Predictive scheduling in multi-carrier wireless networks with link adaptation

Channel-aware scheduling and link adaptation methods are widely considered to be crucial for realizing high data rates in wireless networks. However, predicting the future channel states, and adjusting the transmission schedules and parameters accordingly, may consume valuable system resources, such as bandwidth, time, and power. This paper considers the trade-offs between the prediction quality and the throughput in a wireless network that uses link adaptation and channel-aware scheduling. In particular, we study the effects of the look-ahead window, i.e., the range of future time slots on which we have channel state estimates, and the reliability of the channel state estimates on the throughput. We develop an online scheduling algorithm for a multi-channel multi-user network that employs predictive link adaptation, and generalize it to incorporate imperfect channel state estimates. We use this heuristic together with performance hounds to the offline version of the problem to evaluate the performance with varying prediction quality. Our results suggest that it may be possible to reap most of the potential channel-aware scheduling benefits with a small look-ahead, and imperfect channel state estimates. Thus, a modest consumption of resources for channel prediction and link adaptation may result in a significant throughput improvement, with only marginal gains through further enhancement of the prediction quality. Our results can provide meaningful guidelines in deciding what level of system resource consumption is justified for channel quality estimation and link adaptation.