Base Station Access And Sleeping Control For Energy Harvesting Heterogeneous Cellular Networks

Deploying heterogeneous cellular network where multi-type of small base stations (SBSs) and relay stations (RSs) existing along with macro base stations (MBSs) is one of the key technologies to the explosive growth of mobile date traffic. However, the dense deployment of SBSs and RSs has also lead to the dramatical growth of energy consumption of cellular networks. In the future, energy harvesting (EH) is recognized as a promising technology to reduce the energy consumption of cellular networks by pow-ering communication equipments with renewable energy, such as solar energy and wind energy. However, since these renewable energy is unstable, the time and number of har-vested energy are both stochastic. So new network deployment and operation strategies are needed to guarantee the stability of the network. To improve the performance of EH cellular networks, we propose a heterogeneous cellular network model where SBSs or RSs are powered by EH technology and MBSs are powered by grid energy, thus when SBSs or RSs don’t have enough energy to work, MBSs can still guarantee the quality of communication. And then, according to different objectives, we propose the effec-tive base station (BS) control strategies to analyze and optimize the performance of the network with the aid of stochastic geometry.We consider two typical EH heterogeneous cellular network scenes.For the network where RSs are powered by renewable energy and MBSs are pow-ered by grid power, to provide as much rate for users as possible by effectively using the harvested energy of RS, we propose a max-received-power-with-dynamical-bias based user association policy, and maximize user’s average rate by optimizing the bias under the constraints of EH rate of RS. We consider two kinds of relay-assistance scenes. For the scene of no BS-UE link between MBS and users served by RS, we derive the expres-sions of the active probability of RS and user’s average rate using tools from Markov and stochastic geometry. And then, by formulating and solving the optimization problem, we derive the analytical expression of the optimal bias maximizing user’s average rate. For the scene of existing BS-UE link between MBS and users served by RS, we get the optimal bias maximizing user’s average rate by system simulation. At last, simulation results show that, when the EH rate of RS is not large enough, the optimal bias under the two kind of scenes is always the same, and user’s average rate with the optimal bias is greatly larger than the average rate without bias.For the network where SBSs are powered by renewable energy and MBSs are powered by grid power, since the EH rate of SBS and the traffic load of the network are time-variable, we propose minimize the network grid energy consumption by jointly optimizing the MBS active probability (MAP) and the SBS transmit power (STP) under the constraints of user quality of service (QoS). Using tools from stochastic geometry, we first derive the expression of the service outage probability to evaluate user QoS. And then, the impacts of the MAP and the STP on the service outage probability are characterized. At last, the optimization problem minimizing the network grid energy consumption is formulated, and the closed expressions of the optimal MAP and STP are derived by investigating the monotonicity of the service outage probability. Numerical results show that our strategy of jointly optimizing the MAP and the STP is superior to the strategy of merely considering MBS sleeping with fixed STP, and the superiority is more attractive with lower circuit power of SBS.