Multiuser Detection In Low-cost Massive MIMO

Massive multiple-input multiple-output(MIMO)system can greatly improve the system's transmission rate by configuring hundreds of thousands of antennas at the base station(BS),Thus,it has been considered as one of the key technologies for the future 5G mobile communication.With the exponential increase in cost and power consumption attributed to high-resolution analog-to-digital converters(ADCs)deployed in the BS,massive MIMO with low-cost ADCs has received increased interest recently.However,the nonlinear distortion caused by the low-resolution features of the ADCs makes multiuser detection difficult.To effectively handle the nonlinear distortion caused by the low cost ADC devices,this study presents a multiuser detection algorithm based on the generalized approximate message passing(GAMP).Simulation results demonstrate the effectiveness of the proposed algorithm.Compared with the traditional linear detection algorithms,the GAMP algorithm has a lower complexity,better detection performance and can avoid the operation of matrix inversion,which is beneficial to the realization of hardware circuit.In a practical system,the large number of antennas deployed at the BS side will produce a strong correlation,at which point the channel matrix is ill-conditioned.To solve the problem that the GAMP algorithm may not converge in ill-conditioned channels,we construct the vector generalized approximate message passing(V-GAMP)algorithm.Simulations show that the V-GAMP algorithm can effectively overcome the adverse effects of the channel correlation and achieve a better detection performance with a lower complexity.In addition,we deduced the theoretical performance of the V-GAMP algorithm based on the state evolution(SE)equations,which is beneficial for the system design and optimization.