Conclusions

In this chapter, we have demonstrated that the simple maximum carrier-to-interference scheduling scheme can be a fair scheduler in the OFDMA system, although it is viewed as an unfair scheduling scheme in the single carrier TDMA/CDMA systems. Using this simple maximum C/I scheduling algorithm in the OFDMA system can exploit

multiuser diversity and frequency diversity thoroughly, thereby achieving both high throughput and good fairness performances. Moreover, using this simple maximum C/I scheduling algorithm can combat the worse channel effect and observe the good fairness performance in a multiuser OFDM system.

5 10 15 20 25 30 0

0.1 0.3 0.5 0.6 0.7 0.8 0.9 0.95 0.98 0.9998

# of subchannels

Fairness index

8 users 16 users 24 users 32 users

Fig. 3.5: Fairness index with the number of subchannels varying in different numbers of users when the IEEE 802.16 channel models are used.

5 10 15 20 25 30 0.8

0.95 0.96 0.99 0.995 0.996 0.997 0.998 0.999 0.9995

time

fairness index

Max C/I in SUI−1 Max C/I in SUI−5 PA in SUI−1 PA in SUI−5

Fig. 3.6: Comparison of fairness performance of dynamic sbucarrier allocation and power allocation (1T T I = 2048/6M Hz = 341µs)

0 5 10 15 20 25 30 35 1

1.02 1.04 1.06 1.08 1.1 1.12 1.14 1.16 1.18

time (TTI)

Normalized System Throughput

Max C/I in SUI−1 Max C/I in SUI−5 PA in SUI−1 PA in SUI−5

Fig. 3.7: Comparison of throughput performance of dynamic sbucarrier allocation and power allocation (1T T I = 2048/6M Hz = 341µs)

5 10 15 20 25 30 0.8

0.95 0.96 0.99 0.995 0.996 0.997 0.998 0.999 0.9995 0.9999

time

fairness index

Max C/I in SUI−1 Max C/I in SUI−5 Prop fair in SUI−1 Prop fair in SUI−5

Fig. 3.8: Comparison of fairness performance of max C/I and proportional scheduling (1T T I = 2048/6M Hz = 341µs)

0 5 10 15 20 25 30 35 0.9

0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.4

time (TTI)

Normalized System Throughput

Multicarrier Max C/I and Proportional Fair Scheduling Algorithms Simulation−−Throughput

Max C/I in SUI−1 Max C/I in SUI−5 Prop fair in SUI−1 Prop fair in SUI−5

Fig. 3.9: Comparison of throughput performance of max C/I and proportional scheduling (1T T I = 2048/6M Hz = 341µs)

Channel-aware Subcarrier Allocation and QoS Provisioning for OFDMA Systems

with Multi-type Traffic

The orthogonal frequency division multiple access (OFDMA) is becoming an impor-tant technique for the future wireless systems. Through parallel multi-carrier trans-missions, the inter-symbol interference (ISI) can be easily handled in transmitting high speed data. Furthermore, OFDMA systems bring a new dimension for allocat-ing radio resource - subcarrier. By exploitallocat-ing frequency diversity in the wide frequency spectrum, a suitable subcarrier allocation technique can further enhance throughput for the OFDMA system. This chapter addresses the issue of allocating subcarriers for providing both real-time and non-real-time traffic in the OFDMA system. We sug-gest a categorized subcarrier allocation (CSA) technique to improve throughput for non-real-time traffic, while satisfying the quality of service (QoS) requirement for the real-time method. In the proposed CSA technique, subcarriers are categorized into two groups based on their quality: good and fair. The real-time traffic will be assigned by the subcarrier with fair condition, while the non-real-time traffic will be assigned with good subcarriers. We find that such a subcarrier allocation method can apply the maximum carrier-to-interference (C/I) scheduling to maximize the throughput in good conditioned subcarriers, while the delay for the real-time traffic can be con-trolled by allocating enough fair-conditioned subcarrier through a queueing analytical method. Compared to other methods, such as dynamic subcarrier allocation (DSA)

and random subcarrier allocation (RSA), our results show that the CSA technique outperforms other methods in terms of throughput dropping probability and fairness performances.

4.1 Introduction

With the growing demand of high data rate communication, orthogonal frequency division multiple access (OFDMA) is becoming an important technology. OFDMA has been used in some broadband wireless systems, such as the IEEE 802.16a wire-less metropolitan area network (WMAN) [13] [20]. In single carrier systems, many scheduling schemes are discussed in [14–18,23]. Different from single carrier systems, the channel allocation in such multicarrier OFDMA systems has more dimensional consideration to support high-data-rate services. Besides, future wireless communi-cation networks are expected to support multi-type traffic, such as voice, video and data. Therefore, allocating radio resource to different types of services efficiently to meet quality of service (QoS) requirements of each service is an issue of concern. In [1], many conventional subcarrier allocation schemes, fixed subcarrier allocation (FSA), dynamic subcarrier allocation (DSA) and random subcarrier allocation (RSA), are listed to try to enhance the system performances of constant data rate services. Nev-ertheless, in single carrier systems, if the real-time user with higher priority enters the wireless networks, the non-real-time user will delay the transmission due to lower priority. However, in multicarrier systems, the real-time users can be served by the enough good subcarriers without delay and the non-real-time users use other good subcarriers to achieve the throughput requirements at the same time.

Good scheduling algorithms should have the following characteristics: (1) channel aware, (2) high throughput, (3) fair resource allocation and (4) achieving quality of service. There exist some scheduling algorithms discussed to assure QoS

requirements of different types of traffic in single carrier code division multiple ac-cess (CDMA) systems [2–6]. To provide both minimum service rate guarantees and dynamic channel bandwidth allocation to all users , generalized processor sharing (GPS) [7] [8] discipline is a scheduler candidate. In [2], the author employs fair queueing algorithm to minimize queueing delays in wireless networks. In [3] and [4], the author proposes a GPS based dynamic fair scheduling scheme, called code di-vision GPS (CDGPS) for wideband direct sequence code didi-vision multiple access (DS-CDMA) networks to support multi-type traffic. Furthermore, in [3], the author develops a credit-based CDGPS (C-CDGPS) to improve capacity by trading off short-term fairness. The CARR (channel-aware round robin) scheduler [5] utilizes channel information to increase system capacity and guarantees to allocate certain amount of time slots in an assignment round period in code division multiple access 2000 high data rate (CDMA2000 HDR) [9] or wideband code division multiple access high speed downlink packet access (WCDMA HSDPA) [10] systems. In [6], the idea of the FPLS (fair packet loss sharing) scheduling algorithm is to schedule the session of multimedia packets in the way that all the users share the packet loss fairly de-pending on their QoS requirements and to maximize the system capacity under the QoS constraints. However, in multicarrier systems, such as OFDM, if radio resource management makes use of the frequency diversity, the system performance can be improved. In [11], the author discusses the adaptive modulation and proposes dy-namic GPS (DGPS) scheduling for OFDM wireless communication systems, which exploits both multiuser diversity and frequency diversity. Yet, in [12], the proposed proportional rate adaptive optimization considers subcarrier and power allocation in the multiuser orthogonal frequency division multiplexing (MU-OFDM) system.

In this chapter, we develop a channel-aware and quality of service (QoS) provi-sioning scheduling subcarrier allocation algorithm, categorized subcarrier allocation (CSA), for the OFDMA systems. Frequency diversity inherently exists in OFDMA

systems, while multiuser diversity can be achieved by adopting scheduling algorithms.

Our proposed algorithm makes use of both diversity gains to support non-real-time service flows to achieve high throughput and considers the queueing analysis to al-locate the suitable amount of resource to the real-time service flows. Taking advan-tage of the specific characteristics of channels in OFDMA multicarrier environments, the proposed categorized subcarrier allocation (CSA) scheme can satisfy QoS delay constraint of real-time services and higher throughput requirements of non-real-time services at the same time. Moreover, the proposed subcarrier allocation algorithm can maintain good fairness performance in the multicarrier systems. As described above, we dynamically allocate subcarriers for of different types of service flows. This is a cross-layer design of radio resource management. Furthermore, it can be regarded as another form of water-pouring. We name it Service-oriented Water-pouring, which satisfy the QoS requirements of multi-type services, respectively. In addition, we manage the radio resource allocation from the viewpoint of users. In other words, it is the users that select the subcarriers that can assure their service-oriented QoS requirements.

In a multiuser wireless system, different users may have different channel re-sponses with respect to a time varying wireless channel. Thus, one user may view a channel as a bad channel, whereas the others may view it as a good channel. Con-sequently, for each channel, if the system can first pick a user with the best channel quality among a group of users and then deliver the service to this target user, the system capacity can be significantly improved. We call this capacity improvement as the multiuser diversity gain. However, in addition to multiuser diversity, we also make use of the correlation of subcarriers to efficiently allocate radio resource to real-time and non-real-time services, respectively.

The rest of this chapter is organized as follows. Section 4.2 introduces the quality of service (QoS) scheduling service specified in the IEEE 802.16 standard.

Section 4.3 describes the motivation. Section 4.4 formulates the problem. In Section 4.5, we explain our proposed a QoS provisioning subcarrier allocation method and describe the merit. Numerical results are given in Section 4.6. In Section 4.7, we give our concluding remarks.