Performance analysis of opportunistic distributed scheduling in multi-user systems

Consider the problem of a multiple access channel with a large number of users. In such a system, mostly due to practical constraints (e.g., decoding complexity), not all users can be scheduled together, and usually only one user may transmit at any given time. Assuming a distributed, opportunistic scheduling algorithm, we analyze the system's properties, such as delay, QoS, and capacity scaling laws. Specifically, we start with analyzing the performance while assuming the users are not necessarily fully backlogged, focusing on the queuing problem and, especially, on the strong dependence between the queues. We first extend a known queuing model by Ephremides and Zhu, to give new results on the convergence of the probability of collision to its average value (as the number of users grows), and hence for the ensuing system performance metrics, such as throughput and delay. This model, however, is limited in the number of users one can analyze. We thus suggest a new model, which is much simpler yet can accurately describe the system behavior when the number of users is large. We then proceed to the analysis of this system under the assumption of time dependent channels. Specifically, we assume each user experiences a different channel state sequence, expressing different channel fluctuations (specifically, the Gilbert-Elliott model). The system performance under this setting is analyzed, along with the channel capacity scaling laws.