Extensive deployment of LTE cellular networks can enhance network throughput via the shared spectrum utilization; however, the energy efficiency is largely ignored during the current spectral efficiency maximization, which is especially important in large-scale LTE networks. By now, we know that green communications and energy efficiency are important for the future sustainable 5G networks. Power control can improve both spectral efficiency and energy efficiency. In this paper, a distributed power control method is investigated for LTE uplink, which is based on the formulation and analysis of a defined cooperative game theoretic power control framework. A novel utility function is designed with the energy efficiency into consideration. Furthermore, the location-aware weighted bargaining game theory is formulated with a denoted balancing factor. Finally, simulation results show that the presented algorithm is with a fast convergence rate. Meanwhile, it can ensure the SINR of all users with reducing power consumption, therefore, improve energy efficiency.

Nowadays, as a low-carbon and sustainable transport mode bike-sharing systems are increasingly popular all over the world, as they can reduce road congestion and decrease greenhouse gas emissions. Aiming at the problem of the mismatch of bike supply and user demand, the operators have to transfer bikes from surplus stations to deficiency stations to redistribute them among stations by vehicles. In this paper, we consider a mixed fleet of electric vehicles and internal combustion vehicles as well as the traffic restrictions to the traditional vehicles in some metropolises. The mixed integer programming model is firstly established with the objective of minimizing the total rebalancing cost of the mixed fleet. Then, a simulated annealing algorithm enhanced with variable neighborhood structures is designed and applied to a set of randomly generated test instances. The computational results and sensitivity analysis indicate that the proposed algorithm can effectively reduce the total cost of rebalancing.