No abstract

Abstract-The growing energy demands, the increasing depletion of traditional energy resources, together coupled with the recent explosion of mobile internet traffic call for green solutions to address the challenges in energy efficient wireless access networks. In this paper, we consider possible power saving through reducing the number of active BSs and adjusting the transmit power of those that remain active while maintaining a satisfying service for all users in the network. Thus, we introduce an optimization problem that jointly minimizes the power consumption of the network and the sum of the transmission delays of the users in the network. Our formulation allows investigating the tradeoff between power and delay by tuning the weighting factors associated to each one. Moreover, to reduce the computational complexity of the optimal solution of our nonlinear optimization problem, we convert it into a Mixed Integer Linear Programming (MILP). We provide extensive simulations for various decision preferences such as power minimization, delay minimization and joint minimization of power and delay. Presented results show that we obtain power savings up to 20% compared to legacy network models.

International audienceIn this paper, we formulate an optimization problem that jointly minimizes the network power consumption and transmission delay in broadband wireless networks. Power saving is achieved by adjusting the operation mode of the network Base Stations (BSs) from high transmit power levels to low transmit levels or switched-off. Minimizing the transmission delay is achieved by selecting the best user association with the BSs. We study the case of a realistic Long Term Evolution (LTE) Network where the challenge is the high computational complexity necessary to obtain the optimal solution. Therefore, we propose a simulated annealing based heuristic algorithm for the power-delay minimization problem. The proposed heuristic aims to compute the transmit power level of the network BSs and associate users with these BSs in a way that jointly minimizes the total network power and the total network delay. The simulation results show that the proposed algorithm has a low computational complexity which makes it advantageous compared with the optimal scheme. Moreover, the heuristic algorithm performs close to optimally and outperforms the existing approaches in realistic 4G deployments