Scheduling With Limited Information In Wireless Systems

Opportunistic scheduling is a key mechanism for improving the performance of wireless systems. However, this mechanism requires that transmitters are aware of channel conditions (or CSI, Channel State Information) to the various possible receivers. CSI is not automatically available at the transmitters, rather it has to be acquired. Acquiring CSI consumes resources, and only the remaining resources can be used for actual data transmissions. We explore the resulting trade-off between acquiring CSI and exploiting channel diversity to the various receivers. Specifically, we consider a system consisting of a transmitter and a fixed number of receivers/users. An infinite buffer is associated to each receiver, and packets arrive in this buffer according to some stochastic process with fixed intensity. We study the impact of limited channel information on the stability of the system. We characterize its stability region, and show that an adaptive queue length-based policy can achieve stability whenever doing so is possible. We formulate a Markov Decision Process problem to characterize this queue length-based policy. In certain specific and yet relevant cases, we explicitly compute the optimal policy. In general case, we provide a scheduling policy that achieves a fixed fraction of the system's stability region. Scheduling with limited information is a problem that naturally arises in cognitive radio systems, and our results can be used in these systems.