 Research Article
 Open Access
Uplink CrossLayer Scheduling with Differential QoS Requirements in OFDMA Systems
 Bo Bai^{1, 2}Email author,
 Wei Chen^{2},
 Zhigang Cao^{2} and
 Khaled Ben Letaief^{1}
https://doi.org/10.1155/2010/168357
© Bo Bai et al. 2010
 Received: 15 January 2010
 Accepted: 21 September 2010
 Published: 23 September 2010
Abstract
Fair and efficient scheduling is a key issue in crosslayer design for wireless communication systems, such as 3GPP LTE and WiMAX. However, few works have considered the multiaccess of the traffic with differential QoS requirements in wireless systems. In this paper, we will consider an OFDMAbased wireless system with four types of traffic associated with differential QoS requirements, namely, minimum reserved rate, maximum sustainable rate, maximum latency, and tolerant jitter. Given these QoS requirements, the traffic scheduling will be formulated into a crosslayer optimization problem, which is convex fortunately. By separating the power allocation through the waterfilling algorithm in each user, this problem will further reduce to a kind of continuous quadratic knapsack problem in the base station which yields low complexity. It is then demonstrated that the proposed crosslayer method cannot only guarantee the application layer QoS requirements, but also minimizes the integrated residual workload in the MAC layer. To further enhance the ability of QoS assurance in heavily loaded scenario, a call admission control scheme will also be proposed. The simulation results show that the QoS requirements for the four types of traffic are guaranteed effectively by the proposed algorithms.
Keywords
 Power Allocation
 Call Admission Control
 OFDMA System
 Ergodic Capacity
 Good Effort
1. Introduction
Orthogonal frequencydivision multiple access (OFDMA) offers a very attractive solution in providing high performance and flexible deployment for broadband wireless access network. In particular, OFDMA provides at more degrees of freedom for multiuser systems. The subcarriers can be allocated dynamically at different time instances to exploit the multiuser diversity [1] and frequency diversity [2], and adaptive power allocation can also be applied to further improve the power efficiency [3]. To enhance the efficiency and fairness, OFDMA also allows us to schedule timedomain resources, referred to as timeslots.
The typical OFDMA systems in wireless communications are 3GPP LTEbased cellular system [4] and IEEE 802.16 protocolbased WiMAX system [5]. These newly emerging systems provide a platform for applying the crosslayer resource allocation and scheduling technology. These systems are designed as a unified wireless access system to support multiple types of traffic, such as voice, data, audio/video, multimedia, interactive game, and Internet access. Thus, how to jointly use these technologies in the physical (PHY) layer and MAC layer to support the traffic with differential QoS requirements in the application layer is a central problem in OFDMA systems [6]. In this paper, we shall focus on this problem and use a crosslayer optimization methodology to provide a traffic scheduling method for supporting efficiently multiplexing services with a variety of QoS requirements.
Due to the stochastic nature of the traffic arrival process and the wireless channel, it is a challenging work to achieve fair and efficient resource allocation and QoSguaranteed scheduling in OFDMA systems. In 1995, a jointlayer optimization perspective was proposed by Telatar and Gallager in [7]. Subsequently, Berry and Yeh put forward that the future wireless communication system design needs crosslayer optimization methodology [8]. They also discussed the crosslayer approach for wireless resource allocation in multiaccess and broadcasting queueing systems, respectively. Specifically, in order to collect all the parameters together in the uplinks, one may formulate the system as a multiaccess queueing system or generic switch model and consider the weighted sum of the queue lengths, which is often referred to as the integrated workload. More recently, Stolyar proved the optimality of the MaxWeight scheduling in [9]. In [10], Mandelbaum and Stolyar extended this method to the continuous strictly increasing convex function of the queue length and proved the optimality of law scheduling. Based on the queueing theory and optimization method, Niyato and Hossain studied the radio resource management in IEEE 802.16 wireless broadband system [11]. An alternative method to incorporate concerns and constraints of various layers is to apply utility maximization formulation. In [12], Song et al. used this method to obtain a queueaware and channelaware scheduling algorithm, that is, transmit the traffic which minimizes the average delay. Based on the similar framework, Kulkarni and Rosenberg studied the opportunistic scheduling framework of multiple QoS requirements and shortterm fairness in the system with multiple wireless interfaces [13]. In [14], Fu et al. solved the dual problems of maximizing expected throughput given limited energy and of minimizing expected energy given the minimum throughput constraint.
The above works have significantly enhanced the overall performance of wireless communications. However, they did not consider the scheduling problem of multiple types of traffic with differential QoS requirements, which is a practical scenario in OFDMA wireless access network. A typical OFDMA system, say IEEE 802.16 broadband wireless access network, has multiple independent users communicating with one base station (BS). There are four types of traffic in IEEE 802.16 protocol, namely, best effort service (BE), nonrealtime polling service (nrtPS), realtime polling service (rtPS), and unsolicited grant service (UGS) [5]. Any applicationlayer traffic must be classified into one of these types, and its QoS requirements can be described differentially by minimum reserved rate, maximum sustainable rate, maximum latency, and tolerant jitter. Thus, the arrival traffic of each user will be stored in different buffers and scheduled by a crosslayer scheduler in BS. Since the OFDMAbased PHY layer is timeslotted, every user should offer the traffic transmission request and its QoS parameters at the beginning of each timeslot. Given the constraints of QoS requirements and the instantaneous channel conditions, the scheduler allocates subcarriers, power, and timeslots, so as to transmit the traffic efficiently and guarantee the differential QoS requirements.
In this paper, the integrated residual workload method is introduced to cover the above considerations. By using this method, the resource allocation and traffic scheduling can be formulated into a crosslayer optimization problem under the transmission rate constraints, which is convex fortunately. Since the power allocation gives little advantage in terms of ergodic capacity [15], we decompose the power allocation from the original convex optimization problem through the waterfilling algorithm in each user. The resulting optimization problem in BS, referred to as the timefrequency allocation problem, is fortunately a continuous quadratic knapsack problem with a generalized upper bound and an angular structure in the constraints. The knapsack problem (integer or continuous) has been studied for decades, which has often used to solve resource allocation problems in operational research, economics, military, and communications [16, 17]. According to the results in [18, 19], this timefrequency allocation problem can be solved with a low complexity. At this context, an integrated residual workload minimization (IRWM) algorithm and a heuristic call admission control (CAC) algorithm are proposed as a framework of the resource management scheme for future OFDMAbased wireless access networks. It is then demonstrated that the proposed crosslayer method cannot only guarantee the application layer QoS requirements, but also minimize the integrated residual workload in the MAC layer. The simulation results also verified that the QoS requirements for the four types of traffic are guaranteed effectively by the proposed scheduling algorithms.
The rest of the paper is organized as follows. Section 2 presents the system model and the QoS requirements. In Section 3, we present the crosslayer optimization problem and the problem decomposition. An optimal scheduling policy and a heuristic CAC algorithm is also presented in this section. Simulation results are presented in Section 4. Section 5 concludes this paper.
2. CrossLayer Multiaccess Queuing Model
2.1. QoS Parameters and Traffic Scheduling Framework
 (i)
minimum reserved rate , denoted by , which is the transmission rate that cannot be violated even the system is in congestion;
 (ii)
maximum sustainable rate , denoted by , which is the peak transmission rate allowed;
 (iii)
maximum latency , denoted by , which is the maximum sojourn time of the traffic in a queue;
 (iv)
tolerant jitter (Tol ), denoted by , which is the maximum absolute value of the latency difference for the same type of traffic.
We use , to denote the set of traffic types (in this paper, the script symbol is used to denote a set, whose cardinality will be denoted by ), Then, the best effort (BE) service, denoted by , is used to support the best effort traffic, such as Email and file transfer. There are no explicit QoS requirements. The nonrealtime polling service (nrtPS), denoted by , assures the uplink service flow receives transmission opportunities even during network congestion, such as Internet browsing and data transfer. The QoS requirements supported include and . The realtime polling service (rtPS), denoted by , offers realtime uplink service flows that transport variablesize data packets, such as moving pictures experts group (MPEG) video, interactive game. The QoS requirements supported include , , and . The unsolicited grant service (UGS), denoted by , offers realtime service flows that transport fixedsize data packets arriving periodically, such as T1/E1 and voice over IP without silence suppression. The QoS requirements supported include , (which is equal to ), , and Tol .
In the interested OFDMA system, access user must negotiate the QoS requirements with BS before the traffic connection is established. The negotiation process determines the value of , , , and for each type of traffic. Since this OFDMA system is timeslotted, then each user must provide the current value of the QoS parameters (including rate, latency, and jitter) and the traffic transmission request for each type of traffic at the beginning of every timeslot. Then, under the constraints of the QoS requirements and the channel conditions, BS determines which type and how much the traffic will be transmitted in this timeslot and allocates subcarrier, power, and time to them. Thus, the scheduling policy of BS is the central problem here. The crosslayer method proposed in the paper is an optimal resource allocation and scheduling method.
2.2. Problem Formulation
The channel condition of the whole system is given by , and its state space is denoted by . We also let , , and denote its state space.
where " " denotes was transmitted before .
3. Optimal Scheduling Policy
3.1. CrossLayer Optimization Problem
The scheduling policy for this OFDMA system should transmit all the traffic as soon as possible, while guaranteeing the differential QoS requirements. As a crosslayer design problem, maximizing the spectrum efficiency is also an important consideration. Thus, we need to design a proper objective function to collect all the considerations. Similar to the methods in [9, 10, 13], the integrated residual workload is defined as follows.
Definition 1.
where is the length of timeslot, is the transmission rate allocated to traffic . is the function of the jitter , and is the function of the latency . They are both the continuous strictly increasing nonnegative convex function, and they satisfy: if , , then , ; if , , then , .
can satisfy the conditions in Definition 1, where is the shape factor and is the location parameter, which will be set to or . Thus, the integrated residual workload represents the residual workload of four types and their QoS requirements of delay and jitter. Thus, the crosslayer scheduling algorithm proposed in this paper is to minimize the integrated residual workload.
3.2. Problem Decomposition
Equation (16) represents a complicated nonlinear optimization problem. In this section, we will propose a method to solve this problem with low complexity. Firstly, the following theorem shows the problem represented by (16) is convex.
Theorem 2.
where is the Lagrangian multiplier, and .
Proof.
Consider the definition of convex optimization problem in [23]. First, the feasible region of the optimization variables and constructs a convex polyhedron. Then, besides two groups of linear constraints, there are three groups of nonlinear constraints. Since a nonnegative weighted sum of convex functions is a convex function [23], then is a concave function of and according to (1), (3), and (5). Since is an increasing convex function, is a convex function. Note that and are constants, for the delay and the jitter are known, then is a convex function. Since this is a convex optimization problem, the solutions expressed in (18) can be derived from KarushKuhnTucker (KKT) condition directly.
Although the optimization problem represented by (16) is convex, the numerical algorithm for this problem still has a high computation complexity [23]. In the following, we will decompose this problem. The resulting problem enjoys a low complexity at a cost of trivial performance loss.
According to the algorithm proposed in [18, 19], this subproblem can be numerically solved efficiently.
3.3. Asymptotic Optimal Scheduling Policy
The feasible region of the problem represented by (19) might be an empty set, which means that the system may be unstable for some traffic transmission request and QoS requirements. The scheduling algorithm under which the system is stable is referred to as the stable scheduling algorithm (SSA). In order to discuss the stability of the scheduling algorithm, we define the static service split (SSS) scheduling algorithm which is similar to [9].
Definition 3.
Then, is called the SSS algorithm.
Similar to [9], the simple observation shows that if and the constrains hold, then the SSS algorithm, allocating to each traffic the average rate, will make the system stable. This fact gives the condition on which the system is stable.
Lemma 4.
From this lemma, one can define the scheduling algorithm stability region as the QoS requirements set which satisfies Lemma 4. Then, the asymptotic properties of the optimization problem represented by (19) can be summarized as the following theorem.
Theorem 5.
If QoS parameters are in the scheduling algorithm stability region , then the solution of the optimization problem represented by (19) satisfies the QoS requirements of (6), (7), and (9) when , and minimizes the integrated residual workload .
Proof.
If the QoS requirements are in the region , according to Lemma 4, the SSA must exist. So, the feasible domain of the optimization problem represented by (19) is not null. According to Theorem 2, the optimal solution of the problem represented by (19) exists. Because the arrival rate of traffic is , which is also the requesting rate, then is equal to as long as the optimal solution exists. According to the law of large numbers, the average rates in time are equal to their mathematical expectations, then (6), (7), and (9) hold.
The scheduling algorithm executes as in Algorithm 1: users offer traffic transmission requests and QoS parameters at the beginning of each timeslot, meanwhile the BS estimates the uplink wireless channel condition, then the BS solves the problem represented by (19) and sends the resource allocation results to all users. After receiving , each user executes the waterfilling algorithm independently to obtain . As this algorithm always tries to minimize the integrated residual workload, it will be referred to as the integrated residual workload minimization (IRWM) algorithm.
Algorithm 1: IRWM algorithm.
Receive the transmission request and the QoS parameters.
for and do
if then
.
else if then
.
end if
end for
Solve the optimization problem represented by (19).
Transmit to every user.
3.4. Heuristic Call Admission Control
For an OFDMA system in the heavily loaded scenario, the stability of the queues cannot always be assured. In this case, the optimization problem represented by (19) will have a null feasible region. To overcome this problem, we need to design a call admission control (CAC) mechanism. The algorithm based on this idea is listed as Algorithm 2. Join this heuristic CAC algorithm and the IRWM algorithm will form a crosslayer resource allocation and scheduling framework for OFDMA wireless networks supporting multiple types of traffic.
Algorithm 2: Heuristic CAC algorithm.
Determine , , and for a specific and .
Add , and to (19).
, .
.
if exists then
Admit.
else
Reject.
end if
4. Simulation Results
The uplink scenario of one BS and users is addressed in this section. The wireless channel between each user and the base station undergoes path frequency selective fading. The OFDMA system considered has subcarriers, and the bandwidth for each subcarrier is . The channel gains for different subcarriers are independent and identical distribution and the variance is . The average SNR for the first four users are and for the second user. The target BER of AMC mechanism is . If we allocate transmission power equally, then the channel capacity is about for the first four users and about for the second four users. We consider the time duration of timeslots.
Parameters of the traffic sources for two users.
Traffic source  Type  Type  Type  Type 

ON state length 




OFF state length 




Interarrival time 




Packet size 




QoS parameters of each traffic type for two users.
QoS parameters  Type  Type  Type  Type 
















Tol 




For performance comparison, the heuristic scheme has also been simulated. In this scheme, the interleaved subcarrier allocation is used. The subcarriers are allocated to the traffic of type first. Then, according to the traffic requirements and QoS parameters, the subcarriers are allocated to the traffic of types and , respectively. At last, the residual subcarriers are allocated to the traffic of type . In this scheme, the maximum sustainable rates of traffic types and are two critical parameters, which balance the transmission among traffic types , , and traffic type . If the maximum sustainable rate is too large, the traffic of type can nearly not get transmission opportunities, while if it is too small, the latency requirement of traffic types will be violated. In IRWM algorithm; however, there is no need to set the maximum sustainable rate manually, because the integrated residual workload can balance all the types of traffic automatically. The simulation results show that the proposed IRWM algorithm has a better performance. It has a greater transmission rate for traffic types of , , and . It also yields a smaller latency for the traffic type of . Therefore, the simulation results show that the differential QoS requirements of four types of traffic are guaranteed effectively by the proposed IRWM algorithm.
5. Conclusion
The problem of uplink traffic scheduling with differential QoS requirements in OFDMA systems was addressed in this paper. A crosslayer optimization methodology, which jointly considers the traffic arrival process and the wireless channel conditions, was adopted to achieve better QoS for the users accessing to a common base station. In particular, we introduce the integrated residual workload to formulate the traffic scheduling problem into a convex optimization problem. By decomposing this problem into two steps, that is, a continuous quadratic knapsack problem in BS and a waterfilling power allocation algorithm in each user, we presented a lowcomplexity algorithm referred to as the IRWM. Besides, a heuristic CAC scheme was proposed to avoid the sharply decreasing of QoS, when the system is in congestion. Both the theoretical analysis and the simulation results showed that the differential QoS requirements of the application layer are guaranteed effectively by the proposed algorithm in the MAC layer.
Declarations
Acknowledgment
This work is supported by NSFC key project under Grant no. 60832008, and RGC/NSFC project under Grant no. N_HKUST622/06.
Authors’ Affiliations
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