Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 12, Issue 11, Pages 5813-5825Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2013.092413.130221
Keywords
Heterogeneous networks; capacity-limited backhaul; wired and wireless backhaul; reinforcement learning; game theory
Funding
- SHARING project under the Finland [128010]
- U.S. National Science Foundation (NSF) [CNS-1253731]
- Elektrobit
- Siemens Networks
- LOCON Project, TEKES, Finland
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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.
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