Research On Energy Efficient For Multi-user MIMO System

With the rapid development of Internet, multimedia data services increased dramatically. In order to increase the transmission rate and guarantee the transmission quality, corresponding energy consumption is also growing at a staggering rate. Environmental problems caused by energy consumption should not be underestimated. Therefore, this paper proposes green wireless communication to protect environment and save resource. The green communication means that minimizing energy consumption under the constraint of the transmission quality of the communication system. Energy efficiency is regarded as one of the most important indicators to evaluate the effectiveness of a communication system and defined as the ratio of achievable capacity to power. Multiple-input multiple-output(MU-MIMO) technology can effectively utilize space resources to obtain diversity multiplexing gain, which has become a key technology of the future of mobile communications. This article focuses on the energy efficiency optimization framework for uplink MU-MIMO. The following issues are mainly studied.Considering a flat Rayleigh fading propagation environment, firstly energy efficiency optimization model is established to maximize system energy efficiency. In the absence of power constraint scenario, two different adaptive power allocation algorithms are presented. As verified by simulation analysis, the two algorithms can achieve the similar performance. At the same time, the other system parameters which affect the communication system performance is analyzed, such as number of users, number of transmit antennas and receive antennas. With the users’ power constrain, the influence of power constraints to system performance is studied.Rayleigh statistical channel model which includes large-scale and small-scale fading is more practical for uplink virtual multiple-input multiple-output(MIMO) systems energy efficiency(EE). Firstly the system ergodic capacity which is based on zero-forcing detection at the base station can be achieved by algebra of expectations. The energy efficiency expression is derived through mathematical theory. In the following, the approximate expression with high signal to noise ratio is discussed and the energy efficiency optimization problem is proposed. At last, the paper researches the optimization of energy efficiency of the system with power constraint. Simulation results show that it exists an optimal number of users or the number of receive antennas to maximize energy efficiency.