TY - GEN
T1 - Dynamic uplink-downlink optimization in TDD-based small cell networks
AU - Elbamby, Mohammed S.
AU - Bennis, Mehdi
AU - Saad, Walid
AU - Latva-Aho, Matti
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/21
Y1 - 2014/10/21
N2 - Dynamic Time-division duplex (TDD) can provide efficient and flexible splitting of the common wireless cellular resources between uplink (UL) and downlink (DL) users. In this paper, the UL/DL optimization problem is formulated as a noncooperative game among the small cell base stations (SCBSs) in which each base station aims at minimizing its total UL and DL flow delays. To solve this game, a self-organizing UL/DL resource configuration scheme for TDD-based small cell networks is proposed. Using the proposed scheme, an SCBS is able to estimate and learn the UL and DL loads autonomously while optimizing its UL/DL configuration accordingly. Simulations results show that the proposed algorithm achieves significant gains in terms of packet throughput in case of asymmetric UL and DL traffic loads. This gain increases as the traffic asymmetry increases, reaching up to 97% and 200% gains relative to random and fixed duplexing schemes respectively. Our results also show that the proposed algorithm is well-adapted to dynamic traffic conditions and different network sizes, and operates efficiently in case of severe cross-link interference in which neighboring cells transmit in opposite directions.
AB - Dynamic Time-division duplex (TDD) can provide efficient and flexible splitting of the common wireless cellular resources between uplink (UL) and downlink (DL) users. In this paper, the UL/DL optimization problem is formulated as a noncooperative game among the small cell base stations (SCBSs) in which each base station aims at minimizing its total UL and DL flow delays. To solve this game, a self-organizing UL/DL resource configuration scheme for TDD-based small cell networks is proposed. Using the proposed scheme, an SCBS is able to estimate and learn the UL and DL loads autonomously while optimizing its UL/DL configuration accordingly. Simulations results show that the proposed algorithm achieves significant gains in terms of packet throughput in case of asymmetric UL and DL traffic loads. This gain increases as the traffic asymmetry increases, reaching up to 97% and 200% gains relative to random and fixed duplexing schemes respectively. Our results also show that the proposed algorithm is well-adapted to dynamic traffic conditions and different network sizes, and operates efficiently in case of severe cross-link interference in which neighboring cells transmit in opposite directions.
KW - Dynamic-TDD
KW - reinforcement learning
KW - self-organizing networks
KW - small cells
UR - http://www.scopus.com/inward/record.url?scp=84911961580&partnerID=8YFLogxK
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U2 - 10.1109/ISWCS.2014.6933488
DO - 10.1109/ISWCS.2014.6933488
M3 - Conference contribution
AN - SCOPUS:84911961580
T3 - 2014 11th International Symposium on Wireless Communications Systems, ISWCS 2014 - Proceedings
SP - 939
EP - 944
BT - 2014 11th International Symposium on Wireless Communications Systems, ISWCS 2014 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 11th International Symposium on Wireless Communications Systems, ISWCS 2014
Y2 - 26 August 2014 through 29 August 2014
ER -