# A Scheduling Algorithm for Minimizing the Packet Error Probability in Clusterized TDMA Networks

- Arash T. Toyserkani
^{1}Email author, - Mats Rydström
^{1}, - Erik G. Ström
^{1}and - Arne Svensson
^{1}

**2009**:804621

https://doi.org/10.1155/2009/804621

© Arash T. Toyserkani et al. 2009

**Received: **1 December 2008

**Accepted: **25 July 2009

**Published: **15 September 2009

## Abstract

We consider clustered wireless networks, where transceivers in a cluster use a time-slotted mechanism (TDMA) to access a wireless channel that is shared among several clusters. An approximate expression for the packet-loss probability is derived for networks with one or more mutually interfering clusters in Rayleigh fading environments, and the approximation is shown to be good for relevant scenarios. We then present a scheduling algorithm, based on Lagrangian duality, that exploits the derived packet-loss model in an attempt to minimize the average packet-loss probability in the network. Computer simulations of the proposed scheduling algorithm show that a significant increase in network throughput can be achieved compared to uncoordinated scheduling. Empirical trials also indicate that the proposed optimization algorithm almost always converges to an optimal schedule with a reasonable number of iterations. Thus, the proposed algorithm can also be used for bench-marking suboptimal scheduling algorithms.

## 1. Introduction

One of the problems with many wireless networks today is energy consumption, stemming from the fact that modern radio transceivers are often battery powered, and, hence, energy is a scarce resource that needs to be conserved as much as possible. Complexity is another important issue, since, many wireless network applications require the size and cost of individual network nodes to be kept at a minimum. One important example of the above is wireless sensor networks (WSNs) [1, Chapter 1], that have lately received considerable attention, both from industry and academia.

In order to conserve energy, the number of packet retransmissions in the network should be kept as low as possible. High packet-loss probability is undesirable, since it can potentially cause a high number of packet retransmissions. Another important factor in preserving energy is the duty cycle of individual nodes. For instance, recent work on energy consumption in WSNs has shown that most wireless sensor devices consume almost as much energy when listening to the wireless channel, or even being in idle mode, as they do when actively transmitting a packet [1, Chapter 2]. From this perspective, a synchronized time slotted medium access (MAC) scheme (TDMA) where nodes can sleep for extended periods of time seems preferable both from interference and duty-cycle points of view. However, interference will still be present if two or more networks, or "clusters'' of nodes, are colocated in close vicinity of each other.

In the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol [2], a TDMA-type MAC scheme tailored for WSNs is integrated with clustering and routing mechanisms. In LEACH, each cluster chooses a random spreading sequence that is used locally. This reduces intercluster interference but also increases the complexity in each node. Another WSN protocol that uses a TDMA-type MAC-scheme is the Self-Organizing Medium Access Control for Sensor Networks (SMACS) protocol [3]. SMACS implements both distributed neighborhood discovery and TDMA scheduling. In SMACS, all nodes are assumed to know the time duration of a so-called "superframe''. Many other TDMA-based MAC mechanisms have been also proposed for implementation in clusterized networks [4, 5].

One frequently occurring drawback with MAC design proposals is that overly simplistic propagation models are used, for example, not accounting for Rayleigh fading effects. For instance, channel assignment problem in wireless network is often addressed by modelling the network as directed graph [6], [7, Section III-A-1], and [8]. This assumption is not suitable in fading channels where the link gains vary over time, unless all the instantaneous link gains are frequently measured and made available to the scheduler, resulting in much added overhead and complexity.

In this work, we include Rayleigh fading and log-distance path loss in the system model and propose a TDMA-type MAC mechanism that jointly schedules transmissions between nodes and cluster heads with the objective to minimize the average packet error rate (PER), (The packet error rate is assumed to be equal to the block error rate. However, in general, they are not equal but closely related.) that is, to maximize the total network throughput. We make the assumption that all nodes have a fixed output transmission power and formulate the scheduling problem as an integer programming problem, more specifically an assignment problem.

Similar approaches are taken in [9, 10], where joint opportunistic power scheduling and rate control problems are considered. Our work differs from [9, 10], and references cited therein, on three main points (a) Instead of allowing a smooth tuning of transmitter output powers, we impose an on-off constraint on transmitters. One of the main reasons is that power consumption is sometimes only weakly correlated with transmit power [1, Chapter 2]. (b) Instead of the signal to interference and noise ratio (SINR), we consider the PER (a nonlinear function of SINR) to be the main optimization objective. While the PER is a more relevant measure, the SINR is often preferred in the literature due to the lack of a tractable analytical solution for the PER for a wide range of different modulations, coding methods, and fading channels [11]. To overcome this, a closed-form formula for estimation of the PER in block faded Rayleigh channels in presence of interference is derived and shown to be highly accurate. Finally, (c) in order to make the sleep time as long and uninterrupted as possible, we do not schedule nodes on a slot-by-slot basis. Instead, we schedule all slots in a frame in one run of the algorithm such that no node receives more than one slot.

The remainder of this paper is organized as follows. In Section 2, we define the network and interference model and state additional assumptions on the system. The utility function based on our analytical approximation of PER is introduced in Section 3. The interference model is later used in the proposed MAC algorithm, that is derived in Section 4. The proposed algorithm is analyzed and evaluated through computer simulation in Section 5, and we conclude the paper in Section 6.

## 2. System Model

Let transceiver nodes and data sinks be deployed over a bounded area. The nodes are indexed by integers , and are clustered into sets . Let a frame be an interval of time divided into slots, indexed by , and let be the set of nodes, one from each cluster, scheduled for transmission in slot . If there are fewer nodes in a cluster than the number of slots in a frame, "dummy'' nodes at infinite distance from all sinks are added to the cluster. It is worth noting that a very large may result in a trivial interference-free schedule where in each time slot only one real node is scheduled with dummy nodes from all other clusters. However, setting arbitrarily large is not possible in a majority of practical systems as it also results in a large network delay and a low network throughput. The problem of how to adjust and how to select a subset of nodes when the number of nodes per cluster is larger than is not considered here.

Based on these assumptions, each cluster contains exactly nodes. In each frame, all nodes in each cluster are to be scheduled such that no more than one node from each cluster is scheduled in a given slot , and a node can only be scheduled once per frame. A schedule that satisfies these conditions is called a feasible schedule. Each cluster is assumed to have a dedicated sink node, or cluster head. Similar to a Bluetooth system [1, Chapter 5], the cluster head is the receiver of all transmissions from all nodes in a cluster. The scheduling is performed by a central entity that is connected to all sinks. While these settings resemble a cellular network architecture, the scheduling techniques developed for cellular networks are not applicable here. This is due to the fact that in cellular networks, power and rate control are essential part of the scheduling problem. While in wireless sensor networks, the on-off power control is preferred, which results in a fundamentally different problem formulation.

We assume the packet length is fixed, and that all cluster heads are coarsely synchronized on a packet level, so that transmissions in a given slot takes place at approximately the same time in all clusters. However, synchronization errors among clusters are considered in the numerical evaluations of the proposed algorithm (Section 5).

### 2.1. Interference Model

where denotes the average received power from the node at sink and models the effect of small-scale fading on the instantaneous received signal power. The level of mobility of nodes and the environment are assumed to be such that the small-scale fading can be modelled as block fading [12] over a single time slot. The small-scale fading is assumed to be Rayleigh distributed, hence is a unit mean, exponentially distributed random variable. The effects of path loss and shadowing are captured by which is assumed to be slowly varying and available to the MAC protocol either from models or measurements.

where denotes the (known) thermal noise power at cluster head .

## 3. Utility Function

where is the PER estimate based on SNR threshold model and is the average SNR. Similar results for turbo coded packets are reported in [13].

where is the bit error rate at the given and is the error correction capability (in number of bit errors). Interested readers are referred to [14] and references cited therein, for more information regarding the block error probability of various coding and decoding methods.

Since the closed-form analytical solution to is untractable [11], Monte-Carlo simulation is used in this paper to estimate . The required statistics were obtained through simulations of randomly generated networks. For each the node-sink link, is obtained by averaging over Rayleigh fading realizations. The other simulation parameters can be found in Section 5.

Finally, we note that the choice of only depends on the modulation format, that is, BPSK, the receiver architecture, the packet length, and the properties of the code. The threshold does not depend on the network configuration and layout, that is, , , and so forth. Hence the threshold can be decided prior to network deployment, and does not need to be reconfigured if the network configuration changes. For methods of finding , interested readers are referred to [11].

To isolate the effect of the proposed formula, the reliability, or "utility'', of a link from node to cluster head , is defined as . Adding more terms to does not change the optimization algorithm in Section 4 as long as utility of each schedule can be obtained independently of other schedules.

Note that if is a dummy node, then , and , that is, dummy nodes are implicitly left out from the summation in (7). The inclusion of dummy nodes in the analysis has some interesting implications. A cluster will have dummy nodes if it has more time slots than nodes. The schedule for the dummy nodes then indicates the best time slots for radio silence in the cluster from a global network perspective.

The utility function in (7) does not necessarily need to consider all clusters. The throughput and its subsequent optimization from a subset of clusters' point of view are obtained by simply removing appropriate terms from the sum in (7). The implications of this are analyzed in Section 5. We also emphasize that maximizing the utility function in (7) is different from maximizing the average SINR. With the utility in (7), increasing the SINR for a node beyond the point where does not increase the utility significantly. Conversely, the cluster utility does not change much if the SINR for a node with is further decreased.

## 4. Medium Access Control

The aim of the proposed Medium Access Control (MAC) layer is to schedule node transmissions such that the average probability of a successful packet delivery in the network is maximized. Due to the assumed slotted MAC scheme, this will also maximize the network throughput. We define as a set of all feasible slot schedules, that is, . We also define as a set of feasible schedules where node has been scheduled, that is, .

Here, and throughout the rest of this work, is an indicator function that is unity when and zero otherwise. The constraints in ensures that , for all . That is, is a feasible slot schedule. The constraints in ensure that all nodes are scheduled in exactly one slot. Hence, and are satisfied if and only if is a feasible schedule.

As the number of nodes and clusters in the network grows, the complexity of a brute-force solution to (8) quickly becomes prohibitive. In fact, there are as many as different feasible schedules to choose from.

### 4.1. MAC Problem for Two Clusters

Unlike the case of a multidimensional assignment problem, efficient algorithms exist that solve (9) in polynomial time such as maximum weight matching problem on bipartite graph [15]. We use the auction algorithm, due to Bertsekas [16], [17, Chapter 6], briefly described below.

Consider problem (9), where the schedule for nodes in , that is, , is fixed and known. The auction algorithm for solving this problem is as follows. (a) Envision the nodes in as objects on sale at an auction, and envision the slots as buyers at the auction. Initially, the asking prices of the objects on sale are set to zero. (b) Let each slot successively "place a bid'' on the node , that is, the node that yields the highest net value for slot . (c) When a node is bid upon, its asking price is raised by , where is a small number, and , that is, the second best net value for slot . The reason for the additional small increase in price, , is to prevent ties among buyers, a topic further discussed in [17]. In this paper we let . (d) The auction continues until all nodes have received at least one bid, at which point a solution to (9) has been found.

to within any of the optimal value ( depends on the choice of ). This is, in fact, the dual problem to (9), after relaxation of constraints on nodes in .

For a complete derivation, additional discussions, and results on the auction algorithm, the reader is referred to [16], [17, Chapter 6], and references cited therein.

### 4.2. MAC Problem for Arbitrary Number of Clusters

It was noted above that the complexity of a brute-force solution to (8) grows quickly with and . However, if a relaxed problem, that is, the maximization of a Lagrangian, can be easily solved, and we also have access to a good method that converts a solution to the relaxed problem into one that is primal feasible, then experience with similar types of combinatorial optimization problems; see, for example, [9, 10, 17–19], and references cited therein, gives that an iterative solution of the dual problem often yields a near optimal, or even an optimal solution to the primal problem. Hence, the algorithm we propose is an iterative algorithm similar to one in [18], where each iteration involves the following three steps. ( ) Given a vector of dual variables, a relaxed version of (8) is solved. ( ) A primal feasible schedule is constructed from the solution to the relaxed problem and the vector of dual variables. ( ) If the obtained primal solution is found to be unsatisfactory, then the dual variables are updated, and we iterate again.

#### 4.2.1. The Relaxation Step

Hence, for a given vector of dual variables , this problem is a two-dimensional assignment problem which is easily solved, as was shown in Section 4.1. We note that, to compute , a search over slot assignments is necessary. In the scenarios considered in this work, an exhaustive search is feasible. However, larger networks may require the addition of more advanced search methods, such as branch and bound techniques, further discussed in Section 5.

#### 4.2.2. A Method for Generating Feasible Schedules

After enforcing primal constraints on nodes in all clusters up to and including , a feasible schedule has been generated from the solution to (11), and we can compute its primal objective function value using (8).

#### 4.2.3. Algorithm Termination Criteria

If we find a primal feasible schedule with a zero, or small, duality gap, then this schedule is guaranteed to be optimal, or near-optimal, in (8). On the other hand, if the best found schedule has a large relative duality gap, for example, , then we know that a significant increase in the objective function value could be possible if we continue iterations. The proposed algorithm terminates when falls below a threshold (or when a maximum number of iterations have been exceeded).

#### 4.2.4. Dual Variable Update Step

If a satisfactory schedule has not yet been obtained, we update dual variables in . We use the heuristic "price-update'' method proposed in [18], which is loosely based on the subgradient method [19] and has been shown to perform well for similar problems. We only give a brief overview of the update method here, and refer to [18] for the details.

Note that is implicitly obtained when using the auction algorithm to enforce constraints on nodes in .

Intuitively, this dual variable update approach can be interpreted as follows. If, after fixing the schedule for clusters to , two or more slots have a given node in as their preferred choice in terms of interference conditions, then the "price'' of this node is increased in future iterations of the algorithm. If there exist a node that no slot has as its preferred choice, then the price of this node is reduced. This way, solutions to the relaxed problem (11) that violates constraints in (8) are penalized.

## 5. Numerical Analysis and Discussion

### 5.1. System Setup

To emulate a network configuration where a clustering algorithm, for example, LEACH [2], has been executed, we first manually deploy cluster heads at coordinates . The node coordinates in the th cluster is drawn as realizations from a circular Gaussian distribution with mean equal to the coordinates of the th cluster head and standard deviation . The distance between cluster heads, , , and together determines the expected SINR conditions in the network. For a fixed and , a network with sparsely deployed cluster heads and small on the average experiences less intercluster interference than a network with more dense cluster heads and/or higher .

For notational convenience, we define a constant transmission range , which is the range where the packet error rate (PER) goes above in absence of fading and interference. In all simulated scenarios discussed below, cluster heads were deployed on the corners of a square with side , that is, at , , , and . Each of the clusters has nodes, and there are slots in a frame, which implies that each cluster has one dummy node. The proposed scheduling algorithm was run until the relative duality gap , see (16), or until a maximum of iterations.

### 5.2. Convergence Properties of the Proposed Algorithm and Some Remarks on Complexity

The convergence of Lagrangian relaxation method that is used in this work is only guaranteed if a strong duality property can be proven. Since strong duality of the general method used here is still an open problem in the literature, the convergence of this application of the optimization method is not proven either. Nevertheless, the dual function, defined in (11), provides an upper bound to the primal which implies that even if the algorithm fails to converge in some scenarios, the maximum potential gain of an unknown optimal solution over the best known schedule is always obtained. This result has significant practical importance as it can be used to trade performance for complexity, especially when working with iterative scheduling approaches.

In this section, the convergence properties of the proposed algorithm are studied by extensive simulations. During the simulations, the best achieved utilities , , and after at most , , and iterations, respectively, were stored. An upper bound on achievable utility in each network was also computed. This bound was computed as , where is the relative duality gap defined in (16). To investigate the convergence properties of the proposed algorithm, we computed the relative utility difference , for . This difference indicates how close to the optimal solution the algorithm is after iterations. If , then the optimal schedule has been found, while, if , an average relative increase of in utility per node could possibly be achieved by additional iterations of the algorithm. We define to be the relative difference between a random scheduling (with utility ) and the upper bound.

The results obtained after a single iteration deserves some special attention. It appears that a reduced complexity "greedy'' algorithm that only iterates once, that is, executes consecutive auction algorithms, can be used without a significant degradation in performance. This conclusion is of great importance in networks where complexity is a limiting factor. The overall complexity of the proposed algorithm mainly depends on , , and the number of iterations the algorithm spends before termination. As noted in Section 4.2, there are different feasible schedules to consider. However, the proposed algorithm only investigates a small subset of all possible feasible schedules.

Empirical tests on a personal computer have indicated that networks of up to clusters, each with nodes (e.g., the maximum number of slaves in a Bluetooth network), are manageable with the proposed algorithm. The part of the algorithm that introduces most complexity is the search for in (11), which is implemented here as an exhaustive search over the possible relaxed slot assignments. If larger networks than , is required, then more advanced search methods must be considered.

Since all the nodes in every clusters are scheduled after a single run of the algorithm, the update frequency of the schedules depends only on the mobility, that is, the rate that average powers vary. In the low mobility sensor networks considered in this paper, the frequency of schedule update is substantially lower than the schedule usage time and therefore, the communication overhead cost of the proposed algorithm in these scenarios is negligible.

### 5.3. Throughput in a Perfectly Synchronized Scenario

If the network layout is such that the intercluster interference is low, or the distance between node and sink is too long for communication even in the interference-free case, the benefits of using the proposed algorithm compared to just using an arbitrary schedule should intuitively be quite small (as an extreme case, consider a network where approaches zero, or goes to infinity). To quantify this, networks with , , and m were generated. The threshold was again set to dB. Two schedules were generated for each network layout, one using the proposed algorithm, and one random but feasible schedule. For each network and schedule, in addition to for all nodes, we also compute the normalized network throughput , given by .

### 5.4. Packet-Error Rates with Cluster Synchronization Errors

## 6. Conclusions

We have, by modelling interference as additive and Gaussian, derived an expression for the packet-loss probability in networks with mutually interfering clusters of transceivers deployed in Rayleigh-fading environments. Computer simulations showed a good agreement between the model and actual packet error-rates.

A scheduling algorithm for clustered wireless networks that exploits the derived packet-loss model was then presented. Computer simulations of networks with transmissions scheduled by the proposed algorithm showed that a significant increase in network throughput is achievable as compared to the case where clusters choose schedules independently without considering the schedules at interfering clusters. Although the scheduling algorithm was derived under the assumption of a perfectly synchronized network, we have shown that a synchronization error on the order of several symbol durations does not degrade the algorithm performance significantly.

Numerical results indicate that convergence to the optimal schedule almost always occur with a reasonable number of iterations. Hence, the proposed algorithm can be used as a tool for benchmarking the performance of other (suboptimal) scheduling algorithms.

## Appendix

### Packet-Loss Probability

where we have used the assumption that the fading coefficients are i.i.d. with unit mean. The last equality follows from the identity , that holds for .

## Declarations

### Acknowledgment

This work was supported by Vinnova Project no. 2003-02803.

## Authors’ Affiliations

## References

- Karl H, Willig A:
*Protocols and Architectures for Wireless Sensor Networks*. John Wiley & Sons, New York, NY, USA; 2006.Google Scholar - Heinzelman WB, Chandrakasan AP, Balakrishnan H: An application-specific protocol architecture for wireless microsensor networks.
*IEEE Transactions on Wireless Communications*2002, 1(4):660-670. 10.1109/TWC.2002.804190View ArticleGoogle Scholar - Sohrabi K, Gao J, Ailawadhi V, Pottie GJ: Protocols for self-organization of a wireless sensor network.
*IEEE Personal Communications*2000, 7(5):16-27. 10.1109/98.878532View ArticleGoogle Scholar - Gerla M, Tzu-Chieh Tsai J: Multicluster, mobile, multimedia radio network.
*Wireless Networks*1995, 1(3):255-265. 10.1007/BF01200845View ArticleGoogle Scholar - Li J, Lazarou GY: A bit-map-assisted energy-efficient MAC scheme for wireless sensor networks.
*Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN '04), 2004*55-60.Google Scholar - Ramanathan S: Unified framework and algorithm for channel assignment in wireless networks.
*Wireless Networks*1999, 5(2):81-94. 10.1023/A:1019126406181View ArticleGoogle Scholar - Stankovic JA, Abdelzaher TF, Lu C, Sha L, Hou JC: Real-time communication and coordination in embedded sensor networks.
*Proceedings of the IEEE*2003, 91(7):1002-1022. 10.1109/JPROC.2003.814620View ArticleGoogle Scholar - Gandham S, Dawande M, Prakash R: Link scheduling in wireless sensor networks: distributed edge-coloring revisited.
*Journal of Parallel and Distributed Computing*2008, 68(8):1122-1134. 10.1016/j.jpdc.2007.12.006MATHView ArticleGoogle Scholar - Lee J-W, Mazumdar RR, Shroff NB: Opportunistic power scheduling for dynamic multi-server wireless systems.
*IEEE Transactions on Wireless Communications*2006, 5(6):1506-1515.View ArticleGoogle Scholar - Lee J-W, Mazumdar RR, Shroff NB: Joint opportunistic power scheduling and end-to-end rate control for wireless ad hoc networks.
*IEEE Transactions on Vehicular Technology*2007, 56(2):801-809.View ArticleGoogle Scholar - Toyserkani AT, Ström EG, Svensson A:
*A semi-analytical approximation to the block error rate in Nakagami-m block fading channels.*Department of Signals and Systems, Chalmers University of Technology, Göteborg, Sweden; 2009.Google Scholar - McEliece RJ, Stark WE: Channels with block interference.
*IEEE Transactions on Information Theory*1984, 30(1):44-53. 10.1109/TIT.1984.1056848MATHView ArticleGoogle Scholar - Rodrigues MRD, Chatzigeorgiou I, Wassell IJ, Carrasco R: Performance analysis of turbo codes in quasi-static fading channels.
*IET Communications*2008, 2(3):449-461. 10.1049/iet-com:20060586MATHMathSciNetView ArticleGoogle Scholar - Lin S, Costello DJ:
*Error Control Coding*. 2nd edition. Pearson Prentice-Hall, Upper Saddle River, NJ, USA; 1982.Google Scholar - Tarjan RE:
*Data Structures and Network Algorithms*. SIAM, Philadelphia, Pa, USA; 1983.View ArticleGoogle Scholar - Bertsekas DP:
*Nonlinear Programming*. Athena Scientific; 1999.MATHGoogle Scholar - Bertsekas DP: The auction algorithm for assignment and other network flow problems: a tutorial.
*Interfaces*1990, 20: 133-149. 10.1287/inte.20.4.133View ArticleGoogle Scholar - Deb S, Yeddanapudi M, Pattipatti K, Bar-Shalom Y: A generalized S-D assignment algorithm for multisensormultitarget state estimation.
*IEEE Transactions on Aerospace and Electronic Systems*1997, 33: 523–-538.View ArticleGoogle Scholar - Andréasson N, Evgrafov A, Patriksson M:
*An Introduction to Continuous Optimization*. Studentlitteratur; 2005.Google Scholar

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