  Consider the problem of a multiple access channel in a time dependent
environment 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 analyse the
system's properties, such as delay, QoS and capacity scaling laws.
Specifically, we start with analyzing the performance while \emph{assuming the
users are not necessarily fully backlogged}, focusing on the queueing problem
and, especially, on the \emph{strong dependence between the queues}. We first
extend a known queueing 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 describes the system behaviour 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 analysed, along with the channel capacity scaling laws.
