Joint Optimization Of Precoding And Power Allocation For Multicell Downlink Systems

With the rapid development of the Internet technology and wireless communication network technology,the demand of high quality of wireless communication service is increasing.The performance of communication system needs to be promoted while the loss of system energy is reduced according to the Green Communication.The energy efficiency,which is the main per-formance index of communication system,considers both the transmission performance and the energy loss and becomes the key research contents of Green Communication.Now the research on Green Communication is mostly based on the transmission rate of multiple input multiple out-put(MIMO),total mean square error(MSE)or signal to interference plus noise ratio(SINR).The channel state information(CSI)at the transmitter is assumed to be perfect.However,CSI is usually imperfect in the practical system.Most of the papers focus on the dirty paper coding(DPC)with high complexity or the zero-forcing precoding with bad performance under low signal to noise ratio.Base on all above content,considering the CSI is imperfect at the transmitter,we propose a joint optimization algorithm of the beamforming and power allocation in order to maximize the energy efficiency.We build channel estimation error model to evaluate the impact of imperfect CSI for the design of beamforming matrices and optimize the power allocation ratio by setting the power constraints for the optimization problem at the meantime.The optimization problem is solved by the convex optimization theory knowledge.This thesis investigates a joint optimization design method for precoding and power alloca-tion to improve the energy efficiency(EE)of a multicell MIMO downlink system considering the effect of channel estimation errors.The CSI imperfections can be well modeled with the training sequence power.Assuming that both base stations and users are equipped with multiple antennas,the initial expression of the optimization problem is given.Due to the nonconvex and nonlinear fractional programming,we propose two algorithms based on the sample average ap-proximation(SAA)method and the lower bound of user rate method respectively.The algorithm based on SAA transforms the fractional problem to a linear form by using the Dinkelbach method.The sum-rate maximization problem is,then,replaced by the sum-MSE minimization problem by applying weighted mean square error minimization method.Finally,the expectation taken over the distribution of the channels can be approximated using SAA and the problem can be solved by computing a second order cone programming.Considering the high complexity of this algorithm,the other algorithm based on the lower bound of user rate method is presented to solve the same optimization problem.The robustness and effectiveness of the proposed method are validated by the Matlab simulation results.For the downlink multiuser MIMO relay system,assuming the imperfect CSI at the transmit-ter,we propose a joint optimization design algorithm for transmitter precoding,relay node pre-coding and power allocation by maximizing the system energy efficiency under power constraints.With the system used in this thesis,the relay nodes work in half duplex communication mode and we ignore the direct links between transmitters and receivers,which means the user data is received only from the relay node.This assumption ensures that the total transmit power of system is assigned to the training sequence for channel estimation,the transmitter for transmit data and the relay node for forward data respectively.The simulation results verify the effectiveness of the joint alternating iteration optimization algorithm.