Cross-Layer Design Gains in MANETs Ammar Alhosainy and Thomas Kunz

Systems and Computer Engineering Carleton University

Ottawa, Canada {amammar, tkunz@sce.carleton.ca}

Li Li and Philip J. Vigneron CRC Canada

Ottawa, Canada {li.li, phil.vigneron@crc.ca}

Abstract—In this paper we propose and implement cross-layer network design models with three different MAC protocols: CSMA-CA, CSMA-CA with RTS/CTS, and ALOHA. These network models are used to study the behavior of Cross-Layer Designs (CLDs) in case of dynamic network topologies with different transmission ranges and network sizes. A similar network model but with a conventional Oblivious Layered Design (OLD) is also implemented to compare the performance of the cross-layer models and to show the impact and potential benefit of cross-layering. Our results show that the CLD gain is comparable to the gain that results from selecting a better MAC protocol. The results also show that CLD, unlike OLD, fully utilizes the link capacities and its gain is proportional to the average number of the session hops.

Index Terms— Multihop wireless network, cross-layer design, network utility maximization, CSMA-CA, mobile ad-hoc networks.

I. INTRODUCTION AND RELATED WORK A Mobile Ad-hoc Network (MANET) is an infrastructure-

less network of dynamic nodes communicating via wireless links in a multi-hop fashion. Efficiently using the network resources of such networks is challenging. Communicating via shared wireless links raises a contention problem (typically addressed at the MAC layer). The absence of a fixed infrastructure and centralized administration add a congestion problem where flows or data sessions are typically routed through the same central part of the network. Multi-hop transmissions cause flows not only to interfere with each other, but also with themselves. Finally, node mobility requires adaptive solutions to handle the constant change of the network topology and the node’s local contention neighborhood.

Cross-layer network design provides vertical coordination between network layers. Cross-layering along with a distributed horizontal coordination system between nodes provides flexibility and efficiency to the network that compensates for the absence of a centralized administration. The key idea here is that rather than individual protocol layers solving one or a subset of the stated problems, potentially conflicting with the solution of other problems at other layers, all layers jointly address these problems to derive a consistent, optimal operation of the network.

Much work has been done on cross-layer design showing its advantages over traditional layered designs, referred to as Oblivious Layered Design or OLD in the following, in enhancing TCP over wireless networks [12, 13], congestion control [16], power control [15], or adapting different layer

parameters for a specific application [11]. Most of the approaches interchange signals and information between network layers, not considering the impact of cross-layering on network modularity [14] and the fact that it violates the concept of designing protocols in isolation. Furthermore, these papers typically show the advantage of CLD vs. OLD for very specific simulated scenarios. In this paper, we therefore focus on CLD approaches that can allow decisions to be made separately at different layers. Furthermore, as CLD requires the exchange of information among nodes to jointly optimize their behavior, CLD approaches incur costs. In the long run, we are interested in quantifying both costs and benefits of a suitable CLD approach, and in this paper start by providing a more rigorous evaluation of the benefits of such an approach in MANETs.

Distributed cross-layer optimization algorithms are proposed in [1]-[3] to find the optimum values of different parameters at the MAC and the Transport layers that jointly solve the contention and congestion control problem in the wireless network. The Network Utility Maximization (NUM) framework [5, 6] was employed to maximize the network utilities and achieve different types of fairness among the users. This work was extended in [7, 8] to include the network layer parameters in the optimization process. In this paper, we are exploring whether cross-layering with distributed coordination among MANET nodes is indeed advantageous in solving the contention and congestion control problems as compared to an OLD, and, if so, how much performance gains to expect under what conditions.

Lee et al. [4] developed a distributed optimization algorithm that can find the optimum link rates along with the optimum medium access attempt probabilities of an ALOHA MAC layer in mobile ad-hoc wireless networks meeting specific fairness criteria through a utility function. The algorithm is distributed: every node is communicating with at most its two-hop neighbors to exchange topology information and parameters. The algorithm showed robustness and stability against a high rate of packet loss and inaccurate topology information when compared to other similar algorithms [1].

The algorithm in [4] can withstand high rates of update message loss due to the fact that it only needs to exchange price values that represent the weights of the link rates. The authors translated all parameter values that need to be exchanged between network nodes into link rate prices that can be used in the optimization process vertically between layers and horizontally between nodes. Yu et al. [3] extended the algorithm in [4] to include the transport layer in the optimization process. They divided the link prices into smaller

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values to separately represent each session in each link so that the algorithm can optimize the end-to-end session rates instead of the link rates based on a specific fairness criterion. Yet, this algorithm has neither been tested with any other MAC protocols nor evaluated against a comparable oblivious layer design to explore the potential CLD benefit.

The medium access attempt probability used in the ALOHA protocol represents the transmission opportunity given to the node. This transmission opportunity is determined by the algorithm based on the need for each link to offer enough capacity to carry, at a minimum, all sessions passing through it. This transmission opportunity can be controlled in the IEEE 802.11 MAC protocol by tuning the contention window [9] to achieve node fairness, but in the optimization process it will be used to optimize end-to-end session rates subject to specific fairness criteria.

In this paper we implemented the CLD employing the ALOHA MAC protocol as discussed in [3]. We also designed and implemented alternative MAC protocols within this framework, CSMA-CA with RTS/CTS and basic access mechanisms. The CSMA-CA protocols with the two different access mechanisms are implemented using Bianchi’s models [10]. Finally, we implemented a simulation that models a traditional OLD employing the same protocols at the MAC, network, and transport layers. Using extensive simulations, we then study the network performance varying transmission range and network size. The results show that for all MAC protocols, CLD provides up to twice the network capacity provided by the equivalent OLD approach. The performance gain is particularly pronounced in scenarios where flows/data sessions extend over multiple hops.

The rest of the paper is organized as follows. The cross-layer and oblivious layer network models with a CSMA-CA MAC protocol are discussed in Section II. Section III presents a detailed example with numerical results as well as a discussion about the performance gain of CLD vs. different network parameters. The paper finishes with our conclusions and future work in Section IV.

II. NETWORK MODEL As our interest is concentrated on comparing CLD and

OLD, the absolute performance results are of less importance. We developed an abstract model that can calculate optimistic time-independent utilities for both designs. This means that the topology and the feedback information are known instantaneously and also that the algorithm converges to the optimum immediately. Consequently we ignore the costs of informing neighbors via the exchange of control messages, the impact of inaccurate knowledge, and the delay in acquiring information about the local neighborhood via periodic HELLO messages, for example. This optimistic model helps in finding the upper boundary of the network gain and reveals the actual power of the CLD.

A. Cross-layer network model with CSMA-CA MAC protocol Consider a single channel wireless network modeled as

unidirected graph = ( , ) with number of nodes and

logical links, each link has a fixed physical capacity of bps, and sources, each transmitting at a source rate of bps. Each source emits one flow, using a fixed set ( ) of links in its path, and has a utility function ( ). Each link can be shared by a set ( ) of sources.

We start from the NUM, max ∑ ( ) (1) ∑ ∈ ( ) ≤ where is the link capacity.

With the CSMA-CA MAC protocol, the link capacities will be derived from Bianchi’s model [10]. This model will provide us with the saturation throughput as a function of different parameters as follows, = ( )( ) ( )where: ≡ Probability that there is at least one transmission in

the considered time slot. ≡ Probability of successful transmission. ( ) ≡ Average message payload. ≡ The average time the channel is sensed busy due to a successful transmission. ≡ The average time the channel is sensed busy during a collision. ≡ Slot time size.

The saturation throughput as a function of the number of

contending nodes represents the percentage of the useful transmission opportunities given to the nodes. In saturation condition, each node always has a packet available for transmission, so changing the nodes’ fairness via contention window tuning [9] will not affect the resultant aggregate throughput. Given the transmission opportunity of each node that is derived from the optimization algorithm we can calculate the link capacity using the following equation: = ∗ ∗where is the physical capacity of the link, is the transmission opportunity of the outgoing link , and is the percentage of useful transmission opportunity given to the transmitter .

The utility maximization problem will be divided into three sub-problems connected to each other by the Lagrange parameter (the prices). Two subproblems, addressing the transport layer to optimize the total sessions rate and the link utility fraction of each session, respectively, will be derived by following the same steps as in [3], ( ( ) − ) ∈ (∑ ( )∈ ( ) )

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where is the fraction of the rate on link that is contributed by source session , Ω = { ∑ ∈ = 1, 0 ≤ ≤ 1 } is the projection operator for , is the Lagrange parameter for sessions on link , = ∑ ∈ ( ) , and ( ) is the set of links that uses.

The third maximization sub-problem addresses the MAC layer. It will be derived using the price analogy. Assume that = ∑ ∈ ( ) is the link price and = ∑ ∈ ( ) is the total price of the outgoing links from node , the node transmission opportunity will be as the following, = ∑ ∈ ( ) , ∉ ( )where ( ) is the set of nodes in the same contention area with node . The link transmission opportunity will be given by, = ∑ ∈ ( ) , ∈ ( )

The interpretation of formula (7) is simple, the price is the network benefit, and the denominator represents the sum of the network benefit if all the nodes in the same contention area transmit at the same time. However, as this cannot happen in a wireless network, the network gives more chance (i.e. higher transmission opportunity) to the node that can pay more (i.e. with higher price ). This interpretation applies to formula (8) too. The optimization process in each node can be divided into different functions as shown in Fig. 1.

B. Oblivious layered network model

The only difference in our OLD design compared with the above cross-layered model is the absence of any prices being exchanged between the network layers. Each layer makes its own decisions based on the local information seen by the layer. The transmission opportunity for the nodes will be equally divided over the number of active nodes in the same contention area. We assume that the transport layer at the session source gets an instantaneous feedback about the minimum link capacity in the session paths and can adjust the session rate accordingly.

III. SIMULATIONS AND NUMERICAL RESULTS We designed and implemented a simulator using Matlab.

During the simulation, we assume that the topology information is well known all the time. The random waypoint mobility model is used in the dynamic topology test. After the topology changes, sessions may be rerouted or dropped. The routing problem is not the focus of this paper; the rerouting events are done instantly based on a shortest hop routing protocol. The maximum physical capacity is 1. The utility function that will be used is the log function that achieves proportional fairness among the network sessions. In comparisons we use the sum of the utilities values as our performance metric, a higher sum reflects maximized rates combined with proportional fairness among different sources.

Fig. 1. CLD Flowchart per Node

A. The CSMA-CA model The values of the parameters used to obtain the numerical result with the CSMA-CA Bianchi model [10] are summarized in Table 1.

TABLE 1. CSMA-CA MODEL PARAMETERS

Average message payload 8184 bits Tc (RTS/CTS) 417 bits Ts (RTS/CTS) 9569 bits

Tc (basic) 8713 bits Ts (basic) 8982 bits

50 μs Bit rate 1 Mbit/s

Figure 2 shows the throughput values as a function of the

number of stations for the two access mechanisms, assuming a physical link capacity of 1.

B. Detailed Example For the wireless network shown in Fig. 3, four sessions are

generated according to the paths indicated in Table 2. Using both CLD and OLD models, the link capacities along with their utilized percentages for the three MAC protocols are presented in Table 3. The results show that the network operation derived by CLD for any of the three MAC protocols efficiently assigns

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transmission opportunities to the links so that the resulting capacities are fully utilized by the sessions. In contrast, in the OLD, inefficient transmission opportunity assignments lead to link utilizations of 57% on average, only the bottleneck links are fully utilized. So even though the sum of link capacities is higher, the network utilization is lower as the flows end up with very different rates, which the objective function (aiming for fair sharing) penalizes. Table 4 shows the session rates and the aggregate log rates of the four sessions, which are the objective function value. For each MAC protocol, the solution derived via the cross-layered design has higher aggregate session rate and aggregate utility, indicating that the solution is indeed better (i.e., results in higher user utility).

Fig. 2. CSMA-CA Throughput vs. Number of Nodes.

Fig. 3. Wireless Network.

TABLE 2. SESSION PATHS

Session Path S0 B, C, D, F, I S1 B, C, E, F, I S2 A, C, D, G, H S3 G, D, C, A

C. Varying Network Parameters The benefit of a cross-layered design is further explored in

this section varying a number of network parameters and when nodes move according to the random waypoint mobility model. Note that, given the above stated assumptions, mobility does not directly impact the results, as we assume that any topology changes are immediately reflected in CLD and OLD optimizations. However, having nodes move around allows us to generate many different scenarios from the same initial

configuration and paves the way for future work discussed later. In a simulation of 100s, we take a network snapshot every 0.2s, resulting in 500 topologies, allowing us to derive statistically meaningful results. Table 5 lists the default simulation parameter values.

TABLE 3. THE LINK CAPACITIES WITH UTILIZATIONS FOR THE THREE MODELS, CROSS-LAYER AND OBLIVIOUS LAYER DESIGNS

CSMA-CA protocol with RTS/CTS mechanism

Link Cross-layer design Oblivious layer design

Capacity Utilization (%) Capacity Utilization (%) C-A 0.0687 100 0.0299 100 A-C 0.0609 100 0.1668 18 B-C 0.1217 100 0.1668 36 D-C 0.0687 100 0.0398 75 C-D 0.1212 100 0.0597 100 G-D 0.0687 100 0.1040 29 C-E 0.0614 100 0.0299 100 D-F 0.0604 100 0.0398 75 E-F 0.0614 100 0.1392 21 D-G 0.0609 100 0.0398 75 G-H 0.0609 100 0.1040 29 F-I 0.1217 100 0.1668 36

Total 0.9366 100% 1.0865 57.82% CSMA-CA protocol with basic mechanism

C-A 0.0655 100 0.0281 100 A-C 0.0581 100 0.1620 17 B-C 0.1161 100 0.1620 35 D-C 0.0655 100 0.0374 75 C-D 0.1156 100 0.0562 100 G-D 0.0655 100 0.1029 27 C-E 0.0586 100 0.0281 100 D-F 0.0575 100 0.0374 75 E-F 0.0586 100 0.1330 21 D-G 0.0581 100 0.0374 75 G-H 0.0581 100 0.1029 27 F-I 0.1161 100 0.1620 35

Total 0.8933 100% 1.0494 57.32% ALOHA protocol

C-A 0.0537 100 0.0286 76 A-C 0.0520 100 0.0980 19 B-C 0.1137 100 0.0980 43 D-C 0.0537 100 0.0218 100 C-D 0.1067 100 0.0367 100 G-D 0.0537 100 0.0735 30 C-E 0.0590 100 0.0238 100 D-F 0.0547 100 0.0317 58 E-F 0.0590 100 0.1143 21 D-G 0.0520 100 0.0357 52 G-H 0.0520 100 0.1000 18 F-I 0.1137 100 0.2000 21

Total 0.8239 100% 0.8621 53.14% Figure 4 shows the impact of increasing the number of

nodes as well as the number of sessions on the aggregate network utility in a fixed area size. The number of sessions equals half the number of nodes. For all MAC protocols, the network utility of the CLD solution exceeds the corresponding network utility of the OLD solution, with the gap widening as the network contains more nodes and sessions. Consistent with the results in Fig. 2, the CSMA-CA protocol with RTS/CTS provides higher network utility than the basic CSMA-CA

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TABLE 4. SESSION RATES AND AGGREGATE LOG RATES

S0 S1 S2 S3 AGGREGATE LOG RATES

CSMA-CA RTS/CTS

CLD 0.0604 0.0614 0.0609 0.0687 -11.0746 OLC 0.0299 0.0299 0.0299 0.0299 -14.0444

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CLD 0.0575 0.0586 0.0581 0.0655 -11.2637 OLC 0.0281 0.0281 0.0281 0.0281 -14.2900

ALOHA CLD 0.0547 0.0590 0.0520 0.0537 -11.6161 OLD 0.0184 0.0238 0.0184 0.0218 -15.5593

TABLE 5. DEFAULT SIMULATION PARAMETERS VALUES

Simulation period 100 s Area 200 x 200 m2 Transmission range 100 m Node speed 4 m/s Number of nodes 20 Number of sessions 10 Node density 0.0005 node/m2 Session density 0.5 session/node

Table 6 shows the 95% confidence interval of the aggregate

utilities for 500 snapshots of a dynamic network topology with 70 nodes and 35 sessions. We can see that all the performance differences between CLD and OLD, as well as the different MAC protocols, are statistically significant at the 95% confidence level.

Fig. 4. Average Network Utility vs. Number of Nodes

TABLE 6. THE 95% CONFIDENCE INTERVAL

CSMA-CA RTS/CTS CSMA-CA Basic ALOHA CLD [-144.27, -144.64] [-153.25, -153.66] [-153.75, -154.16] OLD [-159.24, -160.10] [-168.27, -169.13] [-171.24, -172.07]

Figure 5 shows the average network utility vs. the transmissions range of the nodes. The figure also shows the average number of per-session hops and the percentage of average number of active sessions which have a complete valid path. We can see that varying the transmission range parameter reveals the two factors affecting the network utility: the number of active sessions and the average number of per-session hops. As indicated in Table 5, the network area square length is 200m. The deployed 20 nodes are scattered in the entire area, which leads to disconnected nodes and a very low number of active sessions in case of a short transmission range, as shown in Fig. 5. Increasing the transmission range tends to increase the number of active sessions in the network.

Fig. 5. Average Network Utility vs. Node Transmission Range

Once the network is connected, further increases in the transmission range primarily cause the lengths of any path to decrease, until, in the limiting case, the network is fully connected and therefore the length of each path is one hop. The network utility steeply decreases with the increase in the number of active sessions (as it sums up the utility, a negative value, for all active flows), then when all sessions are active, the trend in network utility achievable with CLD respectively OLD changes. At that point, the CLD session rates and utilities keep decreasing with a very small slope affected by the number of active sessions. But the OLD session rates and therefore the network utility start to increase due to the decreasing average number of per-session hops (reducing self-interference of flows). This increase stops as soon as the number of per-

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session hops converges to one. And unlike the CSMA-CA protocols, the absence of a collision avoidance mechanism in the ALOHA MAC protocol results in performance degradation when the number of active nodes in the same collision area increases (i.e. increasing the transmission range). For that reason, the enhancement due to the reduction in the self-interference is not as good as the CSMA-CA protocols.

Figure 6 shows the physical capacity gain achieved by a CLD network. We measure that gain by increasing the link capacities in a network using OLD until such a network with increased link capacities achieves the same average network utility. As the nominal link capacity is 1, a gain of 1 implies that an OLD network would require twice the link bandwidth to achieve the same network utility. The physical capacity gain decreases when the number of hops decreases and converges to a certain small value when all the sessions in the network become single-hop sessions. The reason is that the major advantage of the CLD is apparent only with multi-hop sessions: rather than assigning links (or nodes) equal transmission opportunities, as fair MAC protocols strive to do in a distributed fashion, the CLD solution allows to better match the link capacities to the session rates, as shown above, in particular in Table 3.

Fig. 6. Physical capacity gain vs. Node Transmission Range

In case of multihop transmissions, the superiority of the CSMA-CA MAC over the ALOHA protocol is clear. In case of single hop sessions, OLD can also provide relatively high rates, as it also distributes the transmission opportunities fairly between outgoing links, leading to a good match between link capacities and session rates when striving for fairness among sessions. That result is true as we generate sessions randomly.

If traffic patterns had clearly discernible patterns (such as one-to-all or many-to-one as in the case of Internet gateways), the OLD would suffer even in single-hop scenarios as the central node, competing with transmission opportunities with all other nodes, would not gain the increase it needs to service, in a fair manner, all its sessions. Balancing this gain are the required protocol messages to achieve cross-layer optimization. The number of such messages increases with the number of per-sessions hops, as more nodes contribute in each session.

Figure 7 shows the physical capacity gain of the best MAC performance, which is CSMA-CA with RTS/CTS mechanism protocol, relative to CSMA-CA with Basic access mechanism and ALOHA. The physical capacity gain of the CSMA-CA with RTS/CTS relative to the one with basic access mechanism is nearly constant, about 0.1 (or 10%), as it is limited to the throughput difference based on the number of active nodes contending for media access, which are less than 20 nodes. The physical capacity gain of CSMA-CA with RTS/CTS relative to ALOHA reveals the performance degradation of the ALOHA protocol when increasing the transmissions range. As discussed, in this case the sessions travel over fewer hops and the CLD gain reduces.

Fig. 7. Relative physical capacity gain vs. Node Transmission Range

Obviously the added overhead for the optimization process, in particular the exchange of price update messages, will consume part of the physical capacity gain. Though for a complete evaluation of this cost, we also need to consider that the price update messages, exchanged between nodes, can also be exploited for/replace other messages, such as periodic HELLO messages necessary for neighbor discovery. Also, the CLD approach presented here may not be optimized by reducing the number of messages exchanged: we are currently exploring whether updating price information less frequently will impact convergence speed and derived network utility. Therefore, accounting for the costs of the studied CLD approach is a topic of future work. However, we note that the benefits of CLD are quite significant, in particular for shorter transmission ranges (i.e., for flows that extend over multiple

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hops). Whether CLD will outperform OLD for shorter flows, once all the costs are accounted for as well, is more questionable.

IV. CONCLUSIONS AND FUTURE WORK We propose and implement network utility maximization

models that optimize the medium access probabilities at the MAC layer jointly with the end-to-end source rates at the transport layer with three different MAC protocols: ALOHA, basic CSMA-CA, and CSMA-CA with RTS/CTS. We then evaluated the performance benefit of CLD as compared to OLD under different network scenarios. The results indicate the superiority of the CLD in dealing with multi-hop network transmissions in the way of medium access opportunity assignments and link capacity utilization, with consistent gains for all three MAC protocols studied. The CLD physical capacity gain is roughly proportional to the average number of session hops. In case of single hop transmissions, the CLD gain reaches its lowest value, assuming a random traffic pattern. The results also explore the relative importance of different MAC protocols and their performance gain with CLD. Overall, the results show that the gains a CLD scheme can achieve are comparable to the gains that result from the choice of a better MAC protocol. Comparing Figures 6 and 7, CLD has the highest impact when sessions travel over relatively longer paths, selecting a good MAC protocol results in the biggest gains when session paths are short.

In future work, we will include the network layer parameters in the optimization algorithm and more systematically explore the impact of specific traffic patterns, as exist for example in the presence of Internet gateways. We will also explore the CLD performance gain with more realistic time-dependent network models to reflect the effect of mobility and the impact of inaccurate topology information. We will further quantify the added overhead associated with this gain as a function of the average number of per-sessions hops. A fair evaluation of the network gain vs. added overhead would necessitate a more complete study of the costs as well as the benefits from exchanging price update messages (reduction in neighborhood discovery overhead, for example). Finally, TDMA-based MAC models will also be explored.

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