Research On Energy Efficient Transmission Technology For Massive MIMO Wireless Communication Systems

Massive Multiple Input Multiple Output (MIMO) is one of the key technologies for next generation mo-bile communication systems, and has potential advantages in significantly improving the system spectrum efficiency and energy efficiency. However, its application still faces many challenges, such as pilot pollu-tion, channel measurement and modeling, channel estimation, beamforming design and low-cost hardware implementation. To meet the demands of green and broadband mobile communication systems, this paper researches on energy-efficient transmission technology for massive MIMO systems. The main work and contributions are as follows:Firstly, this paper studied transmission technology in order to improve energy efficiency of multi-cell multi-user MIMO systems. System model was given and energy-efficient optimization problem was defined, which was hard to solve directly due to the objective function in the fractional form and the mutual coupling between variables. By introducing auxiliary variables, using fractional programming and successive convex approximation for low complexity method, the primal problem was equivalently transformed into a convex problem. Then, we proposed a joint beamforming and power allocation alternating iterative algorithm, and theoretically proved its convergence. The simulation results show that the proposed algorithm can converge within several iterations, and obviously outperform traditional algorithms in terms of energy efficiency.Secondly, this paper studied energy-efficient transmission technology for single-cell massive MIMO systems. The optimization problem was designed to maximize energy efficiency subject to quality of service (QoS) demands and power constraints. By introducing auxiliary variables, the original downlink optimiza-tion problem was converted to a virtual uplink optimization problem based on the duality theorem. Then, we considered two cases:finite systems with instantaneous channel state information (CSI) and large-scale systems with statistical CSI, and proposed the corresponding energy-efficient beamforming algorithms with the convergence and computational complexity analysis, respectively. Numerical simulations show that t-wo proposed algorithms have a faster convergence speed, and the system energy efficiency performance of the algorithm based on statistical CSI is similar to that of the algorithm based on instantaneous CSI with the increasing of transmit antennas. Thus, it can effectively balance system performance and computational complexity.Thirdly, this paper studied energy-efficient transmission technology for multi-cell massive MIMO sys-tems, which focused on solving a joint beamforming and power control optimization problem. After trans-forming the considered problem into a min-max problem through introducing auxiliary variables, we proceed-ed with the dual transformation and designed an energy-efficient beamforming algorithm based on instanta-neous CSI for finite systems. According to random matrix theory, we also proposed a novel transmission scheme of power allocation based on statistical CSI and distributed beamforming based on instantaneous CSI for massive MIMO systems, which can effectively reduce signaling overhead and power update frequency, simultaneously reduce implementation complexity and maintain energy efficiency, compared with traditional transmission schemes. The simulation results verify its convergence and effectiveness.Lastly, this paper studied transmission technology for multi-cell multi-user MIMO systems with the aim of achieving a trade-off between spectrum efficiency and energy efficiency. To solve the defined resource-efficient optimization problem in the weighted sum-of-ratios form, we utilized the fractional programming and convex approximation method to gradually derive a solvable subtractive form and a standard geometric programming problem. Based on this idea of joint beamforming and power allocation alternating iterative optimization, we proposed a resource-efficient beamforming algorithm based on perfect CSI and a resource-efficient robust beamforming algorithm based on imperfect CSI, then discussed the convergence and compu-tational complexity in theory, respectively. Numerical simulations verify its effectiveness, and show that it can weigh spectrum efficiency and energy efficiency through reasonably choosing a weighting factor.