  The design of distributed mechanisms for interference management is one of
the key challenges in emerging wireless small cell networks whose backhaul is
capacity limited and heterogeneous (wired, wireless and a mix thereof). In this
paper, a novel, backhaul-aware approach to interference management in wireless
small cell networks is proposed. The proposed approach enables macrocell user
equipments (MUEs) to optimize their uplink performance, by exploiting the
presence of neighboring small cell base stations. The problem is formulated as
a noncooperative game among the MUEs that seek to optimize their delay-rate
tradeoff, given the conditions of both the radio access network and the --
possibly heterogeneous -- backhaul. To solve this game, a novel, distributed
learning algorithm is proposed using which the MUEs autonomously choose their
optimal uplink transmission strategies, given a limited amount of available
information. The convergence of the proposed algorithm is shown and its
properties are studied. Simulation results show that, under various types of
backhauls, the proposed approach yields significant performance gains, in terms
of both average throughput and delay for the MUEs, when compared to existing
benchmark algorithms.
