Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
A framework for opportunistic scheduling in wireless networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Opportunistic beamforming using dumb antennas
IEEE Transactions on Information Theory
CDMA/HDR: a bandwidth efficient high speed wireless data service for nomadic users
IEEE Communications Magazine
Combining opportunistic and size-based scheduling in wireless systems
Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems
Cross-layer channel-aware approaches for modern wireless networks
MACOM'10 Proceedings of the Third international conference on Multiple access communications
Leveraging dynamic spare capacity in wireless systems to conserve mobile terminals' energy
IEEE/ACM Transactions on Networking (TON)
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We study the performance of an opportunistic scheduling scheme maximum quantile scheduling, i.e., scheduling a user whose current rate is in the highest quantile relative to its current rate distribution, in a wireless system. In a practical scenario it is unlikely that users' rate distributions are known at the scheduler, and have to be estimated via measurement. Under the assumption of fast fading, we prove a bound on the relative penalty associated with such estimates, showing that number of independent samples need only grow linearly with the number of active users. This is a fairly limited cost, suggesting one could track distributional changes in users' channels. By contrast other opportunistic scheduling schemes require estimating or setting weights/thresholds that implicitly depend not only on the number of users, but also their rate distributions, and possibly their traffic characteristics. In other words the penalty associated with tuning weights for other schemes can be higher than that associated with estimating users' rate distributions for maximum quantile scheduling. This statement is supported by our simulation results. Furthermore we prove that if rates are bounded and number of users is high, maximum quantile scheduling is sum average throughput maximizing subject to temporal fairness.