  We consider location-dependent opportunistic bandwidth sharing between static
and mobile downlink users in a cellular network. Each cell has some fixed
number of static users. Mobile users enter the cell, move inside the cell for
some time and then leave the cell. In order to provide higher data rate to
mobile users, we propose to provide higher bandwidth to the mobile users at
favourable times and locations, and provide higher bandwidth to the static
users in other times. We formulate the problem as a long run average reward
Markov decision process (MDP) where the per-step reward is a linear combination
of instantaneous data volumes received by static and mobile users, and find the
optimal policy. The transition structure of this MDP is not known in general.
To alleviate this issue, we propose a learning algorithm based on single
timescale stochastic approximation. Also, noting that the unconstrained MDP can
be used to solve a constrained problem, we provide a learning algorithm based
on multi-timescale stochastic approximation. The results are extended to
address the issue of fair bandwidth sharing between the two classes of users.
Numerical results demonstrate performance improvement by our scheme, and also
the trade-off between performance gain and fairness.
