Interference Control Based Energy Efficiency Improvement For Hybrid Cellular Networks

It is shown by previous studies that network equipments and mobile devices in wireless networks consume huge energy, which results in not only harmful carbon emission but high network operators' expenditure. Even worse, due to the popularity of mobile applications, resulted from the popular mobile devices, the demand of network data rate for mobile users is increased, which also leads to much higher energy consumption. Thus, it is necessary for us to investigate the problem of increasing network data rate and reducing energy consumption. In particular, previous studies show that adopting a hybrid architecture for structuring cellular network(ie. Femtocell and Macrocell) can improve the network data rate of a cellular network due to the collaboration between Femtocell and Macrocell. However, the hybrid network architecture incurs the inter-layer interference besides the intra-layer interference in traditional network architecture, which affects the network data rate and energy consumption, and consequently the energy efficiency. Thus, we need to consider improving the energy efficiency by controlling the interference.A scheme to improve energy efficiency of a hybrid cellular network based on the interference control is proposed. To enhance the energy efficiency of hybrid cellular network, this paper defines an interference model that can be suitable to the hybrid cellular network. Then, this paper considers the joint channel allocation and transmission power allocation to control the interference, and thus improving the energy efficiency.Specifically, a heuristic algorithm is used to allocate the channels to mobile users and a particle swarm algorithm is used to allocate the transmission power. In particular, each possible channel allocation strategy can be seen as a state, and all possible channel allocation strategies construct the state space. For the adjustment of transmission power,the power is simulated as particles that are able to find the optimal locations by exploiting its automatical exploration and group directions.Simulation results are provided to validate the effectiveness of the proposed mechanism. Moreover, the stability and accuracy of power adjustment algorithm based on particle swarm algorithm are verified and its convergence is shown. In addition,simulation results also validate the efficiency of the heuristic algorithm.