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Automated uplink power control optimization in LTE-Advanced relay networks

DOI: 10.1186/1687-1499-2013-8

Keywords: LTE-Advanced, Decode-and-forward relay, Automated optimization, Uplink power control, Simulated annealing, Taguchi’s method

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Abstract:

Relaying is standardized in 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE)-Advanced Release 10 as a promising cost-efficient enhancement to existing radio access networks. Relay deployments promise to alleviate the limitations of conventional macrocell-only networks such as poor indoor penetration and coverage holes. However, to fully exploit the benefits of relaying, power control (PC) in the uplink should be readdressed. In this context, PC optimization should jointly be performed on all links, i.e., on the donor-evolved Node B (DeNB)-relay node (RN), the DeNB-user equipment (UE) link, and the RN–UE link. This ensures proper management of interference in the network besides attaining a receiver dynamic range which ensures the orthogonality of the single-carrier frequency-division multiple access (SC-FDMA) system. In this article, we propose an automated PC optimization scheme which jointly tunes PC parameters in relay deployments. The automated PC optimization can be based on either Taguchi’s method or a meta-heuristic optimization technique such as simulated annealing. To attain a more homogeneous user experience, the automated PC optimization scheme applies novel performance metrics which can be adapted according to the operator’s requirements. Moreover, the performance of the proposed scheme is compared with a reference study that assumes a scenario-specific manual learn-by-experience optimization. The evaluation of the optimization methods within the LTE-Advanced uplink framework is carried out in 3GPP-defined urban and suburban propagation scenarios by applying the standardized LTE Release 8 PC scheme. Comprehensive results show that the proposed automated PC optimization can provide similar performance compared to the reference manual optimization without requiring direct human intervention during the optimization process. Furthermore, various trade-offs can easily be achieved; thanks to the new performance metrics.

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