Optimal Link Scheduling for Multimedia QoS Support in Wireless Mesh ...

Optimal Link Scheduling for Multimedia QoS Support in Wireless Mesh Network Tamer Abdelkader, Sagar Naik, Amiya Nayak

Abstract— Supporting multimedia quality of service (QoS) is a necessary and critical requirement for next generation wireless networks. Wireless Mesh Networking is envisioned as an economically viable paradigm and a promising technology for supporting multimedia QoS. The non-mobile mesh routers with the capability of having less-constrained transmission power are the distinct characteristics for this type of wireless networks. Exploiting these capabilities provides better solutions for allocating network resources such as bandwidth, and supports satisfying QoS requirements. Using these characteristics, we propose a QoS-aware link scheduling scheme for wireless mesh networks (WMNs). The scheme allocates time slots and transmission power to the network nodes in a way that maximizes the spatial reuse of the network bandwidth. In this paper, we study two cases: a mesh network with battery-powered devices and a network with outletpowered devices. Using computer simulations, we show that the QoS constraints for the different traffic flows can be met, for the two cases, in addition to maximizing the network throughput.

keywords – Wireless mesh networks, link scheduling, power allocation.

I. I NTRODUCTION With the recent advances in wireless and mobile communications, supporting multimedia Quality of Service (QoS) becomes a necessary while achievable requirement in wireless networks. Next generation wireless networks are envisioned to support a mix of multimedia applications with diverse QoS requirements over a scarce and shared wireless channel and with power-constrained devices. Recent research show a fair success in addressing these problems in the different protocol layers: The physical layer [1], [2]; the data link layer [2], [3]; the internet protocol layer (IP) [4], [5]; and the application layer [6]. Supporting such traffic requires providing fair and bounded-delay channel access among contending flows. Wireless mesh networks (WMNs) are a promising technology for providing wireless broadband services. A WMN is a network of fixed routers connected by wireless links, as illustrated in figure 1, which plays mainly the role of a backbone network and provides its mesh clients with an access to the Internet or other wireline backbones. The mesh clients represent other networks such as cellular, IEEE 802.11, IEEE 802.15, and sensor networks. Hence, WMNs offer a wide scope of applications including, but not limited

to, wireline networking services extension to rural areas, broadband Internet access, and enterprise-scale wireless backbone. These applications differ widely in their QoS requirements. The main service of the wireless mesh backbone is to carry the traffic of the access networks and route it to their destinations. Our objective in this paper is to propose a QoS-aware link scheduling with hop-by-hop routing framework for wireless mesh backbones, exploiting their unique characteristics. A wireless mesh backbone is different from the Internet backbone in the sense that a transmission on a wireless link is interfered by the near transmissions. This interference affects the quality of transmission and causes packet loss. In addition, all the mesh routers are similar in functionalities and capabilities. On the other hand, wireless mesh backbones are similar in architecture to wireless ad hoc networks in the sense that they are infrastructure-less networks with shared wireless links. However, the mesh routers are fixed and need not be battery-powered. In addition, mesh routers carry the traffic aggregated from the networks and devices connected to them, which results in different patterns from those of ad hoc networks, connecting only mobile users.

Tamer Abdelkader is with Faculty of Electrical & Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada.N2L3G1. email:

[email protected]

Fig. 1.

Wireless Mesh Network Architecture

Sagar Naik is with Faculty of Electrical & Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada.N2L3G1. email:

[email protected] Amiya Nayak is with the School of Information Technology & Engineering, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5. email:

[email protected]

In a wireless network with a time division multiple access (TDMA) medium access control (MAC) protocol, time slots are assigned to the links sharing a single wireless channel,

and their source nodes have packets ready to be sent. This assignment is affected by the number of available links, their weights (priorities), and the channel condition. In addition, the assignment affects the delay of the link packets and the total network throughput. We refer to the assignment of time slots to the network links as link scheduling. Our goal in this paper is to find the optimal link schedule that provides QoS guarantees in terms of bandwidth and signal-to-interference and noise ratio (SIN R) to the traffic in the wireless mesh backbone. The traffic from the access network is aggregated in the mesh router connected to it, based on the service class [7], and then routed to its destination. Each service class has a unique bandwidth requirement to be satisfied, and a minimum SIN R to be met at each link. Our main contribution in this paper is a scheme for QoSaware link scheduling in the wireless mesh backbone. Our scheme is based on a TDMA MAC protocol. Our approach for QoS provisioning is to allocate the transmission power of each mesh router optimally in order to maximize the number of simultaneous transmissions in each time slot while meeting both bandwidth and SIN R constraints. Power control plays an important role in utilizing the bandwidth efficiently. If minimum power is allocated for each link, the consequent decrease in interference increases the probability of having more links working simultaneously. However, this may lead to a poor SIN R which may render the transmission unsuccessful. On the other hand, maximum transmit power leads to higher interference, which limits the number of simultaneous transmissions, leading to inefficient wireless bandwidth utilization. Therefore, an optimal power allocation is required to realize QoS provisioning with efficient resource allocation. The rest of the paper is organized as follows. Section II presents an overview of the related work. Section III describes the system model. The QoS-aware link scheduling scheme is presented in Section IV. The simulation results and performance comparisons are discussed in Section V. Finally, we state our conclusions in section VI. II. R ELATED W ORK Link scheduling is addressed in many research work. Using the protocol interference model [8], and assuming a limited interference range, the work in [9], [10], [11] solves the link scheduling problem with graph coloring. Power allocation is also investigated in several research work related to scheduling and routing problems. In [18], maximum power allocation is used to achieve an opportunistic routing, which mainly addresses the link quality problem without any QoS considerations. In [19], an analytical model to adjust the transmission power for slotted ALOHA packet radio networks is presented for reducing interference. Three different methods for allocating power are studied. The first one is the shortesthop allocation, in which the power is allocated just to reach the closest neighbor (shortest hop). The second method allocates the power to reach the longest hop within the transmission range, and the third uses the maximum power. It is shown that the second method results in minimum interference.

In [20], an interference-aware routing algorithm and a centralized TDMA scheduling scheme is introduced. The work aims at achieving high channel utilization by minimizing interference. However, it does not address QoS issues in the scheduling scheme. In [21], the authors propose a protocol to solve the link scheduling problem. The paper focuses on maximizing network throughput while achieving an acceptable SIN R. Power-saving algorithms for battery-powered devices are proposed in many work [12], [13], [14], [15], [16], [17]. In [12], [13], minimum power sufficient for a successful transmission is allocated to transmitting nodes. In [14], [15], [16], throughput is maximized by allowing for spacial reuse of the bandwidth at the cost on increasing power. In [17], a powersaving link scheduling protocol is proposed by minimizing the interference of the surrounding transmissions. In this paper, we study the two cases: battery-powered (highly-constrained) routers and the less-constrained outletpowered routers. We propose an optimal link scheduling protocol with power allocation for the wireless mesh network. In addition, we perform a time complexity analysis on the problem solution. III. S YSTEM M ODEL We consider a network of N routers connected to each other through wireless links. The routers are deployed in a grid topology. The locations of sources and destinations are randomly distributed. Each node (router) knows its location and can adjust its transmission power to any value between zero and a maximum power level Pmax . We assume the availability of line-of-sight (LOS) transmission in all backbone links (between mesh routers) and also in all access links (between mesh routers and mesh clients). Since all the backbone and the access links are static, it is reasonable to assume a free space path loss model. All the backbone wireless routers transceivers are assumed to work in half-duplex manner (i.e. they cannot send and receive simultaneously). Like in all other related works, half-duplex operation is assumed to prevent self-interference, which has been shown to cause severe degradations in network throughput [22]. We consider a centralized TDMA MAC protocol within the network. A timely-slotted channel has an advantage over the traditional carrier sense multiple access (CSMA) technique in terms of throughput and fairness making it suitable for QoSaware networks [23], [24]. The network controller facilitates the centralized control. The exchange of control information is done by the direct communication between the routers and the controller for specific or common control messages. However, for data transmission, all the routers communicate directly with each other in a peer-to-peer fashion. In the TDMA-based MAC, time is partitioned into fixed length frames. Each frame contains a beacon period in addition to the contention-free period, which is partitioned into time slots with equal duration. The controller allocates the time slots and the transmission power for each wireless router via signaling

one frame, and Wls is the weight of link l in slot s. The weight parameter is used to prioritize links, e.g. links whose source nodes have delay-sensitive packets, or a buffer which is about to be full can be assigned higher weights.

messages broadcast in beacon periods. We assume that the controller is aware of its own location and the location of each wireless router in the network using location discovery schemes [25], [26]. IV. Q O S- AWARE L INK S CHEDULING F RAMEWORK Our objective is to obtain the optimal time slot and power assignment for each link that maximizes the channel utilization and meets the bandwidth requirement and the SIN R constraints for each link. A link l is the connection between two nodes i and j, where i is the transmitter and j is the receiver. We use the notation l and {i, j} interchangeably to represent a link, where the latter is used when the terminal nodes of the link are involved in an operation. The path gain1 for link l = {i,j} is modeled, as in [27], by 2 c 1 (1) G{i,j} = Gi Gj 4πf d2ij where Gi and Gj are the antenna gains at i and j respectively, c is the speed of light, f is the carrier frequency, and dij is the distance between the two nodes i and j. Let N denote the set of all transmitting nodes in the network. The achieved SIN R of an active link l = {i, j} is given by SIN Rl =

Pi G{i,j} Pk G{k,j} + n0

•

l∈L

Pls ), ∀s ∈ S

Z2.2 : max(−

Note that a minimization problem is the negative of a maximization problem. The problem has six constraints: • The minimum bandwidth requirement of each link, l, represented in number of slots per one frame, is Bl S

l∈L

where L is the set of links whose source nodes are ready to transmit in slot s, S is the set of all slots in 1 It is usually referred to as path loss in literature, with the antenna gains removed. 2 Also referred to as white noise, because it is found at all the frequency spectrum.

Al ≥ Bl ; ∀l ∈ L

(7)

s=1 •

k∈N,k=i

Each link l has a bandwidth requirement Bl which is the number of time slots required for that link in one frame. We set two objectives in this work: • Z1 : maximize the number of simultaneously active links in one slot. Wls Als , ∀s ∈ S (4) Z1 =

(6)

l∈L

(2)

where Pi and Pk are the transmission powers of nodes i and k respectively, G{i,j} and G{k,j} are the path gains of links {i, j} and {k, j} respectively, and no is the thermal2 noise at the receiver, j. This formula represents the effect of all the other transmitting nodes in the network, k, on the link {i, j} A link l , at any time slot s, has two states:• active: if there is an ongoing transmission on that link at that time slot. • inactive: if there is no transmission on that link at that time slot. We define Als as a binary variable representing the state of each link, where 1, if link l is active at time slot s (3) Als = 0, otherwise.

Z2 : maximize the number of simultaneously active links in one slot, while minimize the transmission power at each transmitter node. (5) Z2.1 : max Wls Als , ∀s ∈ S

The minimum SIN R requirement for each active link,l, in time slot,s, is hl . SIN Rl =

Al Pi G{i,j} ≥ hl Aw Pk G{k,j} + n0

(8)

k∈N,k=i

•

where i is the transmitter of link l, and k is the transmitter of link w. The transmission power at node i, given that there is a transmission on link l connecting nodes i and j, is limited between Pmin and Pmax , where Pmin is the minimum power required for a successful transmission without interference. Als Pmin ≤ Pi ≤ Als Pmax ; ∀l ∈ L, s ∈ S

•

A node j can be either transmitting to a single node, or receiving from a single node, or neither transmitting nor receiving. A{i,j}s + A{j,k}s ≤ 1,∀i = j = k (10) i,j∈N

•

(9)

j,k∈N

There must be at least one link active at any time slot. Als ≥ 1 (11) l∈L

•

A link has only two states at any time slot: active or inactive. (12) Als ∈ {0, 1}; ∀l ∈ L, s ∈ S

The problem is a mixed integer non-linear programming problem. Non-linearity is found in the SIN R constraint where the two variables, A and P , are multiplied by each other. One method to eliminate this type of non-linearity is to use the big − M method [28]. In this method, we first eliminate

the binary variable, assuming that it is set to 1. Then we reintroduce the binary variable in a separate term multiplied by a big number. The intuitive behind that is to have the SIN R constraint verified only if the link explored is active (Al = 1), otherwise it should be redundant. The linearized SIN R constraint is G{i,j} Pi − hl ( G{k,j} Pk + n0 ) ≥ M (A{i,j}s − 1) k=i

where M is a big positive number. If the link {i, j} is active at slot s, then A{i,j}s = 1 and the right hand side is of zero value. Then the values of the P’s can be found by solving the problem, the same as the non-linearized version with the A s set to 1. otherwise, if A{i,j}s = 0, the right hand side is of a very small negative value (theoretically −∞ ) that the formula is verified for whatever the values of the P s,and thus is redundant, which agrees with the original formula when the link explored is not active (A{i,j}s = 0).

Fig. 2. 100 routers in the network, objective Z1 , solid lines are selected links, dotted are non-selected links

V. S IMULATION R ESULTS TABLE I S IMULATION PARAMETERS

Simulation Parameter Inter-node distance Carrier Frequency Speed of Light Receiver Antenna Gain Transmitter Antenna Gain . Thermal Noise No Minimum SINR Threshold Maximum Transmit Power Receiver Sensitivity Slot time Frame time

Value 300 m 2.4 GHz 3 × 106 m/s 20db 20db 10−12 W 10 300mW −120dB 3ms 180ms

We simulate a network of N fixed wireless routers, where N varies in the experiments, deployed in a grid topology using Matlab. We solve the optimization problem using lpsolve [29]. Table I summarizes the simulation parameters. In the experiments, we set the links’ weights to be equal. We conduct two sets of experiments, for Z1 and Z2 ; the first set has the objective of maximizing the number of simulataneous active links, Z1 ; the second set has the objective of minimizing the transmission power on the links,in addition to maximizing throughput. We first show a demo of two runs for each of the objectives. In the fist run, the optimization problem is solved for a network of 100 nodes for one time slot with 10 links ready to be selected. Figure 2 presents the links selected by solving the problem using Z1 , while Figure 3 presents the links selected by solving the problem using Z2 . The two figures show that the choice of links differs by changing the objective function. It is also shown that the power values used on the links is different for the two objective functions.

Fig. 3. 100 routers in the network, objective Z2 , solid lines are selected links, dotted are non-selected link

In the second run, we study the effect of increasing the number of nodes in the network with more links ready to be selected. In these figures, the network has 400 nodes and 20 links are competing to be selected in one slot. The results for Z1 and Z2 show that the number of selected links increased and, again, the two figures have some different links selected with the power used being less in Figure 5. Figures 6,7,and 8 show the results of simulation runs with different number of links in a network of 400 nodes. Figure 6 shows the number of links selected to be active simultaneously for the two objectives. Notice that by increasing the number of links in the network, there is a higher probability to activate more links at the same time. However, since the area of the network is fixed, the number of selected links increases to a certain number where the network saturates and there is no more space to activate more links. Figure 7 shows the average transmission power at the source nodes of the selected links. It

10

First Objective (Z )

Number of Selected Links

1

9

Average Transmission Power (W)

Fig. 6.

7 6 5

Fig. 7.

VI. C ONCLUSION In this paper, we present an efficient QoS-aware link scheduling scheme for wireless mesh backbones. By using cross-layer design, the scheme tackles both power control and scheduling problems, taking into consideration the spatial reuse of the network bandwidth. The scheduling scheme is

15

20 25 Number of Links

30

35

2.5 2

30

35

First Objective (Z1) Second Objective (Z ) 2

1.5 1 0.5

10

15


The average transmission powers, in Watts, for Z1 and Z2

4 First Objective (Z ) Logarithmic Execution Time

is not a surprise to find a large difference between the power used for both objectives, becuase the concern for each one is different. Figure 8 shows a logarithmic representation of the execution time, where time is measured in seconds. We used the logarithmic representation because of the huge difference between execution times for small and large numbers of nodes in the network. It is clear that the execution time increases exponentially with the number of links, which supports going to a heuristic approach for large number of links.

10

Number of simultaneously active links for Z1 and Z2

0 5


2

8

4 5


Second Objective (Z )

3

1

Second Objective (Z2)

2 1 0 −1 −2 −3 5

10

15


30

Fig. 8. The base10-logarithm of the execution times for Z1 and Z2 , where time is in seconds

35

based on TDMA MAC and is done by a central controller, which assigns time slots to the links of different routes. The framework obtains the optimal power allocation that maximizes the number of links sharing the same time slot and satisfies the QoS requirements of each link. We compare two objective functions; the first is to maximize the number of simultaneously active links; the second objective minimizes the transmission power in addition to maximizing the number of simultaneously active links. We show by computer simulations that the two problems have different results, mainly in the values of power used and in the selection of links to be active. We also show that by increasing the number of links in a bigger area, we have more links working simultaneously. The number of selected links is limited by the saturation of the network, and it depends mainly on the size of the networks and the distances between nodes. It is also shown that increasing the number of links to be scheduled, increases the time needed to solve the optimization problem, and may render the problem inefficient from the time complexity perspective. A solution to this problem is to find a fast approximate solution using a heuristic rather than finding the exact optimal solution in an unacceptable time. R EFERENCES [1] M.A. Enright, C.-C.J. Kuo, “Fast linearized energy allocation for multimedia loading on multicarrier systems,” IEEE Journal on Selected Areas in Communications, vol. 24, no 3, Mar. 2006, pp.470-480. [2] X. Zhang, Q. Du, “Cross-Layer Modeling for QoS-Driven Multimedia Multicast/Broadcast Over Fading Channels in Mobile Wireless Networks,” IEEE Communications Magazine, Vol. 45, No. 8, pp. 62–70, August 2007. [3] Z. Niu, L. Long, J. Song, C. Pan, “A New Paradigm for Mobile Multimedia Broadcasting Based on Integrated Communication and Broadcast Networks,” IEEE Communications Magazine, vol. 46, no 7, Jul. 2008, pp.126-132. [4] J. She, F. Hou, P.-H. Ho, X. Liang-Liang, “IPTV over WiMAX: Key Success Factors, Challenges, and Solutions,” IEEE Communications Magazine, vol. 45, no 8, Aug. 2007, pp.87-93. [5] A. Cuevas, J.I. Moreno, P. Vidales, H. Einsiedler, “The IMS service platform: a solution for next-generation network operators to be more than bit pipes,” IEEE Communications Magazine, vol. 44, no 8, Aug. 2006, pp.75-81. [6] D. Natalie, K. Laevens, D. De Vleeschauwer, R. Sharpe, “Increasing the user perceived quality for IPTV services,” IEEE Communications Magazine, vol. 46, no 2, Feb. 2008, pp.94-100. [7] H. Jiang, W. Zhuang, X. Shen, A. Abdrabou, and P. Wang, “Differentiated services for wireless mesh backbone,” IEEE Commun. Mag. , vol. 44, no. 7, Jul. 2006, pp. 113–119. [8] P. Gupta, P. R. Kumar, “The capacity of wireless networks,” IEEE Transactions on Information Theory, Vol. 46(2000), pp. 388404. [9] A. Sen and M. L. Huson, “A new model for scheduling packet radio networks,” em ACM/Baltzer Journal Wireless Networks, Vol. 3 (1997), pp. 71-82. [10] S. Ramanathan, E. L. Lloyd, “Scheduling algorithms for multihop radio networks,” IEEE/ACM Transactions on Networking, Vol. 1(1993), pp. 167177. [11] I. Chlamtac, S. Kutten, “A spatial reuse TDMA/FDMA for mobile multihop radio nertworks,” Proceedings of IEEE INFOCOM’85, pp. 389-394. [12] E. S. Jung, N. H. Vaidya, “A power control MAC protocol for ad hoc networks,” Proceedings of the ACM MobiCom2002, pp. 36-47. [13] P. Karn, “MACA - a new channel access method for packet radio,” Proceedings of ARRL Computer Networking Conference, pp. 134140, 1990. [14] J. Monks, V. Bharghavan, W. M. Hwu, “A power controlled multiple access protocol for wireless packet networks,” Proceedings of the IEEE INFOCOM2001, pp. 219-228.

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