A device-centric clustering approach for large-scale distributed antenna systems using user cooperation

O Zhao, L Shan, WS Liao, MG Kibria, HB Li… - IEICE Transactions …, 2019 - search.ieice.org
O Zhao, L Shan, WS Liao, MG Kibria, HB Li, K Ishizu, F Kojima
IEICE Transactions on Communications, 2019search.ieice.org
Large-scale distributed antenna systems (LS-DASs) are gaining increasing interest and
emerging as highly promising candidates for future wireless communications. To improve
the user's quality of service (QoS) in these systems, this study proposes a user cooperation
aided clustering approach based on device-centric architectures; it enables multi-user
multiple-input multiple-output transmissions with non-reciprocal setups. We actively use
device-to-device communication techniques to achieve the sharing of user information and …
Large-scale distributed antenna systems (LS-DASs) are gaining increasing interest and emerging as highly promising candidates for future wireless communications. To improve the user's quality of service (QoS) in these systems, this study proposes a user cooperation aided clustering approach based on device-centric architectures; it enables multi-user multiple-input multiple-output transmissions with non-reciprocal setups. We actively use device-to-device communication techniques to achieve the sharing of user information and try to form clusters on user side instead of the traditional way that performs clustering on base station (BS) side in data offloading. We further adopt a device-centric architecture to break the limits of the classical BS-centric cellular structure. Moreover, we derive an approximate expression to calculate the user rate for LS-DASs with employment of zero-forcing precoding and consideration of inter-cluster interference. Numerical results indicate that the approximate expression predicts the user rate with a lower computational cost than is indicated by computer simulation, and the proposed approach provides better user experience for, in particular, the users who have unacceptable QoS.
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