Energy efficiency is one of the main requirements for future 5G wireless networks to allow a sustainable network development. Toward this goal, this paper proposes a novel iterative algorithm to maximize the energy efficiency of multicast services in ultra dense networks. Macrocell and small cells cooperate using a multicast beamforming strategy to efficiently radiate the power in the considered area. Moreover, small cells are activated opportunistically, and their power levels are adjusted suitably. Due to the high complexity of the optimization problem, the proposed algorithm works calculating iteratively beamforming weights and power adjustments, thus reducing the set of possible solutions. Then, an exhaustive search is performed among the elements of this restricted set. This solution has reduced computational complexity, and the achieved energy efficiency is higher than benchmark alternatives. Performance in terms of energy efficiency and complexity are provided together with a solution feasibility evaluation
Energy efficient cooperative multicast beamforming in ultra dense networks / Marabissi, D.; Giulio, Bartoli; Fantacci, R.; Micciullo, L.. - In: IET COMMUNICATIONS. - ISSN 1751-8628. - STAMPA. - 12:(2018), pp. 573-578. [10.1049/iet-com.2017.0618]
Energy efficient cooperative multicast beamforming in ultra dense networks
D. Marabissi
;Giulio Bartoli;R. Fantacci;L. Micciullo
2018
Abstract
Energy efficiency is one of the main requirements for future 5G wireless networks to allow a sustainable network development. Toward this goal, this paper proposes a novel iterative algorithm to maximize the energy efficiency of multicast services in ultra dense networks. Macrocell and small cells cooperate using a multicast beamforming strategy to efficiently radiate the power in the considered area. Moreover, small cells are activated opportunistically, and their power levels are adjusted suitably. Due to the high complexity of the optimization problem, the proposed algorithm works calculating iteratively beamforming weights and power adjustments, thus reducing the set of possible solutions. Then, an exhaustive search is performed among the elements of this restricted set. This solution has reduced computational complexity, and the achieved energy efficiency is higher than benchmark alternatives. Performance in terms of energy efficiency and complexity are provided together with a solution feasibility evaluationI documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.