Dynamic Subcarrier Allocation for Real-Time Traffic over Multiuser OFDM Systems
© Fanglei Sun et al. 2009
Received: 24 January 2009
Accepted: 14 April 2009
Published: 31 May 2009
A dynamic resource allocation algorithm to satisfy the packet delay requirements for real-time services, while maximizing the system capacity in multiuser orthogonal frequency division multiplexing (OFDM) systems is introduced. Our proposed cross-layer algorithm, called Dynamic Subcarrier Allocation algorithm for Real-time Traffic (DSA-RT), consists of two interactive components. In the medium access control (MAC) layer, the users' expected transmission rates in terms of the number of subcarriers per symbol and their corresponding transmission priorities are evaluated. With the above MAC-layer information and the detected subcarriers' channel gains, in the physical (PHY) layer, a modified Kuhn-Munkres algorithm is developed to minimize the system power for a certain subcarrier allocation, then a PHY-layer resource allocation scheme is proposed to optimally allocate the subcarriers under the system signal-to-noise ratio (SNR) and power constraints. In a system where the number of mobile users changes dynamically, our developed MAC-layer access control and removal schemes can guarantee the quality of service (QoS) of the existing users in the system and fully utilize the bandwidth resource. The numerical results show that DSA-RT significantly improves the system performance in terms of the bandwidth efficiency and delay performance for real-time services.
Demands for real-time multimedia applications are increasing rapidly for broadband wireless networks. Orthogonal frequency division multiplexing (OFDM) is considered a promising technique in such systems. In this paper, we consider multiuser systems  where multiple users are allowed to transmit simultaneously on different subcarriers per OFDM symbol. Mobile users on certain OFDM subchannels may experience deep frequency-selective fading in a multipath propagation environment. Since each user may have a different subchannel impulse response, a poor subchannel for one user may be a good subchannel for another user. Clearly, if a user who suffers from poor subchannel gain can be reassigned to a better subchannel, the total throughput can be increased. This is also known as multiuser diversity. Since the subcarrier gains vary from user to user, to achieve higher system capacity and spectral efficiency, it is better to allocate subcarriers and the corresponding power dynamically according to the instantaneous channel states of all users.
To support QoS for multiple services, packet scheduling has been identified as an important mechanism in wired networks. When considering the multipath fading, high error rate, and time-varying channel capacity in wireless links, some new packet scheduling algorithms are developed, such as channel state dependent round Robin (CSD-RR) , feasible earliest due date (FEDD) , modified largest weighted delay first (M-LWDF) , and link-adaptive LWDF  algorithms. CSD-RR schedules the packets whose channel is in the "Good'' state in a Round Robin fashion. FEDD focuses on scheduling the packet which has the smallest time to expiry and whose channel is in the "Good'' state. M-LWDF schedules the packet according to , where is the head-of-the-line packet delay for queue , is the channel capacity with respect to flow , and are arbitrary positive constants. M-LWDF is proven to be a throughput-optimal scheduling algorithm. Link-adaptive LWDF aims to satisfy the stringent packet delay constraints, but without any guarantees. The objectives of these algorithms are to maximize the system spectral efficiency by exploiting the random channel variations and to provide QoS guarantees to the users by deferring the transmissions on the deep fading links and compensating for them when the links recover. However, all these scheduling algorithms are based on packet scheduling, and multiple frequency subcarrier scheduling, which may be implemented in multiuser OFDM systems, is not considered. In the PHY layer, the total power resource is limited. Given the required number of subcarriers of different users, how to minimize the power allocation for the users on the subcarriers under users' SNR requirements is still a problem. To solve this problem, a suboptimal subcarrier allocation algorithm based on constructive assignment and iterative improvement is proposed in  and adopted in . The algorithm exploits the similarity between the subcarrier allocation problem and the classical assignment problem. However, the algorithm can only provide a suboptimal allocation. An optimal solution to this power minimization problem is the Kuhn-Munkres algorithm proposed for the classical assignment problem . Kuhn-Munkres is based on the Hungarian algorithm . OFDM subarrier allocation using this method has been studied in . However, an important assumption in that paper is that the number of assigned subcarriers for the users is known. Actually, without this information, the Kuhn-Munkres algorithm cannot perform the subcarrier allocation. In addition, in most of the proposed scheduling algorithms, the dynamic variation of the number of active users in the system is ignored.
In this paper, we propose a cross-layer resource allocation scheduling algorithm, named DSA-RT, for real-time services under frequency-selective fading channel in multiuser OFDM systems. This algorithm has two cooperative components: the MAC-layer scheduling/control scheme and the PHY-layer resource allocation scheme. At the MAC layer, based on queuing theory, active users' expected resource requirements to satisfy delay constrains are calculated in terms of the number of subcarriers per OFDM symbol. With the support of our MAC-layer scheduling scheme, the number of required subcarriers and the users' transmission priorities are given. At the PHY layer, based on the modified Kuhn-Munkres algorithm, a PHY-layer resource allocation algorithm is proposed to satisfy all users' requirements under the system SNR and power constraints and to decide the real subcarrier allocation for each active user. (Users admitted to the system are termed active users. Once new users are admitted, they will be allocated resources (subcarriers) by the access control scheme.) When considering a system where the number of active users changes dynamically, if there are still subcarriers left in an OFDM symbol, the access of new mobile users will be considered. In addition, if the dropping rates of certain users violate their maximum tolerable limits, a removal scheme is triggered to remove the aggressive users so as to guarantee the QoS of the other existing users. With the cooperation of the MAC and PHY layer schemes, our proposed algorithm offers the following advantages: (1) based on queuing theory, real-time users' delay requirements can be evaluated in terms of the number of subcarriers required, leading to a more flexible scheduling algorithm which can effectively guarantee the QoS for real-time services in multiuser OFDM systems; (2) with the number of the expected subcarriers and transmission priority information from the MAC layer, the proposed PHY-layer resource allocation scheme aims to maximize the bandwidth usage under the current channel state, system SNR, and power constraints; (3) when the number of mobile users is dynamically changed, the access control and removal schemes can dynamically adjust system flows and provide delay-related guarantee for the active users in the system.
The rest of this paper is organized as follows. The system model is introduced in Section 2. The detailed description of DSA-RT is presented in Section 3. The simulation results are given in Section 4. Section 5 draws the conclusions.
2. System Model
where represents the SNR requirement of user . C1 states that the total subcarriers allocated to all users are less than or equal to ; C2 shows that the total transmission power should be less than or equal to the system power limit, while satisfying all users' SNR requirements; C3 means that no more than one user transmits in the same subcarrier; C4 is the average delay requirement of each user.
The solution of the above optimization problem (3) is not explicit due to the fact that C4 is not directly related to . Thus in the following section, we will establish the relationship between them and give the suboptimal subcarrier allocation solution for each symbol with lower computational complexity.
3. Cross-Layer Algorithm Description
Based on queuing theory, the MAC-layer scheduling scheme is developed to calculate the users' transmission priorities and their corresponding specific bandwidth requirements in terms of the number of subcarriers. With the channel state information, users' SNR requirements and the system power constraints, the PHY-layer resource allocation scheme can deduce the maximum attainable throughput for each supported user. In addition, the MAC-layer access control and removal scheme will be triggered to adjust the number of users being served and provide the QoS guarantee for the active users in the system.
3.1. MAC-Layer Scheduling Scheme
By solving the above inequality, we can easily obtain the lower bound of the average transmission rate for user . Since is known by the supported modulation, we further scale the average transmission rate in terms of subcarriers, represented by . Given the per-link , the waiting time of the HOL packet , and the delay constraint , an active user's transmission priority and exact bandwidth requirement in terms of the number of subcarriers per symbol are obtained by the following modified LWDF scheduling algorithm.
From the above analyses, the user with the smaller value given by (10) will enjoy a higher transmission priority. From the definition of , if a user's required number of subcarriers exceeds the total number provided by a symbol, even if we allocate the whole symbol to this user, its delay requirement will not be met. Therefore, the HOL packet of this user will be dropped to save bandwidth for other users.
Up to now, our MAC-layer scheduling scheme gives the transmission priority list of the HOL packets according to (10) for the active users and their expected transmission rates in terms of the number of subcarriers in each symbol from (8). However these rates are only the users' expected rates. Considering the users' channel states, SNR requirements, and system power limit in the PHY layer, the real subcarrier allocation will be performed according to the following scheme.
3.2. PHY-Layer Resource Allocation Scheme
Initial subcarrier allocation. With the total number of subcarrier limit , we initially assign the users the required numbers of subcarriers according to their priorities till subcarriers are used up or all users are assigned.
Power minimization. Given a subcarrier allocation, the following modified Kuhn-Munkres algorithm is used to obtain an optimal allocation to minimize the system power under the users' SNR requirements. Denote the minimized power as .
- (c)Power comparison. Compare with the system power limit , and consider the following cases:
if , then the system power cannot support all currently assigned subcarriers. So our scheme will reduce the subcarrier allocation from the lowest priority user. Given requirement for user , among the assigned subcarriers for this user, the smaller the value of on subcarrier , the larger the power consumption on it. So the subcarrier reduction will be performed in ascending subcarrier gain order one by one. Then go to Step (b) in the next iteration, till the updated is less than ;
if , more power resource can be utilized. Then our scheme considers the remaining subcarrier resource. We represent the total number of the assigned subcarriers as . If , the subcarriers are used up, and we maintain the current solution. If , the remaining subcarriers are assigned evenly to the current active users till the updated reaches . If new users' access requirements are received, the access control scheme to be introduced in the next subsection will guide the assignment.
Modified Kuhn-Munkres Algorithm
In the following, we will firstly introduced the Kuhn-Munkres algorithm to find the perfect matching with the maximum sum of edge weights for a bipartite graph. Then a modified algorithm is described for OFDM power allocation. To minimize the system power, the modified algorithm is applied with negative weights.
A graph is denoted by , where is the vertex set, and is the edge set of the graph. If with and each edge in has one endpoint in and the other in , the graph is a bipartite graph, which can also be denoted as . The bipartite graph is very useful for some applications, such as an assignment problem which can be depicted as follows. Given a weighted complete bipartite graph , where edge has weight , find a matching from to with maximum weight. In an application, could be a set of workers, a set of jobs, and the earnings made by assigning worker to job . The goal of the assignment problem is to find the optimal (best total earnings) matching.
Let , be the bipartite sets. Initialize two labels and by , . In Figure 2 (b), the numbers written at the left and the top of the matrix express the values of and , respectively.
Obtain the excess matrix by the following: . This is shown in Figure 2 (c).
Find the subgraph that includes vertices and satisfying and the corresponding edge . Then find the maximum matching of by the Hungarian algorithm, and underline the entries in the weight matrix. (There are various ways to find the maximum matching. See, e.g, .) A maximum matching is a matching with the largest possible number of edges. In this example, the maximum matching is found to be , , and , as shown in Figure 2 (d). If is a perfect matching, that is, the number of edges in a maximum matching is equal to the cardinality of worker set ( ), the optimal assignment is obtained. Otherwise, go to the next step.
Let be a vertex cover of , and let and . The vertex cover is a vertex set of which contains at least one endpoint of each edge. In this example, is chosen to be the nodes corresponding to Workers 1 and 3 and Job 4. So corresponds to Workers 1 and 3, and corresponds to Job 4. Now find . For example, if equals 1 in Figure 2, decrease by for the rows of and increase by for the columns of . Then go to Step 2.
With the above problem formulation, the minimization of the system power as required in the second step of the PHY-layer allocation scheme may be converted to a bipartite matching problem. The edge weight for user on subcarrier is . Therefore, similar to the case illustrated in Figure 5, the modified Kuhn-Munkres algorithm may be applied to give an optimal solution to the minimization of the system power.
3.3. Access Control and Removal Scheme
In real networks, the number of active users changes dynamically. Without access control, the bandwidth may be inadequate. In addition, particularly for real-time traffic, without a removal scheme, not only may the QoS of the users newly granted access not be guaranteed but also the previously granted access users will suffer from QoS degradation. Therefore, the MAC-layer access control and removal schemes are introduced in our DSA-RT algorithm.
As introduced in Section 3.1, the new user's QoS requirements can be evaluated by . Access control will check if this requirement can be satisfied with the remaining power and subcarrier resources. If yes, the new user can be allocated subcarrier resources; otherwise, it continues to wait.
3.4. Implementation of DSA-RT
4. Simulation Results
In this section, the performance of the proposed DSA-RT scheduling algorithm is investigated and compared with CSD-RR, FEDD, and M-LWDF [2–4]. We consider QPSK modulation in multiuser OFDM downlink systems. However, other modulations are supported with different SNR constraints. The IFFT size is 128, and the OFDM symbol duration is equal to 200 microseconds . We consider the quasistatic flat fading channel with multipath . Assume that the users arrive as a Poisson process with parameter , and their active times in the system follow the exponential distribution with mean 10 seconds. In this section, we assume that all users have the same type of real-time traffic. During each user's active time, the packet arrivals follow the Poisson distribution. The packets have a fixed length of 1000 bytes, and the mean traffic rate is 1 Mbps. The delay bound is set to be 50 milliseconds. In simulations, we consider one type of real-time traffic, so we fixed the packet length. However, if multiple types of real-time traffics are supported, a variable length is acceptable in our algorithm. In our simulations, we vary the user arrival rates from 0.01 to 0.1 and compare the delay and dropping rate performance of some packet scheduling algorithms and our proposed DSA-RT algorithm. All simulations are in Matlab 7.3. The simulation time of each experiment is 100 seconds and we repeat it 100 times.
In this paper, DSA-RT aims to satisfy the packet delay requirements of real-time traffics in multiuser OFDM system, while maximizing the system bandwidth efficiency. This algorithm consists of two cooperative components. At the MAC layer, based on queuing theory and the modified LWDF algorithm, active users' expected transmission rates in terms of the number of subcarreirs per symbol and their corresponding transmission priorities are deduced. With different subcarrier states, based on our modified Kuhn-Munkres algorithm, a PHY-layer resource allocation scheme is developed to satisfy the users' requirements under the system SNR and power constraints. When considering a system where the number of active users changes dynamically, the access control and removal scheme can fully utilize the bandwidth resource and guarantee the QoS of the existing users in the system. Finally, compared with other widely used scheduling algorithms, simulation results show that our proposed algorithm significantly improves the system performance for real-time users in multiuser OFDM systems.
- Lawrey E: Multiuser OFDM. Proceedings of the International Symposium on Signal Processing and Its Applications (ISSPA '99), August 1999, Brisbane, Australia 761-764.Google Scholar
- Bhagwat P, Krishna A, Tripathi S: Enhancing throughput over wireless LAN's using channel state dependent packet scheduling. Proceedings of 17th Annual Joint Conference of the IEEE Computer and Communications Societie (INFOCOM '98), March 1998, San Francisco, Calif, USA 1103-1111.Google Scholar
- Shakkottai S, Srikant R: Scheduling real-time traffic with deadlines over a wireless channel. Wireless Networks 2002, 8(1):13-26. 10.1023/A:1012763307361View ArticleMATHGoogle Scholar
- Andrews M, Kumaran K, Ramanan K, Stolyar A, Whiting P, Vijayakumar R: Providing quality of service over a shared wireless link. IEEE Communications Magazine 2001, 39(2):150-153. 10.1109/35.900644View ArticleGoogle Scholar
- Zhang YJ, Liew SC: Link-adaptive largest-weighted-throughput packet scheduling for real-time traffics in wireless OFDM networks. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '05), November-December 2005, St. Louis, Mo, USA 5: 2490-2494.Google Scholar
- Cheong YYW, Tsui CY, Cheng RS, Letaief KB: A realtime sub-carrier allocation scheme for multiple access downlink OFDM transmission. Proceedings of the 49th IEEE Vehicular Technology Conference (VTC '99), September 1999, Amsterdam, The Netherlands 2: 1124-1128.Google Scholar
- Diao Z, Shen D, Li VOK: CPLD-PGPS scheduler in wireless OFDM systems. IEEE Transactions on Wireless Communications 2006, 5(10):2923-2931.View ArticleGoogle Scholar
- Munkres J: Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics 1957, 5(1):32-38. 10.1137/0105003MathSciNetView ArticleMATHGoogle Scholar
- Kuhn HW: The Hungarian method for the assignment problem. Naval Research Logistic Quarterly 1955, 2: 83-97. 10.1002/nav.3800020109View ArticleMathSciNetMATHGoogle Scholar
- Zhu J, Bing B, Li Y, Xu J: An adaptive subchannel allocation algorithm for OFDM-based wireless home networks. Proceedings of the 1st IEEE Consumer Communications and Networking Conference, (CCNC '04), January 2004, Las Vegas, Nev, USA 352-356.Google Scholar
- Bertsekas D, Gallager R: Data Networks. 2nd edition. Prentice-Hall, Englewood Cliffs, NJ, USA; 1992.MATHGoogle Scholar
- West DB: Introduction to Graph Theory. Prentice-Hall, Englewood Cliffs, NJ, USA; 2001.Google Scholar
- Kwon E, Kim S-G, Lee J: Overload control with removal algorithm for real-time flows in wireless networks. Proceedings of the IEEE 63rd Vehicular Technology Conference (VTC '06), May 2006, Melbourne, Australia 3: 1127-1131.Google Scholar
- Cai J, Shen X, Mark JW: Downlink resource management for packet transmission in OFDM wireless communication systems. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '03), December 2003, San Francisco, Calif, USA 6: 2999-3003.Google Scholar
- Xiao C, Zheng YR, Beaulieu NC: Second-order statistical properties of the WSS Jakes' fading channel simulator. IEEE Transactions on Communications 2002, 50(6):888-891. 10.1109/TCOMM.2002.1010606View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.