1. Introduction
1.2 Thesis Outline
The research of this thesis investigates how to exploit frequency diversity and mul-tiuser diversity by scheduling and subcarrier allocation in the multicarrier systems.
We first demonstrate that the simple maximum carrier to interference ratio (C/I) scheduling can both enhance system throughput and maintain fairness performances for the orthogonal frequency division multiple access (OFDMA) system. Furthermore, we develop a new quality of service (QoS) provisioning subcarrier allocation algorithm used in the orthogonal frequency division multiple access (OFDMA) system.
The remaining chapters of this thesis are organized as follows. Chapter 2 in-troduces the background of the IEEE 802.16a wireless metropolitan area network (WMAN) and some scheduling techniques in the single carrier systems. Further-more, we discuss the subcarrier allocation approaches in the multicarrier systems and quantitative measure of fairness. Chapter 3 demonstrates that the simple maximum carrier to interference ratio (C/I) scheduling can both enhance system throughput and maintain fairness performances by exploiting frequency diversity and multiuser diversity in the OFDMA system. Chapter 4 develops a subcarrier allocation scheme that supports quality of service (QoS) requirements of multiple types of services with considering characteristics of channel. At last, Chapter 5 gives the concluding remarks and suggestions for future work.
Background
In this chapter, we introduce the background of the IEEE 802.16 WMAN and some existed resource allocation schemes.
2.1 IEEE 802.16 WMAN
In [13], an OFDMA based wireless metropolitan area network (WMAN) is specified in the IEEE 802.16a standard. In a none line of sight (NLOS) environment, WMAN in the IEEE 802.16a specification is recommended to operate in a multicarrier modu-lation mode. Each OFDMA symbol consists of various types of subcarriers, including data, pilot and null. The number of the total subcarriers is 2048, which is equal to the fast Fourier transform (FFT) size.
As shown in Fig 2.1, the carriers are grouped into subsets of carriers, which is called a subchannel. Each carrier of a subchannel is not adjacent, which can mitigate the effect of deep fading by exploiting frequency diversity.
IEEE 802.16 provides two frequency bands. One is IEEE 802.16 and the other is IEEE 802.16a [13]. The former is applied in a line of sight (LOS) The latter is operated in the frequency band of 2 to 11 GHz. In none line of sight (NLOS) channel, Table 2.1 describes the difference between IEEE 802.16 and 802.16a.
The carrier allocation condition of the uplink and the downlink in IEEE 802.16a are showed in Table 2.2 and Table 2.3. Considering downlink case, there are 173 left and 172 right guard carriers, respectively. One dc carrier and 166 pilots.
Actually, only 1536 carriers are used for data transmiision. On the other hand, in
Fig. 2.1: OFDMA carrier allocation diagram
Tab. 2.1: Air Interface Nomenclature
Destination Applicability PHY MAC Duplexing
WirelessMAN-SC 10-66GHz SC Basic TDD,FDD,HFDD
WirelessMAN-SCa 2-11GHz SCa Basic,ARQ,STC,AAS TDD,FDD,HFDD WirelessMAN-OFDM 2-11GHz OFDM Basic,AAS,ARQ,Mesh,STC TDD,FDD,HFDD WirelessMAN-OFDMA 2-11GHz OFDMA Basic,ARQ,STC,AAS TDD,FDD,HFDD
uplink case, there are 176 left and 175 right guard carriers and one dc carriers. The remaining carriers are divided into 32 subchannels. Each subchannel has 53 carriers, including 48 data carriers and 5 pilots. Table 2.4 shows frequency spacing in different definition of guard time Tg in Multichannel Multipoint Distributed Service (MMDS) band (from 2.1 to 2.7 GHz). We will adopt this to model our channel and describe the corresponding channel delay profile.
The way of allocating subcarriers is based on the following formula.
carrier(n, s) = Nsubchannels· n + {ps[nmod(Nsubchannels)] +
IDcell· ceil[(n + 1)/Nsubchannels]}mod(Nsubchannels) (2.1)
where
carrier(n, s) = carrier index of carrier n in subchannel s.
s = index number of a subchannel from the set [0,1,...,Nsubchannels-1].
n = carrier-in-subchannel index from the set [0,1,...,Nsubcarriers-1].
Nsubchannels= number of subchannels.
ps[j] = the series obtained by rotating {P ermutationBase0} cyclically to the left s times.
ceil[ ] = function which rounds its argument up to the next integer.
IDcell = a positive integer assigned by the MAC to identify this particular BS sector.
Xmod(k) = the remainder of the quotient X/k (which is at most k-1).
We take an uplink example to illustrate the use of this formula. The subcarriers for subchannel s = 1 in cell IDcell = 2 are computed. The number of subchannels Nsubchannels = 32, while the number of carriers in each subchannel Nsubcarriers = 53, and the number of data carriers in each subchannel is 48.
P ermutationBase0 = {3, 18, 2, 8, 16, 10, 11, 15, 26, 22, 6, 9, 27, 20, 25, 1, 29, 7, 21, 5, 28, 31, 23, 17, 4, 24, 0, 13, 12, 19, 14, 30}.
Using equation (2.1),
1. The basic series of 32 numbers is {3, 18, 2, 8, 16, 10, 11, 15, 26, 22, 6, 9, 27, 20, 25, 1, 29, 7, 21, 5, 28, 31, 23, 17, 4, 24, 0, 13, 12, 19, 14, 30}
2. In order to get 32 different permutation the series is rotated to the left (from no ro-tation at all up to 31 roro-tations). Since we have assumed s=1, (permuro-tationbases=1) is: {18, 2, 8, 16, 10, 11, 15, 26, 22, 6, 9, 27, 20, 25, 1, 29, 7, 21, 5, 28, 31, 23, 17, 4, 24, 0, 13, 12, 19, 14, 30, 3}
3. We repeat the permuted series 2 times and take the first 53 numbers only: {18, 2, 8, 16, 10, 11, 15, 26, 22, 6, 9, 27, 20, 25, 1, 29, 7, 21, 5, 28, 31, 23, 17, 4, 24, 0,
13, 12, 19, 14, 30, 3, 18, 2, 8, 16, 10, 11, 15, 26, 22, 6, 9, 27, 20, 25, 1, 29, 7, 21, 5, 28, 31, 23, 17, 4, 24, 0, 13, 12, 19, 14, 30, 3}.
4. The concatenation depends on the IDcell (which characterizes the working cell and can range from 0 to 31). Since we have assumed s = 1 and IDcell = 2, the last term in the equation becomes
ps[nmod(32)] + 2 · ceil[(n + 1)/32]mod(32) with n = 0,1, ..., 52
= {20, 4, 10, 18, 12, 13, 17, 28, 24, 8, 11, 29, 22, 27, 3, 31, 9, 23, 7, 30, 1, 25, 19, 6, 26, 2, 15, 14, 21, 16, 0, 5, 22, 6, 12, 20, 14, 15, 19, 30, 26, 10, 13, 31, 24, 29, 5, 1, 11, 25, 9, 0, 3}
5. Finally adding in the first term, the set of carriers is found: carrier(n, 1) = {20, 36, 74, 114, 140, 173, 209, 252, 280, 296, 331, 381, 406, 443, 451, 511, 521, 567, 583, 638, 641, 697, 723, 742, 794, 802, 847, 878, 917, 944, 960, 997, 1046, 1062, 1100, 1140, 1166, 1199, 1235, 1278, 1306, 1322, 1357, 1407, 1432, 1469, 1477, 1505, 1547, 1593, 1609, 1632, 1667}
2.2 Scheduling Techniques
Many scheduling algorithms have been developed for the single carrier code division multiple access (CDMA) system [14–18]. First, the maximum C/I scheduling scheme allocates the channel to the user J that has the best current channel condition.
J = arg{max
i ri(t)} , (2.2)
where J is the scheduled user, i is the user index and ri is the channel response of the user i, while t indicates time. This scheduling algorithm can exploit multiuser diversity at the expense of the fairness performance to some other users. Second, the
round-robin scheduling approach allocates resource to each user periodically, which can provide the best fairness performance, but has poor throughput because it does not take the channel information into account. We express this algorithm mathemat-ically as follow.
J = arg{max
i di(t)} , (2.3)
where di is the delay of user i. Third, the proportional fair scheduling algorithm [15]
was proposed to use the ratio of the short-term channel response to the long-term channel condition of each user as a criterion to allocate the resource. We describe the principle of the proportional fair scheduling by (2.4).
J = arg{max
i (ri(t)
ri(t))} , (2.4)
where ri(t) is the long-term channel response of user i while ri(t) is the short-term channel response of user i. Last, the exponential rule scheduling policy [16–18] further considers the service delay of each user. If the user has waited for a long time, this user will be allocated a channel with a higher priority depending on (2.5).
J = arg{max
where d(t) is the average delay of all users. By (2.5), we can see the exponential term dominates that means the delay of each user can determine the priority of each user. These wireless scheduling algorithms were only evaluated in the single carrier wireless system. To our knowledge, how these scheduling algorithms perform in the multi-carrier OFDMA system is an open issue.
2.3 Subcarrier Allocation Strategies
In this section, we introduce three conventional multicarrier allocation (MCA) [1]
schemes as follows.
2.3.1 Fixed Subcarrier Allocation (FSA)
Fixed subcarrier allocation means that we allocate the fixed sets of subcarriers to certain users whether the subcarriers is good or bad for these users. In other words, users use a certain set of subcarriers all the time. This scheme does not exploit multiuser diversity and frequency diversity at all. Therefore, FSA scheme is regarded as a simple but inefficient subcarrier allocation scheme.
2.3.2 Random Subcarrier Allocation (RSA)
In each time slot, users randomly select sets of subcarriers for transmission when using random subcarrier allocation (RSA) scheme. This scheme makes use of frequency diversity to avoid some unfavorable condition. If a user select a set of bad subcarriers in a time slot, he may select another better set of subcarriers for communication and does not suffer from bad communication environments all the time. In short, the RSA scheme exploit the frequency diversity to provide fairer resource allocation and this scheme do not know any channel state information.
2.3.3 Dynamic Subcarrier Allocation (DSA)
In order to enhance the system throughput performance, we prefer that each sub-carrier is allocated to the user that the subsub-carrier channel response is the best to the user among all users in each time slot. Nevertheless, it will cause unfair re-source allocation. Therefore, we allocate the rere-source from the viewpoint of users.
Furthermore, the wireless communication environments are time-varying. Hence, dynamically allocating subcarriers according to channel condition can improve the throughput performance while this resource allocation approach is called dynamic subcarrier allocation (DSA) scheme. The DSA scheme is similar to the maximum C/I (carrier-to-interference-plus-noise ratio) scheduling used in single carrier CDMA system [14] [18]. This scheme exploits both frequency diversity and multiuser diver-sity. We describe the subcarrier allocation policy as follows.
First, we define a channel matrix H in the OFDMA environment. Assume that there are N users and M subcarriers in the system.
H =
where hn,mrepresents the m−th subcarrier condition to the n−th user. For example, h3,2 means the response of subcarrier 2 observed by user 3. The DSA scheme operates as the following procedure.
1. Give each user a priority number.
2. According to the priority of each user, the user n selects his own favorite subcar-riers for any user in order.
3. Users with lower priority do not select the selected subcarriers of the users with higher priority and they can only select the rest subcarriers.
Due to the multiuser environments, if the channel responses are independent of users, which makes the multiuser diversity exist to improve system throughput.
2.4 Quantitative Measure of Fairness
Fairness consideration is very important in the field of radio resource management.
We should show the how fair resource allocation is among all users numerically. How-ever, there are many fairness measures proposed to indicate the fairness level of re-source allocation. We describe those fairness indices as follows. Define xithe resource quantity allocated to user i, and n the number of total users.
Variance
Coef f icient of V ariation (COV ) = V ariance
Mean (2.8)
As above, all the indices can represent the degree of fairness of resource allo-cation among users. Nevertheless, in [19], we desire that the fairness index has the following properties.
(a) Population size independence: The index should be suitable for infinite or finite users. The above four indices all satisfy the requirement.
(b) Scale and metric independence: We expect that the indices are indepen-dent of scale. In other words, if users are allocated ten times quantity of resource simultaneously, the indices should be the same values. However, the variance index does not meet the goal.
(c) Boundedness: In addition to population size independence and scale in-dependence, the indices are desired to be bounded between 0 and 1. Therefore, we can judge the policy of resource allocation fair according whether the fairness index approaches 1 or not. The COV (coefficient of variation) is not bounded. Its value distributes from 0 to infinity.
(d) Continuity: The continuous index can respond the little change in allo-cation way. The min-max ratio index only take the users with best resource and with worst resource into account. Therefore, if the allocation changes among medium users, the min-max ratio index does not change.
To sum up, we observe the behaviors of the indices. We find that Jain’s fairness index satisfies all the desired properties. Consequently, we adopt the index as one of our simulation performance metric.
Tab. 2.2: OFDMA downlink subcarriers allocation
Parameters Value
Number of DC carriers 1
Number of Guard Carriers: Left, Right 173,172
Number of Used Carriers 1702
Nused 1702
Total Number of Carriers 2048
NvarLocP ilots 142
Number of Fixed-location Pilots 32
Number of Variable-Location Pilots which 8
coincide with Fixed-Location Pilots
Total Number of Pilots 166
Number of data carriers 1536
Nsubchannels 32
Nsubcarriers 48
Number of data carriers per subchannel 48
BasicFixedLocationPilots {0,39,261,330,342,351,522,636,645,651,708,726, 756,792,849,855,918,1017,1143,1155,1158,1185, 1206,1260,1407,1419,1428,1461,1530,1545,1572,
1701}
{P ermutationBase0} {3,18,2,8,16,10,11,15,26,22,6,9,27,20,25,1,29 7,21,5,28,31,23,17,4,24,0,13,12,19,14,30}
Tab. 2.3: OFDMA uplink subcarriers allocation
Parameters Value
Number of DC carriers 1
Nused 1696
Number of Guard Carriers: Left, Right 176,175
Nsubchannels 32
Nsubcarriers 53
Number of data carriers per subchannel 48
{P ermutationBase0} {3,18,2,8,16,10,11,15,26,22,6,9,27,20,25,1, 29,7,21,5,28,31,23,17,4,24,0,13,12,19,14,
30}
Tab. 2.4: MMDS band frequency spacing (fs/BW = 8/7) OFDMA(NF F T = 2048)
T
g(µ
s)
BW(MHz) ∆f (kHz) T
b(µ
s) T
b/32 T
b/16 T
b/8 T
b/4
1.5
36431194
2337
1374
23149
13298
233.0 1
6089597
1318
2337
1374
23149
136.0 3
238298
239
1318
2337
1374
2312.0 6
3956149
134
239
1318
2337
1324.0 13
112874
232
134
239
1318
23Throughput and Fairness Enhancement for OFDMA Broadband Wireless Access
Systems Using the Maximum C/I Scheduling
In this chapter, we demonstrate that the simple maximum carrier to interference ratio (C/I) scheduling can both enhance system throughput and maintain fairness perfor-mances for the orthogonal frequency division multiple access (OFDMA) system. The maximum C/I scheduling has long been recognized as an effective method to enhance throughput, but it is viewed as an unfair scheduling policy in the the single carrier code division multiple access (CDMA) system. We reassess the fairness performance of the maximum C/I scheduling in the context of the multi-carrier OFDMA system.
Through analysis and simulations, we find that the maximum C/I scheduling is indeed an fair scheduling for OFDMA systems. Thus, with respect to the OFDMA system, we develop a maximum C/I scheduling based resource allocation algorithm. Our re-sults show that the fairness of the maximum C/I scheduling in OFDMA systems is comparable to that of the proportional fair scheduling scheme. In short, we conclude that in the OFDMA system, the maximum C/I scheduling not only can maximize system throughput, but simultaneously maintain very good fairness performance.
3.1 Introduction
With the growing demand for high data rate communication, orthogonal frequency division multiple access (OFDMA) is becoming an important technology. OFDMA has been used in many broadband wireless systems, such as the IEEE 802.16a wireless metropolitan area network (WMAN) [13] [20]. This chapter investigates the benefits of OFDMA systems from both frequency diversity and multiuser diversity perspec-tives. Frequency diversity inherently exists in OFDMA systems, while multiuser diversity can be achieved by adopting scheduling algorithms. Although both diver-sity gains can enhance the system throughput, the challenging issue here is how to select a scheduling algorithm that can achieve high system throughput and maintain the fairness among users simultaneously.
OFDMA is not only a modulation scheme but also a multiple access technology.
In an OFDMA system, each user is allocated a set of orthogonal subcarriers. In addition to overcoming the inter-symbol interference (ISI), an OFDMA system can also mitigate the multiple access interference (MAI) due to the orthogonality among subcarriers. Moreover, it can result in frequency diversity benefit with interleaving and channel coding. To further take advantage of frequency diversity [21–25], many adaptive resource allocation techniques were suggested from a view point of subcarrier power allocation [12] [26].
The goal of this chapter is to investigate the OFDMA system from another resource allocation viewpoint, i.e, scheduling algorithms. Wireless scheduling tech-niques are developed to exploit the multiuser diversity. In a multiuser wireless system, different users may have different channel responses in a time varying wireless chan-nel. Thus, a channel may be viewed as a bad channel, but may be viewed as a good channel by other users. Consequently, if the system can first pick a user with the best channel quality among a group of users to serve in each channel , the system capacity
can be improved significantly. We call this capacity improvement as the multiuser diversity gain. Clearly, for providing delay-tolerant data services, wireless scheduling is an inevitable technique to exploit multiuser diversity which inherently exists in the multiuser system.
Many scheduling algorithms have been developed for the single carrier time division multiple access (TDMA) or code division multiple access (CDMA) systems [14–18]. First, the maximum C/I scheduling scheme allocates the channel to the user that has the best channel condition [14]. This scheduling algorithm can fully exploit multiuser diversity at the expense of sacrificing the fairness performance for other users. Second, the round-robin scheduling approach allocates resource to each user periodically, which can provide the best fairness performance, but has lowest throughput because it does not take the channel information into account. Third, the proportional fair scheduling algorithm [15] was proposed to use the ratio of the short-term channel response to the long-short-term channel condition of each user to allocate the resource. Last, the exponential rule scheduling method [16–18] further considers the service delay of each user. If the user has waited for a long period of time, this user will be allocated a channel with a higher priority. These wireless scheduling algorithms were only evaluated in the single carrier wireless system. To our knowledge, how these resource management algorithms perform in the multi-carrier OFDMA system is an open issue.
There were a lot of dynamic radio resource management technologies in OFDM based multicarrier systems discussed in the literature. In traditional wired dis-crete multitone asymmetric digital subscriber lines (ADSL), a resource management method named water-filling power allocation [27] is popularly used. Therefore, a lot of papers [21] [23] [24] adopted this rule to solve the optimization problem that to maximize the system capacity under the total power constraint.
In this chapter, we first assess the fairness performance of the maximum C/I
scheduling in the multi-carrier OFDMA system. The maximum C/I scheduling has long been recognized as an effective method to enhance throughput, but it is also viewed as an unfair scheduling policy in the the single carrier CDMA system. Through analysis and simulations, we will find that the maximum C/I scheduling is indeed an fair scheduling for OFDMA systems. Thus, with respect to the OFDMA system, we develop a maximum C/I scheduling based resource allocation algorithm. We will show that the fairness of the maximum C/I scheduling in OFDMA systems is comparable to that of the proportional fair scheduling scheme. Hence, we conclude that the simple maximum C/I scheduling can enhance both system throughput and fairness performances for the OFDMA system.
The rest of this chapter is organized as follows. Section 3.2 introduces the channel models for an OFDMA based IEEE 802.16a system. Section 3.3 formulates this problem. In Section 3.4, we analyze the system throughput performance with the maximum C/I scheduling algorithm in the multicarrier systems. Section 3.5 introduces the current two resource allocation strategies. Simulation results are given in Section 3.6. We give our concluding remarks in Section 3.7.
3.2 Channel Models for the IEEE 802.16a System
We will introduce more complicated but practical channel models specified in the IEEE 802.16a WMAN standard [28]. There are six typical Stanford University In-terim (SUI) channel models for three types of terrains. These SUI channels are used for the fixed broadband wireless applications (BWA) in the multichannel multipoint distributed service (MMDS) band. We will use the two SUI channel models, SUI-1 and SUI-5, in our simulations. Parameters in the two SUI channels are summarized in Tables 3.1 and 3.2.
SUI-1 channel model is for low mobility with small delay spread, which is close
Tab. 3.1: SUI-1 Channel
Tap 1 Tap 2 Tap 3 Units
Delay 0 0.4 0.8 µs
power (omni. ant) 0 -15 -20 dB
power (30◦ antenna) 0 -21 -32 dB
K Factor 18 0 0
Maximum Doppler frequency 0.4 0.4 0.4 Hz
Tab. 3.2: SUI-5 Channel
Tap 1 Tap 2 Tap 3 Units
Delay 0 5 10 µs
power (omni. ant) 0 -5 -10 dB
power (30◦ antenna) 0 -11 -22 dB
K Factor 0 0 0
Maximum Doppler frequency 2 2 2 Hz
to Rician fading. On the other hand, SUI-5 is close to Rayleigh fading channel and it is exposed severe multipath fading effect. Moreover, the channel response value “1”
is defined to be the state that received signal-to-noise ratio (SNR) can be satisfied. If the value is above 1, the channel is in good condition. In our simulation, we evaluate the system capacity by using the QPSK with coding rate 1/2 case [13]. The channel response “1” corresponds to the received SNR 9.4 dB. The receiver with SNR values in Table 3.3 can achieve BER less than 10−6.
Tab. 3.3: Receiver SNR and Eb/N0 assumptions Modulation coding rate Receiver SNR
QPSK 1/2 9.4
QPSK 3/4 11.2
16QAM 1/2 16.4
16QAM 3/4 18.2
64QAM 2/3 22.7
64QAM 3/4 24.4
3.3 Problem Description
3.3.1 Two-state Random Channel Matrix
A simple channel model is adopted to describe the impact of the number of subchan-nels when using the maximum C/I scheduling algorithm. We assume that there are N users requiring the same data rate. Each subchannel has two states: good and bad [29]. Good state means that the channel could bear 1 + δ times of the required rate rate, while the bad state means that the channel only could transmit 1 − δ times of the normal data rate. We also assume that the channel condition on which each user observed is independent. In other words, the same channel may be viewed as a good channel for a user, but a bad one for others.
As described above, an arbitrary subchannel may have different states for