Green-Markov models - new optimization strategies: a case study for user allocation in co-channel macro/femto networks

• Autor(a) principal: Cardoso,Diego L.
• Data de Publicação: 2012
• Outros Autores: Santana,Adamo L. de, Silva,Marcelino S. da, Francês,Carlos R. L., Costa,João C. W. A., Carvalho,Solon V. de, Vijaykumar,N. L.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Journal of Microwaves. Optoelectronics and Electromagnetic Applications
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742012000200005
• topic: Femtocell; Green Network; Markov Decision Process; Optimized Allocation; Quality of Service

The femtocell concept aims to combine fixed-line broadband access with mobile telephony using the deployment of low-cost, low-power third and fourth generation base stations in the subscribers' homes. While the self-configuration of femtocells is a plus, it can limit the quality of service (QoS) for the users and reduce the efficiency of the network, based on outdated allocation parameters such as signal power level. To this end, this paper presents a proposal for optimized allocation of users on a co-channel macro-femto network, that enable self-configuration and public access, aiming to maximize the quality of service of applications and using more efficiently the available energy, seeking the concept of Green networking. Thus, when the user needs to connect to make a voice or a data call, the mobile phone has to decide which network to connect, using the information of number of connections, the QoS parameters (packet loss and throughput) and the signal power level of each network. For this purpose, the system is modeled as a Markov Decision Process, which is formulated to obtain an optimal policy that can be applied on the mobile phone. The policy created is flexible, allowing different analyzes, and adaptive to the specific characteristics defined by the telephone company. The results show that compared to traditional QoS approaches, the policy proposed here can improve energy efficiency by up to 10%.