Interference-Aware Routing in Wireless Multihop Networks

7/31/2019 Interference-Aware Routing in Wireless Multihop Networks

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Cross-layer Interference-aware Routing for WirelessMulti-hop Networks

Tamer ElBatt Timothy AndersenInformation and System Sciences Laboratory Dept. of Mathematical Sciences

HRL Laboratories, LLC Rensselaer Polytechnic InstituteMalibu, CA 90265, USA Troy, NY 12180, USA

ABSTRACT

In this paper we address the problem of interference-aware routingthat tightly couples the design of the lower three layers of the ISOOpen Systems Interconnection (OSI) protocol stack. This is pri-marily motivated by theobservation that shortest path routing couldpotentially lead to degrading the single-hop throughput which con-stitutes an upper bound on the end-to-end multi-hop throughput.We introduce the concept of set-based routing in an attempt to in-

corporate interference into the routing decision as well as reducethe problem complexity. Towards this objective, we propose anovel algorithm that takes routing, scheduling and power controldecisions for a set of interference-coupled transmitters. Further-more, we discuss set coordination schemes for combating inter-setinterference. Finally, we conduct a simulation study that showsconsiderable throughput improvement over a reference system thatuses minimum hop routing and collision-free scheduling.

Categories and Subject Descriptors

C.2.1 [Computer-Communication Networks]: Network Archi-tecture and DesignWireless Communication

General Terms

Algorithms, Performance

Keywords

Routing, scheduling, power control, interference, cross-layer de-sign, simulation

1. INTRODUCTIONThe problem of routing over multi-hop wireless networks has

received considerable attention in the literature, primarily, alongtwo thrusts: i) Efficient route discovery/maintenance under mobil-ity conditions and time-varying topologies and ii) Developing linkmetrics to match a wide variety of performance objectives. Most ofthe protocols under the former thrust adopt the shortest path (SP)routing criteria widely employed in wireline networks [1]. Underthe second thrust, a variety of routing metrics have been introduced

for the purposes of energy-efficiency, link stability, minimum de-lays, etc. Nevertheless, the problem of interference-aware routing

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that tightly couples the MAC and routing decisions has not receivedadequate attention in the literature. [2] establishes bounds on theoptimal throughput of interference-limited wireless multi-hop net-works given the existence of an omniscient and omnipotent cen-tral entity. However, developing interference-aware routing proto-cols was left as an open problem. The problem of joint routing,scheduling and power control has been recently addressed in [3, 4,5]. However, [3] assumes no interference between links in the sameneighborhood sharing the same slot. On the other hand, [4] focuses

on the simple setting of symmetric one-dimensional multi-hop net-works. The work in [5] is the closest to ours, however, it hinges onthe Gaussian approximation for interference [6]. This assumptionfacilitates separating the MAC and routing portions of the prob-lem which considerably simplifies the problem. In this paper, wepresent a solution to the problem without making any assumptionsabout the structure of interference known to be non-Gaussian incase of finite number of interferers.

This work constitutes a step beyond our earlier work [7] to in-corporate the impact of interference in the design of higher layerprotocols. Our contribution in this paper is two-fold: i) Introduce anovel hop-by-hop set-based routing concept that incorporates inter-ference into the routing decision of spatially close transmitters andii) Introduce a joint routing, scheduling and power control (RSP)

algorithm that solves the problem within each set. Motivated bythe challenge of defining interference-aware link metrics, the com-plexity of the optimal RSP over the entire network [8] and thenegli-gible interference among spatially far nodes, the solution proceedsthrough three steps: i) Construct interference-coupled transmitterssets, ii) Resolve intra-set interference via the RSP algorithm andiii) Resolve potential inter-set interference via set coordination.

The paper is organized as follows: In section 2, the problemis motivated. In section 3, we highlight the challenges associatedwith interference-aware routing followed by a detailed descriptionof set-based routing along with the RSP algorithm. The simulationresults and discussion are given in section 4. Finally, conclusionsare drawn in section 5.

2. MOTIVATION

In this section, we show that handling MAC and routing deci-sions in isolation could lead to throughput degradation in wirelessmulti-hop networks. This is confirmed with the aid of a simpleexample. Consider a wireless network consisting of 13 station-ary nodes with connectivity shown in Figure 1. For this exam-ple, we assume time is slotted and the channels are constant overa time slot. Moreover, we assume two source-destination (S-D)pairs (S1,D1) and (S2,D2) with identical traffic demands andthere is always a packet in the queue of each source ready for trans-mission. We compare the average slot throughput and end-to-end(E2E) throughput of three routing policies. The slot throughput is


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defined as the average number of successful transmissions per slot.In this section, the E2E throughput is defined as the average num-ber of packets that are successfully transferred from each source toits respective destination over d slots.

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Figure 1: Schedules associated with 3 routing policies. The notation

used in (b) for the transmission schedule S1, S2, I1I2 can be inter-

preted as: node S1 transmits in slot 1, node S2 transmits in slot 2 and

nodes I1 and I2 transmit simultaneously in slot 3.

First, we consider minimum hop (MH) routing which is a specialcase of SP routing when all links have unit metrics. Based on the

topology, there are two MH routes for each pair and we considerfirst the MH routes that pass through node I0 as shown in Figure1(a). It is evident that I0 constitutes an interference bottleneck atthe MAC layer. This is a direct consequence of ignoring the inter-action among the chosen paths through the amount of interferencea certain path may introduce to others. We refer to this problemas interference-induced congestion which constitutes a fundamen-tal challenge unique to wireless networks. Thus, the two sourcenodes can not simultaneously transmit to I0 in the same slot, andneither one can transmit while I0 is transmitting. Moreover, I0 canforward only one packet to either destination at a time. This yieldsan average slot throughput of 1 transmission per slot. Moreover,this policy consumes 4 slots for a single packet transfer from eachsource to its respective destination (referred to as the Transmission

Cycle). Hence, we conclude that the above routing policy can trans-fer d4

packets, from each source to its destination, over d slots.Second, we consider another MH routing policy where node S1

forwards to I1 and S2 forwards to I2 as shown in Figure 1(b). Weassume that nodesI1 and I2 are spatially close enough to: i) preventsimultaneous reception from the source nodes and ii) prevent I1from receiving while I2 is transmitting and vice versa. In addition,we assume that nodes D1 and D2 are spatially far enough to allowsimultaneous reception of their packets. Clearly, this policy shouldyield better performance due to choosing different next hops for S1and S2. However, the next hops are very close which still restrictsspatial reuse. The transmission cycle becomes 3 slots, the averageslot throughput 4

3transmissions per slot and the E2E throughput

increases to d3

packets, i.e. 33% improvement over Figure 1(a).Finally, we examine the performance of a routing policy that uses

longer paths, yet, spatially far enough to allow simultaneous trans-missions over both paths. Assume that S1 follows the 3-hop paththrough I3 and I4, whereas S2 follows the path through I5 and I6as shown in Figure 1(c). If slot reuse is allowed on the same path,then the average slot throughput can be shown as 3 transmissionsper slot and the transmission cycle becomes 2 slots. Moreover, theE2E throughput turns out to be d

2packets, i.e. two-fold improve-

ment over the first policy and 50% better than the best MH routingpolicy in Figure 1(b). This confirms the fundamental role inter-ference plays in limiting single-hop throughput and, consequently,E2E throughput in wireless multi-hop networks.

3. INTERFERENCE-AWARE ROUTING3.1 Challenge

Our focus is to incorporate interference awareness mechanismson top of table-based routing. Thus, we assume that next hop nodesresiding on all possible routes to destination (not only MH routes)are known at each hop. Two nodes are single-hop neighbors if theycan communicate using maximum power assuming no interference.Incorporating interference into on-demand routing decisions is outof the scope of this paper. We consider n nodes which commu-

nicate only via the wireless medium and are indexed with uniqueIDs from 1 to n. All nodes share the same frequency band andtime is divided into slots that are grouped into frames. Each frameis of fixed length and divided into a control sub-frame and a datasub-frame which consists ofd slots. We focus on unicast traffic.

Next, we identify the fundamental challenge associated with defin-ing link-based interference-aware routing metrics. Under SP rout-ing, the path length (which depends on the link metric) is the onlyfactor that decides the best route between any S-D pair. Variousexamples of link metrics in the literature, namely Euclidean dis-tance, residual battery charge, and buffer occupancy, depend solelyon the two nodes forming the link. They are independent of theexistence of other S-D pairs or their SP routes. This, in turn, hasled to the notion of link metrics and link-based routing. However,interference depends on the existence of other sources/intermediaterelays and their spatial separation. Hence, the routing decision of agiven S-D pair becomes coupled to the routing decision of other S-D pairs. Accordingly, the notion of a link metric that incorporatesinterference becomes impossible as illustrated by the following ex-ample: Assume node a is transmitting to next hop b and node uis transmitting to next hop v as shown in Figure 2(a). Accord-ing to the non-linear decay of power with distance, governed byPr(z) = Pt z

where Pt is the transmitted power, z is thedistance between transmitter and receiver and is the path lossexponent, the amount of interference at node v from transmittersother than u is given by: Iuv = Pab z

av . If node a was

transmitting to a different next hop (say c) as shown in Figure 2(b),then the amount of interference seen at node v would be different:Iuv = Pac z

av . Thus, the interference introduced to linkuv

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Figure 2: The challenge of defining an interference-aware link metric:

interference introduced to link uv depends on the routing decision of

node a which, in turn, depends on the interference caused by node u

(needed to compute its link metric) depends on the routing decisionof transmitter a which, in turn, depends on the routing decision oftransmitter u. This suggests that there is a fundamental hurdle to-

wards defining link metrics that explicitly account for interference.In addition, it constitutes a key observation that motivates the no-tion ofset-based routing presented next where the MAC and rout-ing decisions are taken jointly for a set of spatially close transmit-ters (as opposed to link-based routing where the routing decision istaken for each transmitter independently).

3.2 Cross-layer Set-based RoutingIn this section, we incorporate interference into hop-by-hop rout-

ing decisions through the concept of set-based routing where decid-ing next hops is taken jointly for a set of transmitters. The proposed


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algorithmis executed on a frame-by-framebasis where a packetcanprogress at most one hop in each frame. Thus, given a set ofKS-Dpairs at the beginning of a frame (where source nodes may be orig-inal sources of packets or intermediate relays), we first investigatethe problem of set construction and its associated trade-offs in lightof the clustering algorithms introduced in the literature. Second,we introduce the joint routing, scheduling and power control (RSP)algorithm which constitutes an important contribution of the paper.Finally, we discuss candidate set coordination schemes to combat

inter-set interference.

3.2.1 Set Construction

The problem of grouping transmitters depending on their inter-ference coupling exhibits similarity to the problem of clustering inmobile ad hoc and sensor networks [9]. In contrast to the knownmotivation for clustering (e.g. local processing and communica-tions for scalability and reduced overhead), it is solely motivatedhere by interference. For instance, [5] groups transmitters in a pre-specified geographical region in the same cluster. However, inter-cluster interference was approximated as static ambient noise. Inthis paper, we adopt a simple topology-based criteria for defininginterference-coupled transmitters (ICT) sets, such that transmitterswithin H hops from a specific transmitter belong to the same ICTset. Otherwise, they belong to different sets. Recall that transmit-

ters (original sources/intermediate relays) and final destinations arethe only information available at this point of the algorithm, i.e.next-hop receivers are not known yet, and hence, set constructionis solely based on transmitters spatial separation. Despite its sim-plicity, the topology-based criteria could dynamically control theset size, depending on the number of transmitters and their spatialseparation, via adapting the parameter H. This gives rise to an in-terference model that explicitly accounts for interference caused bytransmitters who are within the same set. Otherwise, interference ishandled indirectly as discussed in section 3.2.3. This model is con-sidered more realistic than the circular interference range typicallyused in 802.11 modelling.

One way to construct ICT sets according to a pre-specified pa-rameter H is described next. Given a set ofK S-D pairs at thebeginning of a frame, the algorithm goes through all nodes, in anascending order of their IDs, where it skips nodes who have noth-ing to transmit in the current frame. On the other hand, nodes whowish to transmit in the current frame disseminate their IDs to theirH-hop neighbors via H-hop flooding. By the completion of the al-gorithm, each node in the network would have a list of up to H-hopaway transmitters. Nodes who have nothing to transmit simply ig-nore their respective lists whereas transmitter k considers its H-hoptransmitters list as ICTk set, becomes the leader of this set andnotifies set members. Thus, the above algorithm constructs K ICTsets, one per transmitter. This, in turn, leads to overlapping amongmost of the constructed sets (i.e. transmitters who belong to multi-ple ICT sets at the same time). Coordination schemes for handlingset overlapping are discussed in section 3.2.3.

Notice that transmissions in the ICT set construction phase are

carried over the control sub-frame at the beginning of each frame,possibly using TDMA contention-free scheduling. This is justi-fied by the fact that control messages are considerably smaller thandata packets and, hence, contention-free transmission should notconstitute significant waste of resources. Finally, it can be shownthat the number of control messages per frame associated with ICTset construction scales linearly with the number of transmitters K

and exponentially with H, as O(KNH1

N1), where N is the average

number of single-hop neighbors.

3.2.2 Joint Routing-Scheduling-Power Control

In this section, we introduce a sub-optimal RSP algorithm thatsolves the problem within a given ICT set. The objective is three-fold: first, to determine the next hop subject to the following rout-ing constraints: (i) Thenext hop of any transmitter should be amongits neighbors and (ii) Length of the path from Sk to Dk shouldbe less than a pre-specified threshold, k, which relaxes the MHcriteria widely used in the MANET literature. Second, to decidewhich link is activated in which slot subject to the scheduling con-

straints: (iii) No simultaneous transmission/reception by a node inany slot (self-interference problem) and (iv) No multiple simul-taneous transmissions to the same receiver in any slot (common-receiver problem). Third, to specify the set of powers needed tosatisfy the power control problem constraints: (v) The signal-to-interference-and-noise-ratio (SINR) is greater than a pre-specifiedthreshold(power admissibility condition) and (vi) The peak powerconstraint per node (Pmax).

Next, we highlight the main features of the proposed algorithmfollowed by a detailed description of the interaction of various partsof the algorithm. The essence of RSP is to construct links andschedules that pack as many transmissions as possible in each slotwhileguarantee convergence of the continuous, iterative, distributed,power control (DPC) algorithm, introduced in [10], to the minimumpower vector in all slots:

Ptj (T+ 1) = min[Pmax,

SINRj(T)Ptj (T)] (1)

where Ptj is the power transmitted from node j to its next hopneighbor,SINRj is measured at the receiver of node j, is a min-imum requirement on the SINR for successful reception and T isthe iteration number. Thus, the routing and scheduling portions ofthe algorithm are search-based. This is attributed to their combi-natorial nature [8] and lack of a tractable mathematical structurethat lends itself to analytical optimization techniques. On the otherhand, DPC is an iterative algorithm that is known to converge ex-ponentially fast to the minimum power vector, if one exists. Hence,techniques for speeding up the routing and scheduling search pro-cess are sought. Towards this objective, we incorporate the fol-

lowing features. First, the routing search process commences withthe MH policy since minimizing the path length is a desirable cri-teria in the solution. If there are multiple MH paths, we resolvethe tie via random selection. The natural question that arises nextis: In what order should the routing policies be examined? Thesignificance of this question stems from the fact that neighborsof a source/intermediate relay differ in the length of the path onwhich they reside and the amount of interference they get exposedto. Thus, we argue that neighbors who satisfy the path length con-straint (ii), along with the loop-freedom constraint, should be or-dered according to their path lengths to destination, such that neigh-bor(s) on MH path(s) are examined first and neighbors on longerpaths are examined later. This ranking is instrumental in guidingthe search process throughout the routing policy space, and, hence,finding good sub-optimal solutions in a timely manner. Second, the

scheduling search process commences with the All Transmissionsin a Single Slot (ATSS) policy followed by policies that tend todistribute transmissions evenly among slots in the frame. The ra-tionale behind this choice is to pack as many transmissions as pos-sible in a slot in order to maximize the single-hop MAC throughputwhich constitutes an upper bound on the multi-hop throughput. Ifthis leads to empty slots in the frame, then the generated schedule,or part of it, could be repeated using next packets in the queuesready for transmission.

Figure 3 shows a flowchart that demonstrates the interaction ofthe routing, scheduling and power control portions of the algorithm.


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As indicated earlier, the algorithm starts with examining MH rout-ing and ATSS scheduling as directed by the setleader. Accordingly,all transmitters in the set send control information to the set leader,in a contention-free manner, that defines the set of links. This isessential to solve the self-interference and common receiver prob-lems. Accordingly, the set leader examines constraints (iii) and(iv) (i.e. scheduling constraints) for all slots in the frame. If oneor both constraints are violated, the scheduling algorithm defersconflicting transmissions to a future slot, using the heuristic in [7]

which examines the two constraints in sequence and defers userstransmissions to resolve conflicts. Otherwise, the algorithm pro-ceeds to the power control portion with a set of single-hop linksalong with their slot assignments. The DPC algorithm, executed bytransmitter-receiver pairs in each slot, examines their power admis-sibility judged by the convergence of (1). Notice that the operation

- Routing Policy

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- Transmission Powers

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Figure 3: Flowchart of the Intra-set RSP Algorithm

of DPC requires receivers to send their SINR measurements backto their respective transmitters at the end of each iteration. Thisis achieved using a separate feedback channel that can be used in

a contention-free manner since the feedback messages are consid-erably smaller than data packets. If all slots turn out to be poweradmissible, then the set leader announces the routing, schedulingand power control solutions. Otherwise, another routing and/orscheduling policy is examined for power admissibility. This givesrise to an important question: Which of the two policies, routingor scheduling, should be given precedence in the search process?Motivated by the observation that routing can circumvent the nega-tive impact of interference without degrading the MAC throughput,we give precedence to examining routing policies over schedulingpolicies. More specifically, for a scheduling policy under examina-

tion, all routing policies that satisfy constraints (i) and (ii) and areloop-free are examined until either a solution is found or anotherscheduling policy is examined. This choice is based on the premisethat routing could reduce interference, like scheduling, yet withoutsacrificing the MAC throughput, which constitutes an upper boundon the E2E throughput. Notice that a new scheduling policy is gen-erated from an existing one via deferring the link(s) with minimumSINR [7] in the last DPC iteration of violating slot(s) in an attemptto lower the level of interference. This heuristic enables the re-

maining links to converge to the minimum power vector quite fast.

3.2.3 Set Coordination

It is evident from section 3.2.1 that ICT sets could be overlap-ping, i.e. one or more transmitters may belong to multiple ICTs.Set overlapping gives rise to the so-called inter-set interferencewhich leads to coupling different ICT sets. Executing the RSP al-gorithm for these sets simultaneously is problematic since trans-mitter(s) in the overlapped regions would receive conflicting ordersfrom respective set leaders. This is equivalent to merging the sets,yet, the leaders of the original sets are trying to find solutions fortheir respective sets without any coordination. One way to circum-vent this hurdle is to enable the merged sets to somehow detect theoverlapping and then elect a new leader for the larger set who runsa single RSP algorithm. However, this is achieved at the expense of

two drawbacks: first, executing RSP for larger sets implies highercomputational overhead due to larger routing and scheduling policyspaces. Moreover, it could lead to merging a chain of ICT sets to asingle set that covers the entire network. Second, it contradicts oneof the fundamental premises in the paper that spatially far trans-mitters, whose mutual interference is negligible, should belong todifferent sets.

In this section, we embrace an alternative approach that avoidsset merging via the notion of set scheduling (or coordination) that iscarried out in the control sub-frame. Thus, the problem boils downto coordinating the execution of the RSP algorithm for differentICT sets such that: i) It is executed for overlapping ICT sets at dif-ferent times and ii) Executed simultaneously for non-overlappingICT sets. The problem of determining the minimum length set

schedule subject to the above two constraints is a combinatorialproblem that requires global information about set overlapping ata central controller. It can be mapped to a graph coloring problemvia constructing a conflict graph for ICT sets.

We limit our attention here to a sequential coordination schemethat satisfies the first constraint only. Adopting such coordinationscheme constitutes a simple first step towards the problem and is

justified by our focus on the proposed cross-layer framework andRSP algorithm as the main contributions. Simulation results pre-sented in the next section confirm the profound impact that set-based routing (RSP+sequential coordination) has on the networkthroughput. According to the proposed coordination scheme, theRSP algorithm is executed for all ICT sets sequentially, in an as-cending order of leader IDs, irrespective of their overlap. Dis-

tributed operation is achieved via exchanging leader IDs through-out the network with the aid of efficient information disseminationschemes, e.g. probabilistic flooding. Notice that a transmitter thatbelongs to multiple ICT sets determines it routing, scheduling andpower control decisions through the RSP execution of the set reach-ing its turn first. Afterwards, its solution is handled as a constraintduring the RSP execution of the other sets. This represents a keyfeature of this scheme which resolves inter-set interference with-out the need to merge overlapped sets. Finally, it can be shownthat the control overhead of sequential coordination grows expo-nentially with H since each set leader is responsible for flooding its


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set solution over H hops in order for the leaders of overlapped setsto utilize such information in solving their respective sets.

4. PERFORMANCE EVALUATION

4.1 Simulation SetupIn this paper, we conduct simulation experiments using the ns-2

simulator. We consider a uniform network topology where n = 64fixed nodes are placed across a square area of length 1000 meters.The square is split into 64 smaller squares where the location ofeach node is selected randomly within each of these squares. Eachnode is supported by an omni-directional antenna that radiates en-ergy according to an isotropic pattern and the path loss exponent = 4. The number of data slots per frame is set to d = 5 whereeach slot is of duration 6 msec. We primarily focus on fixed multi-hop wireless networks (e.g. mesh networks) and, hence, mobilityis not modelled. However, this assumption can be relaxed to theclass of low mobility MANETs where the link gain matrix changesover a time scale much larger than the time scale over which theproposed algorithm operates, namely a frame.

A source node generates packets of length 512 bytes accordingto a Poisson process with rate packets/sec. We assume that eachnode has a queue of arbitrarily large size since our objective here isto capture packet losses attributed to interference. The maximum

radio transmit power is set to 20 dBm which translates to a rangeof approximately 300m in the absence of interference. The SINRthreshold for successful reception at a receiver () is set to 5 dB.The receiver thermal noise power is assumed to be -90 dBm. Themaximum number of iterations in the DPC algorithm is set to 20.We assume three S-D pairs where source nodes are separated fromtheir respective destinations by approximately 1000 meters on theaverage in order to emphasize the role of interference contributedby intermediate nodes on multi-hop paths. Motivated by the com-putational complexity of solving DPC for large number of linksalong with the exponential growth of control overhead per framewith H, we limit H to small values (H = 2) in this set of experi-ments. The network load is varied via increasing the packet arrivalrate per source node () from 10 packets/sec to 360 packets/sec.Each simulation run is carried out for the duration of 900 seconds.

4.2 Reference SystemIn this section, we describe the reference system (REF) used as

a benchmark to gauge the performance gains achieved by jointlydesigning the MAC and routing algorithms. First, we assume thetransmission power is fixed at Pmax and there is no provision fordynamic adaptation. Second, we assume that the routing and mul-tiple access decisions are taken in isolation. A table-based routingprotocol is executed by each source/intermediate relay using theclassical link-based MH metric. Ties between multiple MH pathsare resolved via random selections. Once the routing decision ismade for all transmitters at the beginning of each frame, then itis the responsibility of the scheduling algorithm to resolve the con-tention among the constructed links. Hence, we adopt maximal slotscheduling proposed in [11] which attempts to create collision-freeschedules via satisfying the following constraints: i) A node is notallowed to simultaneously transmit and receive in the same slot, ii)A node is not allowed to receive from more than one transmitter inthe same slot and iii) A receiver should not be a neighbor of anyother transmitter.

4.3 Performance ResultsThe prime focus of this paper is to assess the throughput im-

provement achieved by the joint design of MAC and routing overthe reference system. Comparing the control overhead associatedwith the two systems requires a detailed simulation of the control

sub-frame which is out of the scope of this paper. The performancemetric used to compare the two systems is the E2E throughput de-fined as the long-run average number of data packets that reachtheir respective final destinations successfully per second.

In order to better understand the trade-offs involved, we exam-ine three routing policies. These policies are generated via widen-ing the scope of the routing policy space (through varying the pathlength constraint k). Recall that the routing search process in theRSP algorithm ranks MH paths in the highest rank and the rank de-

creases as the path length increases. Accordingly, the first policy,referred to as Highest Rank Paths Only (HRPO), limits search inthe routing policy space to neighbors residing on MH paths. Thispolicy may show performance gains only if there are multiple MHpaths between some S-D pairs, which is typical in many networkswith moderate connectivity. The second policy, referred to as Sec-ond Rank Paths Also (SRPA), widens the search scope to accom-modate MH paths in the highest rank along with longer path(s) inthe second rank. This policy trades path length for MAC through-put in an attempt to improve the E2E throughput. Finally, the thirdpolicy, referred to as All Rank Paths (ARP), is an extreme onethat considers neighbors on all possible paths between the sourceand destination as potential next hops, irrespective of their associ-ated path lengths. Accordingly, MH routing and ARP policies canbe viewed as the extremes. At one end of the spectrum, MH routing

attempts to minimizepath lengths irrespective of theMAC through-put. At the other end of the spectrum, the ARP policy attempts tomaximize the MAC throughput irrespective of path lengths. Sim-ulation results confirm that neither extreme constitutes a favorabledesign choice.

First, we compare the long-run average number of successfulsingle-hop transmissions per slot under the four schemes. The im-portance of this experiment stems from the fact that this parameterreflects the MAC Throughput. It can be easily noticed from Figure4 that the reference system yields the lowest MAC throughput dueto ignoring the negative impact MH routing may have on the MACperformance. On the contrary, the HRPO and SRPA policies showconsiderably higher MAC throughput. Notice that HRPO improvesthe MAC throughput over REF by a factor of 25% at heavy loads.It is crucial to notice that this is achieved while preserving the MHrouting criteria since the HRPO policy attempts to exploit the spa-tial separation of next hop nodes to pack as may transmissions aspossible in each slot. Moreover, it can be noticed that as longerpaths are considered, the SRPA policy improves the MAC through-put over REF by a factor of 38% at heavy loads. This is attributedto enlarging the routing search space which creates more room forspatially separating the paths of various S-D pairs. Under the ARPpolicy, the MAC throughput increases at low to moderate loads andthen starts to decrease under high loads. We attribute this phenom-ena to routing based solely on MAC throughput which could leadto forwarding packets to directions far from destination. This maylead to following arbitrarily long paths, as shown later, which im-poses higher load on the network and starts to have negative impacton the MAC throughput around 150 packets/sec/source node.

Next, we compare the long-run average path length under thefour policies as shown in Figure 5. This measure reflects the pricepaid for improving the MAC throughput. It is straightforward tonoticethat thereference systemyields thelowest average path lengthdue to adopting the MH routing criteria. Moreover, policies thatimprove the MAC throughput, namely HRPO and SRPA, have av-erage path lengths similar to or longer than the reference system.Finally, the ARP policy has the longest path length on the averageas expected. Figures 4 and 5 confirm that the interplay betweenMAC throughput and path length is what determines the net E2Ethroughput.


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erageNumberofSuccessfulTransmissionsperSlot

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Figure 4: Average Number of Successful Transmissions per

Slot for four Routing Policies

Figure 6 shows the E2E throughput performance under the fourpolicies. First, we notice that the reference system yields low per-formance due to ignoring the trade-off between MAC throughputand path length. Second, the HRPO and SRPA policies give thehighest E2E throughput due to improving the MAC throughput sub-

ject to a constraint on the path length. Third, the ARP policy yieldsthe worst throughput performance due to following spatially far,yet excessively long, paths from source to destination. Finally, wenotice that the HRPO policy outperforms REF by a factor of 50%at heavy loads and decreases to 35% at moderate loads. On theother hand, the SRPA outperforms the reference system by a fac-tor of 34% for moderate and heavy loads. Notice that although theSRPA policy is inferior to HRPO, it still outperforms the referencesystem. Referring to the relative performance of HRPO, SRPA andARP we observe that there is a turning point in the behavior of theRSP algorithm, that is directly related to the path length constraintand worth further investigation. Under HRPO and SRPA, whererouting is restricted to MH and slightly longer paths, there could beroom for overall performance improvement as demonstrated. Onthe other hand, as we approach the ARP policy (via relaxing thepath length constraint), improving the MAC throughput becomes

out weighed by the lengthy paths followed from source to destina-tion. Thus, we conclude that by appropriately limiting the scope ofthe routing policy space, the proposed cross-layer framework couldachieve significant performance improvement over MH routing.

5. CONCLUSIONSIn this paper we introduced a cross-layer multiple access and

routing algorithm for interference-limited wireless multi-hop net-works. Our main contribution is to incorporate interference intothe routing decision via the set-based routing framework that solvesthe problem for spatially close transmitters as opposed to the classi-cal link-based routing paradigm. Moreover, we introduced a novel

joint routing, scheduling and power control (RSP) algorithm thathandles intra-set interference. We conducted a simulation studythat compares the cross-layer MAC and routing framework to a

reference system where MAC and routing decisions are taken inisolation. Results exhibit performance improvement up to 50% fora variety of routing policies. Finally, this work opens room forexploring interference-aware on-demand routing protocols and de-veloping distributed minimum-time set coordination schemes.

6. REFERENCES[1] D. Bertsekas and R. Gallager, Data Networks. New

Jersy:Prentice-Hall Inc., 1987 (2nd Ed. 1992).

[2] K. Jain, J. Padhye, V. Padmanabhan and L. Qiu, Impact ofInterference on Multi-hop Wireless Network Performance,

Proc. ACM MOBICOM, Sept. 2003.

[3] R. Bhatia et al. On Power Efficient Communication overMulti-hop Wireless Networks: Joint, Routing, Schedulingand Power Control, IEEE INFOCOM, March 2004.

[4] B. Radunovic et al. Joint Scheduling, Power Control andRouting in Symmetric, One-dimensional Multi-hop WirelessNetworks, WiOpt Workshop, March 2003.

[5] R. Cruz and A. Santhanam Optimal Routing, LinkScheduling and Power Control in Multi-hop Wireless

Networks, Proc. IEEE INFOCOM, April 2003.[6] T. Rappaport, Wireless Communications Principles and

Practice. New Jersy:Prentice-Hall Inc., 1996 (2nd Ed. 2002).

[7] T. ElBatt and A. Ephremides Joint Scheduling and PowerControl for Wireless Ad hoc Networks, IEEE Transactionson Wireless Communications, vol. 3,no. 1, Jan. 2004.

[8] T. ElBatt and T. Andersen A Cross-layer Framework forMultiple Access and Routing Design in Wireless Multi-hopNetworks, under submission, Dec. 2005.

[9] D. Baker and A. Ephremides The ArchitecturalOrganization of a Mobile Radio Network via a DistributedAlgorithm, IEEE Transactions on Communications, vol. 29,No. 11, Nov. 1981.

[10] G. Foschini and Z. Miljanic A Simple Distributed

Autonomous Power Control Algorithm and itsConvergence, IEEE Transactions on Vehicular Technology,vol. 42, No. 4, Nov. 1993.

[11] I. Cidon and M. Sidi Distributed Assignment Algorithmsfor Multihop Packet Radio Networks, IEEE Transactions onComputers, vol. 38, No. 10, pp. 1353-1361, Oct. 1989.

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Figure 6: Average E2E Throughput for four Routing Policies

Documents

Interference-Aware Routing in Wireless Multihop Networks