On Link Adaptive Transmission Method For Large-scale MIMO Systems

The rapid development of mobile internet and the explosion of handhold terminals,multimedia services and the "Internet of Thing" calls for network that is able to provide capacity and data transmission rate tens and hundreds times as the present.Technical standards of 5G(5th generation mobile communication systems)which focus on future mobile communication are expected to be determined around 2020.As one of the key candidate technologies of 5G,large scale multiple-input multiple-output(Large-Scale MIMO)technique has aroused widespread concern in the academic community for its potential to greatly improve spectral and power efficiency by equipping the base station(BS)with massive antennas.In wireless communication systems,resource scheduling and transmission parameter adjustment is an important aspect to reduce interference and ensure transmission reliability as well as improving spectrum efficiency.This thesis studies link adaptive transmission method for Large-Scale MIMO,the main contents include the following aspects:Firstly,we study beam division multiple access(BDMA)transmission for Large-Scale MIMO spatial correlated rayleigh fading channel,specially focusing on user scheduling and adaptive transmission.Based on the description of beam domain channel model,BDMA is optimal for sum-rate upper bound maximization.Further more,details about system implementation are provided for BDMA transmission.For user scheduling in BDMA downlink transmission,the online sum-rate estimation is the bottleneck of calculation and we propose a low complexity sum-rate estimation algorithm based on deterministic equivalent.For adaptive transmission,some classic adaptive transmission techniques are provided.Simulation results show that the proposed sum-rate deterministic equivalent algorithm can greatly reduce computation complexity,and this type of sum-rate approximation is robust and relatively accurate.Then,we propose a link adaptation method based on iterative receiver for Large-Scale MIMO BDMA downlink transmission.When using iterative receiver,traditional link adaptation method based on post sig-nal to interference plus noise ratio(P-SINR)of linear minimum mean square error detection(LMMSE)can not reach the best throughput since the performance gain is not considered brought by the iterative proce-dure.We propose a semi-analytical performance prediction method based on extrinsic information transfer(EXIT)for iterative receiver.Further,we propose a calibration method for over optimistic prediction caused by the a posteriori probability instead of extrinsic information fed back by the decoder.Then we come up with a performance predictor.On this basis an efficient link adaptation method is proposed,in which the performance predictor for iterative receiver is used to estimate the final code word error rate(CWER)and based on CWER we get the best feedback parameters.Numerical results verify the validation of the proposed performance predictor for iterative receiver and the corresponding link adaptation method can achieve high spectrum efficiency.Finally,we propose a low complexity link adaptation method based on MMSE detection for Large-Scale MIMO uplink.When the number of BS antennas and UE becomes large,traditional link adaptation method faces several challenges including the complexity of matrix inversion of feedback parameter calculation and the dependency of instantaneous CSI makes it difficult to realize in practice since it is a bottleneck in Large-Scale MIMO.We study the estimation of ergodic rate of LMMSE jointly detection,for which a low complexity deterministic equivalent of the ergodic rate lower bound is derived.The proposed deterministic equivalent depends only on channel coupling matrix(CCM).The estimation of MMSE ergodic rate is used to perform MMSE rate based link adaptation for which a rate threshold calibration method is proposed.Simulation results show that the deterministic equivalent estimation is a good approximation of the ergodic rate,and the proposed link adaptation method can greatly reduce computation complexity while maintain relatively high spectrum efficiency.