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Traditional scheduling algorithms are focused in maximizing system throughput considering a grade of fairness. However, maximizing the system throughput does not necessarily result in maximizing the number of satisfied users (users with a packet delay below a threshold). More over, the throughput maximization could cause a low grade of fairness. In this paper, we propose a low complexity scheduling algorithm that improves fairness and maximizes the number of satisfied users in the system. The main idea of this algorithm is to find the optimum data transmission rate for each user, based on their statistical channel variations and their required qualities of service (QoS). The algorithm dynamically adapts to the optimum data transmission rate for each user according to their channel variations. We consider a channel gain that reflects the effects of shadowing and multipath fading. The results show that, with this algorithm, the system capacity is increased 10% and the throughput for user in bad channel conditions increased 42% over the Modified Largest Weighted Delay first (M-LWDF) scheduler.