The paper studies the max-min fair multicast multigroup beamforming problem in a multi-cell environment, with perfect (instantaneous or statistical) Channel State Information (CSI). We propose a new general distributed algorithmic framework based on INner Convex Approximations (INCA): the nonsmooth NP-hard problem is replaced by a sequence of smooth strongly convex subproblems, which can be solved in a distributed fashion across the cells, with limited communication overhead. Differently from renowned semidefinite-relaxation-based schemes, the INCA algorithm is proved to always converge to a d-stationary solution of the aforementioned class of problems. Numerical results show that it compares favorably with state-of-the-art algorithms.
D3M: Distributed multi-cell multigroup multicasting / Song, P.; Scutari, G.; Facchinei, Francisco; Lampariello, L.. - STAMPA. - (2016), pp. 3741-3745. (Intervento presentato al convegno 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 tenutosi a Shanghai; China) [10.1109/ICASSP.2016.7472376].
D3M: Distributed multi-cell multigroup multicasting
FACCHINEI, Francisco
;
2016
Abstract
The paper studies the max-min fair multicast multigroup beamforming problem in a multi-cell environment, with perfect (instantaneous or statistical) Channel State Information (CSI). We propose a new general distributed algorithmic framework based on INner Convex Approximations (INCA): the nonsmooth NP-hard problem is replaced by a sequence of smooth strongly convex subproblems, which can be solved in a distributed fashion across the cells, with limited communication overhead. Differently from renowned semidefinite-relaxation-based schemes, the INCA algorithm is proved to always converge to a d-stationary solution of the aforementioned class of problems. Numerical results show that it compares favorably with state-of-the-art algorithms.File | Dimensione | Formato | |
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