3. Throughput and Fairness Enhancement for OFDMA Broadband Wireless Ac-
4.3 Motivation - Channel Characteristics of OFDMA Systems
To develop an efficient subcarrier allocation scheduling algorithm, we have to com-prehend the characteristic of the communication channel. At first, we assume that in the multicarrier environments, each subcarrier channel response to each user is independent. Therefore, we can exploit the multiuser diversity and frequency di-versity to enhance the system throughput and maintain good fairness performance.
Nevertheless, in the practical OFDMA multicarrier environments, we observe that the subcarrier channel responses have some relationship among different users. We describe the characteristic by Fig. 4.1. In Fig. 4.1, each subcarrier is judged for 12000 times. Each user gives the good mark to the first 682 best subcarriers, medium mark to the following 682 subcarriers and bad mark to the first worst 684 subcarriers among the total 2048 subcarriers. Fig. 4.1 (A) shows that for each subcarrier, how many users think it is good for himself. Fig. 4.1 (B) points how many users think the subcarriers medium while Fig. 4.1 (C) represents the number of users who think subcarriers bad.
Here, we illustrates the subcarrier correlation for users by Fig. 4.2. We only care the medium subcarriers and good subcarriers because the bad subcarriers are scheduled lastly. In Fig. 4.2, we observe that the ratio of the number of good users to the number of medium plus good uers for each subcarrierfar far away 0.5 is in the majority, which means that for the same subcarrier, the most part of users regard it as the same rank, good, medium or bad. Of course there are exceptions. But the
exceptions is minor. From Fig. 4.2, we can find that the number of subcarriers whose ratio is less than 0.2 or greater than 0.8 is 82.6% of all subcarriers. The fact shows that the subcarriers are correlated among users. We will make use of the characteristic of the OFDMA channel to design our subcarrier allocation scheduling algorithm.
In the following, we see Fig. 4.3 and Fig. 4.4. The two figures show that the distribution different opinions to each subcarrier. Figure 4.3 is the summation of Fig.
4.1 (A), (B) and (C). We can find that for the most part of subcarriers, opinion of users on them are almost the same. We take some examples from Fig. 4.4 which is selected a section of 4.3 for the sake of clear observation. For the 2001st subcarrier, there are 68 users regarding it as a good subcarrier, while there are 11100 and 832 users seeing it as a medium and bad subcarrier, respectively. Taking another example, for the 2018th subcarrier, 11618 users regarding it as a good subcarrier, while 382 and 0 users seeing it as a medium and bad subcarrier. These two subcarriers have common consensus of users. However, there are still subcarriers with different opinions. For instance, 6296 users regard the 2008th subcarrier as a good subcarrier while 5699 users see it as a medium subcarrier and 5 users think it good. Nevertheless, this kind of subcarriers is minor that the fact can be observed by Fig. 4.2. Based on the characteristics of the channel, we will design a useful subcarrier allocation algorithm for OFDMA systems.
4.4 Problem Formulation
In the third-generation and beyond or the future communication systems, there will be a mixture of different traffic classes. Therefore, what is the suitable ratio of real-time service and non-real-real-time service resource allocation is a research topic. How to utilize the limited radio resource for various types of service flows with different QoS requirements is a very important issue.
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Fig. 4.1: (a) The number of users that judge the subcarrier is good for each subcarrier; (b) The number of users that judge the subcarrier is medium for each subcarrier; (c) The number of users that judge the subcarrier is bad for each subcarrier.
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Fig. 4.2: The cumulative number of subcarriers of different ratios, which is the number of good users to number of medium plus good uers for each subcarrier.
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Fig. 4.3: The stack presentation of OFDMA channel characterisitcs
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Fig. 4.4: The stack presentation of parts of the OFDMA channel characterisitcs. We take from the 2001th to 2020th subcarriers for examples
In the integrated service networks, we initially divide services into two parts, real-time voice and non-real-time data services. The demand of real-time voice service is that the delay can not exceed a certain threshold Wth. On the contrary, the non-real-time data service is tolerant of delay, but takes best efforts to achieve the maximum throughput. Consequently, the goal is to achieve the maximum throughput of non-real-time data users under QoS constraints of non-real-time users. We mathematically formulate this problem by the following equations.
maxρn,m
where n is the user index, m is the subcarrier index. nrt means the number of real-time users, while mrt is the number of real-time subcarriers. N is the number of total users while M is the number of total subcarriers. ρn,m means whether the m − th subcarrier is assigned to the n − th user. ρn,m = 1 means the m − th subcarrier is assigned to the n − th user while ρn,m = 0 means the m − th subcarrier is not assigned to the n − th user. hn,m is the m − th subcarrier channel response to the n − th user.
N0 is the noise spectral density and B is the total bandwidth. Wrtis the waiting time of real-time service while Wth is the delay constraint.
We would like to maximize the throughput of non-real-time users described by (4.1). The constraint (4.2) represents whether the m − th subcarrier is assigned to the n − th user. The constraint (4.3) means that each subcarrier is allocated to the only one user. In (4.4), (M − mrt) mod (N − nrt) = r 6= 0, there are r users allocated bM −mN −nrtrtc + 1 subcarriers, and M − mrt− r users allocated bM −mN −nrtrtc subcarriers. In (4.5), Wrt is calculated by queueing analysis to find the suitable number of real-time subcarriers.