisson process and call duration modelled by a negative exponential distribution. Web traffic also uses a packet model with geometric distribution for packet inter-arrival times and mean of 0.125s and packet size is Pareto distributed as described in [4]. The video traffic is modelled as direct video packet input with more details in [2] . We consider all video streaming as real-time traffic, while the web service is a non real-time service. Based on a 3GPP traffic example [2], simulated traffic consists of 30% 64 Kbps streaming video, 45% general voice calls and 25% non real-time web services.
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Simulation parameters are summarised as follows: the lognormal shadow fading standard deviation σ = 10 dB, shadowing correlation distance is χs = 50 m, and mean SIR value is rd = 17 dB. The default energy model provided by the OMNeTT++ simulator is implemented, specifically, for a 250m transmission range the transmit power used is 0.282W. The transmit power used for a transmission range of d is proportional to d− 4. The MTs are randomly distributed in 13 regular hexagonal cells (1km length, 2.6 km2 ) in an 8 km x 8 km grid. From 0 to 500 MTs are scattered in the grid to simulate varied scenarios. To ensure that the same cellular frequencies are reused in the cellular mode, 7 frequencies are allocated. The MTs travels from 0 to 80 km/h. A maximum packing based RN placement is also simulated to provide comparison with our proposed heuristic in tersm of performance. Maximum packing is straight-forward but only suitable for ideal deployment scenarios where the BS sites are also packed based on similar principles. It is well known from planar geometry that to cover a two dimensional area with equal sized circles, the best possible packing solution can be obtained by surrounding each circle by six circles as shown in Figure 4. But to have connections between the RNs to & from a virtual RN backbone, an overlap is needed between relay cells. The framework considers a situation where the location of the RNs are centered with maximum coverage. The deployments shown in Figure 4 are two examples of such a pre-engineered approach. The first one tries to cover the entire area and the second one addresses a populated region scenario. A. Simulation Result and Discussion The first experiment is to present the influence of RN positioning strategies on the HWN* capacity under various traffic loads. We implemented a modified AODV [7] for the multihop cellular infrastructures to be compared with our previously proposed Adaptive Distributed Cross-layer Routing (ADCR) based HWN*. In the modified demand-driven AODV, when paths are needed by MTs, the path query messages are uni-cast to the BS. If MTs do not obtain paths from the base, then path query messages will be flooded as in AODV. The ADCR lets each MT adaptively select a most practical transmission mode with the best QoS, cellular medium resource and MANET medium resource are mutually balanced with dedicated RN [10]. Restricted MANET clusterhead selection, inter and intra system traffic handover[1] and maximum packing based RN
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positioning schemes are also included in the simulation. When the heuristic or maximum packing based RN positioning schemes are implemented for HWN*, the HWN* per cell capacity is expected to be greater than the multi-hop cellular network, especially under high traffic volume. This is because MTs, which are refused access to a particular cell, will use the RN assistance as an alternative in the heuristic plan or use a packed relay path to borrow and access other medium resources from other cells. As the traffic load grows, maximum packing based relay nodes achieve almost complete connectivity considering both cellular relay and MANET relay. Figure 5 records per cell capacity performance of three scenarios as traffic load increases. The capacities of both heuristic and maximum packing based HWN* reach maximum throughput at around 5.7 Mbps and 5.6 Mbps, respectively. As can be seen from the trend of the capacity lines, when the traffic load increases, the heuristic RN based HWN* outperforms the maximum packing RN HWN* in terms of network fairness, and its maximum capacity is approaching the theoretical gain with uniform communication fairness. It also indicates that the traffic is adaptively routed through both heuristic and maximum packing plans, while the heuristic RN plan improves the system capacity at disadvantaged locations and reduces service disruptions. In order to analyse packet delivery ratio of the HWN*, the Node Average Throughput (NAT) is defined as: N DRn 100% (1) N AT = 1N 1 DSn In total, there are N MTs in the system with n designating a particular node with 1 ≤ n ≤ N . DRn is the total number of data received by MT n and DSn is the total number of data sent to MT n. Figure 6 shows the impact of increased traffic on the node average packet delivery ratio when all nodes are sending data with full capacity during the simulations. It indicates under any traffic load, the proposed ADCR with heuristic and packing based RNs placement plans give a higher throughput than
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Fig. 7. Average end to end transmission performance of various HWN* scenarios with HHRP relay positioning
the multi-hop cellular system. The curve of heuristic RN HWN* corresponds to the case where all transmitted packets are successfully received, which can be considered to be an upper throughput bound on this proposed scheme. One can see that the increase in traffic load does not affect greatly this schemes performance. When the maximum traffic load is reached, the heuristic based RN approach outperforms the maximum packing based approahc by 3%. Overall, the average node packet delivery ratio decreases when the traffic load increases, this is mainly due to congestion and routing failure. Here, the maximum packing RN based HWN* outperforms the multi-hop cellular network by 21% on average at maximum traffic load. Apart from RN placement plan, the number of relay nodes is a critical parameter to affect the HWN* system performance. The Average End-to-end Delay (AED) is defined as: N DTn (2) AED = 1 N in total N packets are sent during transmission and n stands for the the packet ID, with 1 ≤ n ≤ N . DTn is the packet delivery time of packet n. Suppose there are N RNs planned in the network. Figure 7 presents the delay performance of three scenarios, all RNs being loaded, only 2/3N RNs loaded, and only 1/3N RNs being loaded over an increasing traffic load. The heuristic based RN placement plan is deployed for network fairness using ADCR routing. The results clearly indicate that the delay is much less in the scenario with all N RNs loaded, compared to the other two scenarios with less infrastructure nodes. One can expect that an increase of one RN reduces endto-end delay in a small domain including seven cellular BSs. However, excessive installation of RN may not be a preferable approach because there is a tradeoff between infrastructure cost and expected system performance.
and simulation results indicate that the positions of relay nodes, which compose the second HWN* communication infrastructure tier, has significant impact on the system performance in terms of wireless network throughput, average transmission delay and average node QoS. The algorithm arranges RNs to optimise connectivity at disadvantaged locations and uses both cellular and ad hoc medium resources in a fair manner. Further research will consider how to apply this two tired hierarchical communication framework to wireless sensor networks.
V. C ONCLUSIONS This paper has presented a heuristic relay node placement algorithm for a heterogeneous wireless network. The analysis
ACKNOWLEDGMENT The authors acknowledge the financial support of the Irish Higher Education Authority under the Programme for Research in Third Level Institutions cycle 4 NEMBES project in part funding the work reported in this paper. R EFERENCES [1] C. Shen, S. Rea and D. Pesch, “HWN* Mobility Management Considering QoS, Optimisation and Cross Layer Issues,” Journal of Communication Software and Systems, Vol. 4, No. 3, December 2007. [2] C. Shen, Management Framework for Hybrid Wireless Network PhD Thesis, Cork Institute of Technology, 2008. [3] A. Varga, “Using the OMNeT++ Discrete Event Simulation System in Education,” IEEE Trans. on Education, Vol. 42, No. 4, 1999. [4] ETSI, “Universal Mobile Telecommunication System (UMTS),” Technical Report TR 101112 v3.1.0, November, 1997. [5] E. Tameh, A. Nix and A. Molina, “The Use of Intelligently Deployed Fixed Relays to Improve the Performance of a UTRA-TDD System,” in Proc. of IEEE VTC Fall, Orlando, FL, USA, Oct. 2003. [6] V. Sreng, H. Yanikomeroglu, and D. Falconer, “Relayer Selection Strategies in Cellular Networks with Peer-to-Peer Relaying”, in Proc. of IEEE VTC Fall, Orlando, FL, USA, Oct. 2003. [7] Y. Hsu and Y. Lin, “Base-centric Routing Protocol for Multihop Cellular Networks,” IEEE Global Telecommunications Conference, Taipeh, Taiwan, Nov. 2002, pp. 158 - 162. [8] H. Hu and K. Yanikomeroglu, “Performance Analysis of Cellular Networks with Digital Fixed Relays,” Carleton University, May 8, 2006. [9] I. Stojmenovic, “Position-based routing in ad hoc networks”, IEEE Communications Magazine, pp.128- 134, July 2002. [10] C. Shen, S. Rea and D. Pesch, “Resource Sharing via Planed Relay for HWN*,” EURASIP Journal on Advances in Signal Processing Article ID 793126, 2008.
