- Research Article
- Open Access
Throughput Analysis of Band-AMC Scheme in Broadband Wireless OFDMA System
© S. K. Kim and C. G. Kang. 2009
- Received: 1 August 2008
- Accepted: 23 February 2009
- Published: 8 March 2009
In broadband wireless Orthogonal Frequency Division Multiple Access (OFDMA) systems where a set of subcarriers are shared among multiple users, the overall system throughput can be improved by a band-AMC mode that assigns each suband, a set of contiguous subcarriers within a coherence bandwidth, to individual user with the better channel quality. As long as channel qualities for the subbands of all users are known a priori, multiuser and multiband gains can be simultaneously achieved with opportunistic scheduling. This paper presents an analytical means of evaluating the maximum system throughput for a bandadaptive modulation and coding (AMC) mode under the various system parameters. In particular, the practical features of resource management for OFDMA system are carefully modeled within the current analytical framework. Our numerical results demonstrate that band-AMC mode outperforms the diversity mode only by providing the channel qualities for a subset of good subbands, confirming the multiuser and multiband diversity gain that can be achieved by the band-AMC mode.
- Orthogonal Frequency Division Multiplex
- Diversity Mode
- Orthogonal Frequency Division Multiplex System
- System Throughput
- Orthogonal Frequency Division Multiple Access
Demands for high bandwidth multimedia information in the mobile environment have spawned the development of various mobile broadband wireless access (BWA) systems for high-speed communication. Particular examples include the mobile WiMAX, which is based on the IEEE 802.16e Mobile Wireless MAN technologies, and 3GPP's new standards for 3G long-term evolution (LTE). The IEEE 802.16e standard aims to unify the underlying solutions , specifying two flavors of OFDM systems: one simply identified as Orthogonal Frequency Division Multiplexing (OFDM), the other Orthogonal Frequency Division Multiple Access (OFDMA). OFDMA is considered to be one of the most spectrally efficient multiple access alternatives for mobile BWA systems. It has the ability to dynamically assign a subset of the subcarriers to individual users, attuning the technology to the particular mobility requirement. This scheme fully takes advantage of multiuser diversity, in conjunction with the frequency diversity inherent in the OFDM scheme. In fact, the mobile BWA system must contend with fluctuations across the frequency band, in addition to time variations. In the multiuser scenarios upon a multicarrier system, a subcarrier in deep fading to one user may be of good quality to another user, which lends support to dynamic subcarrier allocation on improving system throughput [2–5]. The different signal quality (e.g., carrier-to-interference ratio or CIR) seen at each subcarrier governs the capacity of each subcarrier. Ideally, a different modulation and coding level should be selected for each subcarrier in order to maximize the capacity. This particular approach is referred to as an adaptive modulation and coding (AMC) scheme.
For the fast selective AMC scheme, Channel Quality Indication (CQI) must be reported immediately for all subcarriers within the entire bandwidth, which allows for selecting the appropriate modulation and coding level for each subcarrier without incurring a channel mismatching problem. It usually involves unrealistic feedback overhead, especially under the fast fading channel. Fortunately, however, recent broadband measurements indicate that per-subcarrier information is typically not necessary . Namely, the feedback coefficient is sufficient for a group of several subcarriers in the fast selective AMC process, while the coherence bandwidth of the channel is larger than that of subband. In general, further enhancement can be realized by providing CQI reports a set of optimum subcarriers. This particular approach is specified as a band-AMC mode in IEEE 802.16 Task Group e standard. Due to the frequency-selective characteristics of a time-varying nature in the broadband channel, it is not straightforward to evaluate the average throughput of the OFDMA system, without resorting to the computer simulation. Furthermore, it becomes more involved as many parameters are configured to optimize system performance. For example, the number of bands selected for reporting CQI information is one important parameter that governs overall average system throughput.
The objective of this paper is to develop an analytical means of evaluating the maximum system throughput of band-AMC mode. In particular, practical features of resource management for the OFDMA system are carefully modeled within the proposed analytical framework. We consider order statistics to model the statistical nature of multiuser/multiband diversity in the OFDMA system. Order statistics have been a unique research area for statisticians for some time, with special application in statistical estimation. Recently, a more general case of order statistics has captured the attention of researchers in the area of signal processing and wireless communication systems [6, 7].
The remainder of this paper is organized as follows. In Section 2, the operational concept of band-AMC mode, is described, formulating the scheduling problem under consideration. In Section 3, the maximum average throughput of the band-AMC system is derived, using the order statistics. Then, Section 4 presents the numerical results to demonstrate the advantage of using the band-AMC mode with the sufficient number of CQI reports for the selection bands. Furthermore, the maximum throughput bounds that depend on the multi-user diversity and multiband effect are provided. Finally, concluding remarks are provided in Section 5.
2.1. Diversity Mode versus Band-AMC Mode
In the OFDMA system, all available subcarriers are shared by multiple users in each symbol, as opposed to the OFDM system where all subcarriers must be assigned to a single user. In general, the advantage of the OFDMA system is the multiuser diversity gain that can be obtained by selecting only good subcarriers for individual user, so as to fill the whole band with the multiple users. In other words, a "water-pouring" type of adaptive subcarrier and bit allocation algorithms can be evoked for maximizing system capacity . However, this involves reporting the channel quality indicator (CQI) for each subcarrier of every user. In practice, it may cost a prohibitive amount of overhead, especially under the fast fading channel condition in the mobile communication. Instead, a subset of subcarriers can be randomly selected in each symbol, which can warrant a frequency diversity effect over a frequency-selective fading channel. Toward this end, a subchannel is defined as a basic unit of resource allocation, which consists of a finite number of subcarriers, for example, 48 subcarriers in the IEEE 802.16e standard.
2.2. Multiple-Access Interference and CIR Distribution
where the is the distance separating the transmitter from the receiver and the subscript denotes the desired user and corresponds to the interfering cochannel users, is the loss at distance , and is a pathloss exponent that depends on the propagation environment.
If channel measurements are taken at a number of random locations, then the received amplitude typically follows a Rayleigh distribution. Assuming that instantaneous interference is constant, a carrier-to-interference ratio for each subband is shown to be exponentially distributed in a frequency nonselective channel. In particular, if is the mean value of the carrier-to-interference ratio at a specified distance from the transmitter, then the distribution of the observed carrier-to-interference ratio has the following probability density function :
2.3. Multi-User and Multi-Band Scheduling Problem
Note that each user experiences a varying channel quality for each band. Let be the carrier-to-interference ratio (CIR) of the band for the user . We assume that each user measures the CQI for all bands in terms of the CIR and then selects a preferred subset of bands with the -best CIR's for the CQI feedback. The partial CQI report reduces the feedback overhead cost while trading off the throughput performance. Some users may select the same band within the same time slot. Let denote a set of users who have chosen the band in their CQI reports in the same frame. We assume that the packet scheduler is designed to select a single user for each band so that the overall bandwidth utilization can be maximized, that is,
This particular scheduler, frequently referred to as a max C/I-scheduler, is one of the most typical opportunistic packet schedulers in the broadband wireless mobile systems.
In this section, the average throughput performance of the band-AMC system is evaluated. It is assumed that a full buffer traffic model is used, that is, infinite traffic waiting for each user. Depending on the channel quality, it is assumed that each user belongs to one of groups. The channel quality of all users in the same group is identically distributed. Let and represent the total bandwidth and coherence bandwidth, respectively. Then, the total number of independent subbands can be given approximately by . Note that the optimal number of subbands may be greater than or equal to . For example, it has been demonstrated in  that the optimum contribution to performance improvement is found for , where denotes the bandwidth of subband. Nevertheless, can be still fixed to the minimum number of independent subbands, that is, is just large enough to warrant the independence of channel qualities between the adjacent subbands. Determining a proper is beyond the scope of this paper.
Now let a vector represent the sampled values of a channel quality for user in group . Note that is not always necessarily equal to . Therefore, we consider two different cases: and For the case of there is no correlation between those samples, that is, the channel quality for each subband is independent of each other. Denoting as the expected value of CIR for band in group , then the following probability density function (PDF) for CIR of the corresponding band under the condition that can be obtained:
For the diversity channel, meanwhile, CIR for each user in group is given by taking average of CIRs for all subbands, that is, . In the case that are identically distributed over a whole bandwidth, turns out to be the normalized random variables.
The design of band-AMC system depends on the bandwidth of each subband, channel characteristics, the number of users served by band-AMC mode, the feedback overhead to report the CQI of subbands, and so on. Consider the situation that the bandwidth of subband, , chosen by band-AMC system is subject to the nonflat fading characteristics. This particular situation can be specified by for , which corresponds to the case of Then, the observed channel quality for each subband cannot be represented by (4). When the bandwidth is divided into several adjacent segments, each with the bandwidth of , it can be now approximated as , . Then, (4) is replaced with the following PDF:
where denotes the convolution operation: .
3.1. CQI Report for Band-AMC Mode
Suppose that every band-AMC user feedbacks -best CQI reports to the base station in every scheduling interval. To represent the chance that each subband is selected for feedback, define a band selection vector for user as follows:
where is the probability that user in group has a preference to the band within chances, that is, . In the case that samples in the subband are independent and identically distributed, it is obvious that However, consideration must be taken, that the dependence assumption is retained when the 's are no longer identically distributed, that is, for the inid case.
Let denote the CDF of -order statistics, exclusive of band within the entire band pool, where represents a band set of the system, that is, . Hence, the probability that the user in group selects the band is given by
The CDF of the -order statistic is generalized to
they prove the following relation:
where , and
Now from (7) and (9)–(12), the band selection vector can be directly determined. It is obvious that is obtained with , which corresponds to the case of full CQI feedback.
3.2. Maximum System Throughput in Band-AMC Mode
Let denote the total number of users in group . The probability that band is simultaneously selected by users can be written as follows:
Similarly, a vector is defined to represent the distribution of order statistics in the corresponding band j. By means of the max C/I-scheduling scheme, the received signal quality is then expressed as
Therefore, the CDF of the received CIR in band can be expressed as
When the existing cellular systems are considered, in which multi-path fading is dominant, the rate function of the Shannon type with the log-based linear relationship between rate and CIR may not be valid. In practice, a link-level simulation is performed in order to determine the required CIR for a given data rate, so as to meet the target frame error rate (FER). Let denote a set of MCS levels with the corresponding data rates , with the data rate for MCS level m defined by . To meet the given level of FER, a range of CIR is prescribed for each data rate . More specifically, the CIR required for is prescribed as . For the given target FER, the average system throughout of band is defined as follows:
Considering overall bandwidth, therefore, the average throughput of band-AMC system is provided by .
Basic OFDMA system parameters.
Number of data subcarriers
Number of symbols per frame
Downlink: 27 symbols
Uplink: 15 symbols
Convolutional turbo code
Number of subcarriers per subchannel
Number of subcarriers per CQI channel
Transmission modes for AMC.
CIR for 1% FER (dB)
Data rate* (kbps)
It is important to note that system throughput is dependent on not only the mean channel quality but also in the user distribution. In the current numerical analysis, we consider the scenarios with the mean channel qualities given by for and respectively. To impartially compare the performance according to various users' distributions, the mean channel quality on the same overall cases needs to be kept. Furthermore, it is assumed that while and 3, respectively.
As for it is observed from Figure 4 that not much multiuser gain can be achieved with the diversity mode. We note that band-AMC mode is almost always superior to the diversity mode, even with a very small number of band CQI reports, as long as there are sufficient number of users in the system. It is also found that the maximum multiuser and multiband diversity gain has been achieved by the band-AMC mode, corresponding to an increase in the average throughput of 2.54 Mbps.
In this paper, the maximum possible throughput of the band-AMC mode in the OFDMA system has been numerically evaluated using the order statistics for various system-level parameters, including the number of band CQI reports, the total number of available bands, and mean channel qualities. A conventional system-level simulation involves too much complexity associated with various physical parameters and thus the proposed analytical approach will be useful for dimensioning the system and configuring the optimal set of parameters. Our numerical results confirm the multiuser and multiband diversity gain that can be achieved by the band-AMC mode. It has been shown that the band-AMC mode outperforms the diversity mode only by providing the channel qualities for a subset of good subbands. Depending on the average CINR for each subband and how fast the channel varies for individual subband, for example, measured in terms of standard deviation of CINR for each subband, the band-AMC and diversity modes can be adaptively combined, so as to maximize the overall system throughput. Toward that end, the current analytical framework can be a useful basis for operation of the band-AMC mode under the varying traffic and CQI report constraints.
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