• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4A
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• pp.332-340
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• 2010

In this paper, we deal with multi-user diversity scheduling methods that transmit simultaneously signals from multiple users using superposition coding multiplexing. These methods can make various scheduling methods be obtained, according to strategies for user selection priority from the first user to the first-following users, strategies for per-user power allocation, and resulting combining strategies. For the first user selection, we consider three strategies such as 1) higher priority for a user with a better channel state, 2) following the proportional fair scheduling (PFS) priority, 3) higher priority for a user with a lower average serving rate. For selection of the first-following users, we consider the identical strategies for the first user selection. However, in the second strategy, we can decide user priorities according to the original PFS ordering, or only once an additional user for power allocation according to the PFS criterion by considering a residual power and inter-user interference. In the strategies for power allocation, we consider two strategies as follows. In the first strategy, it allocates a power to provide a permissible per-user maximum rate. In the second strategy, it allocates a power to provide a required per-user minimum rate, and then it reallocates the residual power to respective users with a rate greater than the required minimum and less than the permissible maximum. We consider three directions for scheduling such as maximizing the sum rate, maximizing the fairness, and maximizing the sum rate while maintaining the PFS fairness. We select the max CIR, max-min fair, and PF scheduling methods as their corresponding reference methods [1 and references therein], and then we choose candidate scheduling methods which performances are similar to or better than those of the corresponding reference methods in terms of the sum rate or the fairness while being better than their corresponding performances in terms of the alternative metric (fairness or sum rate). Through computer simulations, we evaluate the sum rate and Jain’s fairness index (JFI) performances of various scheduling methods according to the number of users.