The presence of multiple users in a network provides us with a valuable resource known as multiuser diversity. With information on the instantaneous states of the channels, multiuser diversity can be tapped by opportunistic multiuser scheduling. It is important that the channel state information is acquired in a cost-effective way so that the losses involved in this operation do not offset the gains promised by opportunistic scheduling. For various network environments of practical interest, this dissertation models the radio frequency links with memory, and studies the modalities to exploit the channel memory to simultaneously estimate channel state information, while performing opportunistic multiuser scheduling. The data transmission at any point of time is shown to be associated with two potentially contradicting objectives: opportunistic scheduling for immediate gains and channel exploration for future gains. Thus the joint scheduling problem is a dynamic program, specifically a partially observable Markov decision process that is traditionally known to be intractable or computationally expensive to implement. For various networks, we study these processes in an optimality framework and whenever possible, derive the optimal scheduling policy in closed form. In other cases, strongly founded on the optimality framework, we derive computationally inexpensive scheduling policies with near-optimal numerical performances.
By appropriately exploiting the memory in the fading channels, significant system level gains can be achieved using opportunistic scheduling, even with minimal feedback, and a considerable portion of these gains can be realized even with sub-optimal policies that are computationally inexpensive to implement -- This is the central message of this dissertation.