Research On Advanced Receiver Technology Of Uplink Multiuser Massive MIMO Systems With Low-resolution ADCs

Multiple-Input Multiple-Output(MIMO)technology transmits multiple data streams in parallel through multiple antennas at the transceiver end.In the case of fixed bandwidth,it can double the system capacity and throughput.Extensive research by researchers and widely used in the fourth generation of mobile communication technology standards,such as 3GPP LTE and IEEE 802.16 e,the current LTE-Advanced system with 8 antennas is basically unable to meet the increasing data rate demand.As the core technology of next generation wireless mobile communication system,Massive MIMO further increases the data rate by deploying large-scale antennas on the transceiver side,and has achieved certain scientific research results.However,as the number of antennas at the transceiver end increases,the hardware implementation in the Massive MIMO system becomes very complicated.In order to be more economical and to meet the reliability of the system,the application of low-bit ADCs becomes the key technology of the Massive MIMO system.one.Therefore,this paper mainly studies the detection algorithm of Massive MIMO with 1-bit ADCs.The current research status of Massive MIMO and low-bit ADCs is introduced this paper,and further analyzes the three types of base-station side signal detection algorithms,and introduces the algorithms such as ML,ZF,MMSE and SD in the hard decision detection algorithm.The MAP algorithm is introduced in the soft decision detection algorithm,and a low complexity MMSE-MAP algorithm is introduced.Then the system model of the SISO detector is given,and the Turbo iterative process based on the MAP algorithm is introduced.However,none of the above algorithms are based on the low-bit ADCs unit for further analysis and discussion.Secondly,after combining 1-bit ADCs,a low complexity detection algorithm based on grouping strategy and vector segmentation strategy is proposed in this paper.This algorithm combines 1-bit ADCs with vector segmentation strategy to reduce the received signal.The size of the vector space,and the convex optimization tool is used to obtain a set of transmission signal candidates corresponding to each sub-vector by the relaxation constraint.Through such a strategy,the candidate signal can be quickly found every time the received signal vector is observed,what's more such receiver complexity is further reduced as the length of the data symbols in a channel impulse response increases.The simulation results show that the grouping-based detection algorithm performs satisfactorily and is close to the ML detector.Then a soft decision receiver based on gradient descent algorithm is proposed for the problem that the receiver can't output soft information.The approximation formula is used to convert the log likelihood ratio into two optimization problems solved through the convex optimization tool.It can be seen from the simulation that the receiver can output the correct soft information,and the number of iterations of the gradient descent algorithm decreases with the number of antennas at the base station,which will further reduce the number of iterations in the Massive MIMO system.Finally,a soft input soft output receiver is proposed in this paper.The detector of the receiver is implemented based on the GAMP algorithm.The external information is exchanged between the detector and the decoder,and the external information output by the decoder is the initial conditions of the GAMP algorithm,therefor the Turbo iteration is completed.It is also shown through simulation that the soft input soft output receiver through Turbo iteration has better performance than the soft output receiver.