The design of distributed mechanisms for interference management is one ofthe key challenges in emerging wireless small cell networks whose backhaul iscapacity limited and heterogeneous (wired, wireless and a mix thereof). In thispaper, a novel, backhaul-aware approach to interference management in wirelesssmall cell networks is proposed. The proposed approach enables macrocell userequipments (MUEs) to optimize their uplink performance, by exploiting thepresence of neighboring small cell base stations. The problem is formulated asa noncooperative game among the MUEs that seek to optimize their delay-ratetradeoff, given the conditions of both the radio access network and the --possibly heterogeneous -- backhaul. To solve this game, a novel, distributedlearning algorithm is proposed using which the MUEs autonomously choose theiroptimal uplink transmission strategies, given a limited amount of availableinformation. The convergence of the proposed algorithm is shown and itsproperties are studied. Simulation results show that, under various types ofbackhauls, the proposed approach yields significant performance gains, in termsof both average throughput and delay for the MUEs, when compared to existingbenchmark algorithms.
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